Tootfinder

An Interpretable Deep Learning Model for General Insurance Pricing
Patrick J. Laub, Tu Pho, Bernard Wong
https://arxiv.org/abs/2509.08467 https://arxiv.org…

An Interpretable Deep Learning Model for General Insurance Pricing
This paper introduces the Actuarial Neural Additive Model, an inherently interpretable deep learning model for general insurance pricing that offers fully transparent and interpretable results while retaining the strong predictive power of neural networks. This model assigns a dedicated neural network (or subnetwork) to each individual covariate and pairwise interaction term to independently learn its impact on the modeled output while implementing various architectural constraints to allow for…

Scaling Laws for Uncertainty in Deep Learning
Mattia Rosso, Simone Rossi, Giulio Franzese, Markus Heinonen, Maurizio Filippone
https://arxiv.org/abs/2506.09648

Scaling Laws for Uncertainty in Deep Learning
Deep learning has recently revealed the existence of scaling laws, demonstrating that model performance follows predictable trends based on dataset and model sizes. Inspired by these findings and fascinating phenomena emerging in the over-parameterized regime, we examine a parallel direction: do similar scaling laws govern predictive uncertainties in deep learning? In identifiable parametric models, such scaling laws can be derived in a straightforward manner by treating model parameters in a B…

Lightweight Deep Unfolding Networks with Enhanced Robustness for Infrared Small Target Detection
Jingjing Liu, Yinchao Han, Xianchao Xiu, Jianhua Zhang, Wanquan Liu
https://arxiv.org/abs/2509.08205

Lightweight Deep Unfolding Networks with Enhanced Robustness for Infrared Small Target Detection
Infrared small target detection (ISTD) is one of the key techniques in image processing. Although deep unfolding networks (DUNs) have demonstrated promising performance in ISTD due to their model interpretability and data adaptability, existing methods still face significant challenges in parameter lightweightness and noise robustness. In this regard, we propose a highly lightweight framework based on robust principal component analysis (RPCA) called L-RPCANet. Technically, a hierarchical bottl…

Efficient Deep Neural Receiver with Post-Training Quantization
SaiKrishna Saketh Yellapragada, Esa Ollila, Mario Costa
https://arxiv.org/abs/2508.06275 https://

Efficient Deep Neural Receiver with Post-Training Quantization
Deep learning has recently garnered significant interest in wireless communications due to its superior performance compared to traditional model-based algorithms. Deep convolutional neural networks (CNNs) have demonstrated notable improvements in block error rate (BLER) under various channel models and mobility scenarios. However, the high computational complexity and resource demands of deep CNNs pose challenges for deployment in resource-constrained edge systems. The 3rd Generation Partnersh…

RoentMod: A Synthetic Chest X-Ray Modification Model to Identify and Correct Image Interpretation Model Shortcuts
Lauren H. Cooke, Matthias Jung, Jan M. Brendel, Nora M. Kerkovits, Borek Foldyna, Michael T. Lu, Vineet K. Raghu
https://arxiv.org/abs/2509.08640

RoentMod: A Synthetic Chest X-Ray Modification Model to Identify and Correct Image Interpretation Model Shortcuts
Chest radiographs (CXRs) are among the most common tests in medicine. Automated image interpretation may reduce radiologists\' workload and expand access to diagnostic expertise. Deep learning multi-task and foundation models have shown strong performance for CXR interpretation but are vulnerable to shortcut learning, where models rely on spurious and off-target correlations rather than clinically relevant features to make decisions. We introduce RoentMod, a counterfactual image editing framewo…

Attention Head Embeddings with Trainable Deep Kernels for Hallucination Detection in LLMs
Rodion Oblovatny, Alexandra Bazarova, Alexey Zaytsev
https://arxiv.org/abs/2506.09886

Attention Head Embeddings with Trainable Deep Kernels for Hallucination Detection in LLMs
We present a novel approach for detecting hallucinations in large language models (LLMs) by analyzing the probabilistic divergence between prompt and response hidden-state distributions. Counterintuitively, we find that hallucinated responses exhibit smaller deviations from their prompts compared to grounded responses, suggesting that hallucinations often arise from superficial rephrasing rather than substantive reasoning. Leveraging this insight, we propose a model-intrinsic detection method t…

Silent Until Sparse: Backdoor Attacks on Semi-Structured Sparsity
Wei Guo, Maura Pintor, Ambra Demontis, Battista Biggio
https://arxiv.org/abs/2509.08747 https://

Silent Until Sparse: Backdoor Attacks on Semi-Structured Sparsity
In the deployment phase, semi-structured sparsity accelerates the execution of deep neural networks on modern GPUs via sparse matrix multiplication. In this paper, targeting the semi-structured sparsity, we introduce a Silent Until Sparse (SUS) backdoor attack, where the released full model remains silent (benign), but becomes a backdoored model after sparsification. The attack operates in two phases: (i) in the backdoor training phase, the backdoor functionality is injected into specific weigh…

Cat states in one- and two-mode $\mathbb{Z}_3$ Rabi models
Anatoliy I. Lotkov, Denis V. Kurlov, Valerii K. Kozin, Jelena Klinovaja, Daniel Loss
https://arxiv.org/abs/2509.08603 …

Cat states in one- and two-mode $\mathbb{Z}_3$ Rabi models
We investigate one- and two-mode variants of the $\mathbb{Z}_3$-symmetric quantum Rabi model, which describe the interaction of a qutrit with one or two bosonic modes and are directly relevant for circuit-QED and spin-qudit platforms. We find a canonical transformation that allows one to obtain the spectrum of the $\mathbb{Z}_3$ Rabi model using perturbation theory in a magnetic field. We show that in a certain parameter regime (deep-strong-coupling and a small magnetic field) the three lowest …

A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
Elahe Delavari, Feeza Khan Khanzada, Jaerock Kwon
https://arxiv.org/abs/2509.08221

A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
Autonomous-driving research has recently embraced deep Reinforcement Learning (RL) as a promising framework for data-driven decision making, yet a clear picture of how these algorithms are currently employed, benchmarked and evaluated is still missing. This survey fills that gap by systematically analysing around 100 peer-reviewed papers that train, test or validate RL policies inside the open-source CARLA simulator. We first categorize the literature by algorithmic family model-free, model-bas…

A Deep Generative Model for the Simulation of Discrete Karst Networks
Dany Lauzon, Julien Straubhaar, Philippe Renard
https://arxiv.org/abs/2506.09832 http…

A Deep Generative Model for the Simulation of Discrete Karst Networks
The simulation of discrete karst networks presents a significant challenge due to the complexity of the physicochemical processes occurring within various geological and hydrogeological contexts over extended periods. This complex interplay leads to a wide variety of karst network patterns, each intricately linked to specific hydrogeological conditions. We explore a novel approach that represents karst networks as graphs and applies graph generative models (deep learning techniques) to capture …

Detecting Galactic Rings in the DESI Legacy Imaging Surveys with Semi-Supervised Deep Learning
Jianzhen Chen, Zhijian Luo, Cheng Cheng, Jun Hou, Shaohua Zhang, Chenggang Shu
https://arxiv.org/abs/2507.07552

Detecting Galactic Rings in the DESI Legacy Imaging Surveys with Semi-Supervised Deep Learning
The ring structures of disk galaxies are vital for understanding galaxy evolution and dynamics. However, due to the scarcity of ringed galaxies and challenges in their identification, traditional methods often struggle to efficiently obtain statistically significant samples. To address this, this study employs a novel semi-supervised deep learning model, GC-SWGAN, aimed at identifying galaxy rings from high-resolution images of the DESI Legacy Imaging Surveys. We selected over 5,000 confirmed r…

User-Intent-Driven Semantic Communication via Adaptive Deep Understanding
Peigen Ye, Jingpu Duan, Hongyang Du, Yulan Guo
https://arxiv.org/abs/2508.05884 https://

User-Intent-Driven Semantic Communication via Adaptive Deep Understanding
Semantic communication focuses on transmitting task-relevant semantic information, aiming for intent-oriented communication. While existing systems improve efficiency by extracting key semantics, they still fail to deeply understand and generalize users' real intentions. To overcome this, we propose a user-intention-driven semantic communication system that interprets diverse abstract intents. First, we integrate a multi-modal large model as semantic knowledge base to generate user-intention pr…

Deep Context-Conditioned Anomaly Detection for Tabular Data
Spencer King, Zhilu Zhang, Ruofan Yu, Baris Coskun, Wei Ding, Qian Cui
https://arxiv.org/abs/2509.09030 https://

Deep Context-Conditioned Anomaly Detection for Tabular Data
Anomaly detection is critical in domains such as cybersecurity and finance, especially when working with large-scale tabular data. Yet, unsupervised anomaly detection -- where no labeled anomalies are available -- remains a significant challenge. Although various deep learning methods have been proposed to model a dataset's joint distribution, real-world tabular data often contain heterogeneous contexts (e.g., different users), making globally rare events normal under certain contexts. Conseque…

Two-Stage Swarm Intelligence Ensemble Deep Transfer Learning (SI-EDTL) for Vehicle Detection Using Unmanned Aerial Vehicles
Zeinab Ghasemi Darehnaei, Mohammad Shokouhifar, Hossein Yazdanjouei, S. M. J. Rastegar Fatemi
https://arxiv.org/abs/2509.08026

Two-Stage Swarm Intelligence Ensemble Deep Transfer Learning (SI-EDTL) for Vehicle Detection Using Unmanned Aerial Vehicles
This paper introduces SI-EDTL, a two-stage swarm intelligence ensemble deep transfer learning model for detecting multiple vehicles in UAV images. It combines three pre-trained Faster R-CNN feature extractor models (InceptionV3, ResNet50, GoogLeNet) with five transfer classifiers (KNN, SVM, MLP, C4.5, Naïve Bayes), resulting in 15 different base learners. These are aggregated via weighted averaging to classify regions as Car, Van, Truck, Bus, or background. Hyperparameters are optimized with t…

Audio-Thinker: Guiding Audio Language Model When and How to Think via Reinforcement Learning
Shu Wu, Chenxing Li, Wenfu Wang, Hao Zhang, Hualei Wang, Meng Yu, Dong Yu
https://arxiv.org/abs/2508.08039

Audio-Thinker: Guiding Audio Language Model When and How to Think via Reinforcement Learning
Recent advancements in large language models, multimodal large language models, and large audio language models (LALMs) have significantly improved their reasoning capabilities through reinforcement learning with rule-based rewards. However, the explicit reasoning process has yet to show significant benefits for audio question answering, and effectively leveraging deep reasoning remains an open challenge, with LALMs still falling short of human-level auditory-language reasoning. To address thes…

Transaction Categorization with Relational Deep Learning in QuickBooks
Kaiwen Dong, Padmaja Jonnalagedda, Xiang Gao, Ayan Acharya, Maria Kissa, Mauricio Flores, Nitesh V. Chawla, Kamalika Das
https://arxiv.org/abs/2506.09234

Transaction Categorization with Relational Deep Learning in QuickBooks
Automatic transaction categorization is crucial for enhancing the customer experience in QuickBooks by providing accurate accounting and bookkeeping. The distinct challenges in this domain stem from the unique formatting of transaction descriptions, the wide variety of transaction categories, and the vast scale of the data involved. Furthermore, organizing transaction data in a relational database creates difficulties in developing a unified model that covers the entire database. In this work, …

Deep Brain Net: An Optimized Deep Learning Model for Brain tumor Detection in MRI Images Using EfficientNetB0 and ResNet50 with Transfer Learning
Daniel Onah, Ravish Desai
https://arxiv.org/abs/2507.07011

Deep Brain Net: An Optimized Deep Learning Model for Brain tumor Detection in MRI Images Using EfficientNetB0 and ResNet50 with Transfer Learning
In recent years, deep learning has shown great promise in the automated detection and classification of brain tumors from MRI images. However, achieving high accuracy and computational efficiency remains a challenge. In this research, we propose Deep Brain Net, a novel deep learning system designed to optimize performance in the detection of brain tumors. The model integrates the strengths of two advanced neural network architectures which are EfficientNetB0 and ResNet50, combined with transfer…

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
Jonas Svedas, Hannah Watson, Nathan Laubeuf, Diksha Moolchandani, Abubakr Nada, Arjun Singh, Dwaipayan Biswas, James Myers, Debjyoti Bhattacharjee
https://arxiv.org/abs/2506.09275

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
Distributed deep neural networks (DNNs) have become a cornerstone for scaling machine learning to meet the demands of increasingly complex applications. However, the rapid growth in model complexity far outpaces CMOS technology scaling, making sustainable and efficient system design a critical challenge. Addressing this requires coordinated co-design across software, hardware, and technology layers. Due to the prohibitive cost and complexity of deploying full-scale training systems, simulators …

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication
Mohammad Ghassemi, Sara Farrag Mobarak, Han Zhang, Ali Afana, Akram Bin Sediq, Melike Erol-Kantarci
https://arxiv.org/abs/2506.09855

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication
Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by dynamically controlling signal propagation in the environment. However, their efficient deployment relies on accurate channel state information (CSI), which leads to high channel estimation overhead due to their passive nature and the large number of reflective elements. In this work, we solve this challenge by proposing a novel framework that leverages a pre-trained open-sou…

An Improved Rapid Performance Analysis Model for Solenoidal Magnetic Radiation Shields
Joseph L. Hesse-Withbroe, Katya S. Arquilla
https://arxiv.org/abs/2509.09051 https://

An Improved Rapid Performance Analysis Model for Solenoidal Magnetic Radiation Shields
Astronauts participating in deep-space exploration missions will be exposed to significantly greater amounts of radiation than is typically encountered on Earth or in low Earth orbit (LEO), which poses significant risks to crew health and mission safety. Active magnetic radiation shields based on the Lorentz deflection of charged particles have the potential to reduce astronaut doses with lower mass costs than passive shielding techniques. Typically, active shielding performance is evaluated us…

TGLF-SINN: Deep Learning Surrogate Model for Accelerating Turbulent Transport Modeling in Fusion
Yadi Cao, Futian Zhang, Wesley Liu, Tom Neiser, Orso Meneghini, Lawson Fuller, Sterling Smith, Raffi Nazikian, Brian Sammuli, Rose Yu
https://arxiv.org/abs/2509.07024

TGLF-SINN: Deep Learning Surrogate Model for Accelerating Turbulent Transport Modeling in Fusion
The Trapped Gyro-Landau Fluid (TGLF) model provides fast, accurate predictions of turbulent transport in tokamaks, but whole device simulations requiring thousands of evaluations remain computationally expensive. Neural network (NN) surrogates offer accelerated inference with fully differentiable approximations that enable gradient-based coupling but typically require large training datasets to capture transport flux variations across plasma conditions, creating significant training burden and …

Conditioning on PDE Parameters to Generalise Deep Learning Emulation of Stochastic and Chaotic Dynamics
Ira J. S. Shokar, Rich R. Kerswell, Peter H. Haynes
https://arxiv.org/abs/2509.09599

Conditioning on PDE Parameters to Generalise Deep Learning Emulation of Stochastic and Chaotic Dynamics
We present a deep learning emulator for stochastic and chaotic spatio-temporal systems, explicitly conditioned on the parameter values of the underlying partial differential equations (PDEs). Our approach involves pre-training the model on a single parameter domain, followed by fine-tuning on a smaller, yet diverse dataset, enabling generalisation across a broad range of parameter values. By incorporating local attention mechanisms, the network is capable of handling varying domain sizes and re…

DLGE: Dual Local-Global Encoding for Generalizable Cross-BCI-Paradigm
Jingyuan Wang, Junhua Li
https://arxiv.org/abs/2509.07991 https://arxiv.org/pdf/2509.…

DLGE: Dual Local-Global Encoding for Generalizable Cross-BCI-Paradigm
Deep learning models have been frequently used to decode a single brain-computer interface (BCI) paradigm based on electroencephalography (EEG). It is challenging to decode multiple BCI paradigms using one model due to diverse barriers, such as different channel configurations and disparate task-related representations. In this study, we propose Dual Local-Global Encoder (DLGE), enabling the classification across different BCI paradigms. To address the heterogeneity in EEG channel configuration…

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector
Amal Chebbi, Babajide Kolade
https://arxiv.org/abs/2509.07177 https://arxiv.org…

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector
Large Language Models have demonstrated impressive capabilities across various domains. However, their general-purpose nature often limits their effectiveness in specialized fields such as energy, where deep technical expertise and precise domain knowledge are essential. In this paper, we introduce EnergyGPT, a domain-specialized language model tailored for the energy sector, developed by fine-tuning LLaMA 3.1-8B model using Supervised Fine-Tuning on a high-quality, curated corpus of energy-rel…

Improving Deep Learning Framework Testing with Model-Level Metamorphic Testing
Yanzhou Mu, Juan Zhai, Chunrong Fang, Xiang Chen, Zhixiang Cao, Peiran Yang, Kexin Zhao, An Guo, Zhenyu Chen
https://arxiv.org/abs/2507.04354

Improving Deep Learning Framework Testing with Model-Level Metamorphic Testing
Deep learning (DL) frameworks are essential to DL-based software systems, and framework bugs may lead to substantial disasters, thus requiring effective testing. Researchers adopt DL models or single interfaces as test inputs and analyze their execution results to detect bugs. However, floating-point errors, inherent randomness, and the complexity of test inputs make it challenging to analyze execution results effectively, leading to existing methods suffering from a lack of suitable test oracl…

LD-ViCE: Latent Diffusion Model for Video Counterfactual Explanations
Payal Varshney, Adriano Lucieri, Christoph Balada, Sheraz Ahmed, Andreas Dengel
https://arxiv.org/abs/2509.08422

LD-ViCE: Latent Diffusion Model for Video Counterfactual Explanations
Video-based AI systems are increasingly adopted in safety-critical domains such as autonomous driving and healthcare. However, interpreting their decisions remains challenging due to the inherent spatiotemporal complexity of video data and the opacity of deep learning models. Existing explanation techniques often suffer from limited temporal coherence, insufficient robustness, and a lack of actionable causal insights. Current counterfactual explanation methods typically do not incorporate guida…

A High-order Backpropagation Algorithm for Neural Stochastic Differential Equation Model
Daili Sheng, Minghui Song, Xiang Peng, Xuanqi Dong
https://arxiv.org/abs/2509.06292 http…

A High-order Backpropagation Algorithm for Neural Stochastic Differential Equation Model
Neural stochastic differential equation model with a Brownian motion term can capture epistemic uncertainty of deep neural network from the perspective of a dynamical system. The goal of this paper is to improve the convergence rate of the sample-wise backpropagation algorithm in neural stochastic differential equation model which has been proposed in [Archibald et al., SIAM Journal on Numerical Analysis, 62 (2024), pp. 593-621]. It is necessary to emphasize that, improving the convergence orde…

Universal Deep Research: Bring Your Own Model and Strategy
Peter Belcak, Pavlo Molchanov
https://arxiv.org/abs/2509.00244 https://arxiv.org/pdf/2509.00244

Universal Deep Research: Bring Your Own Model and Strategy
Deep research tools are among the most impactful and most commonly encountered agentic systems today. We observe, however, that each deep research agent introduced so far is hard-coded to carry out a particular research strategy using a fixed choice of tools. We introduce Universal Deep Research (UDR), a generalist agentic system that wraps around any language model and enables the user to create, edit, and refine their own entirely custom deep research strategies without any need for additiona…

Heart Disease Prediction: A Comparative Study of Optimisers Performance in Deep Neural Networks
Chisom Chibuike, Adeyinka Ogunsanya
https://arxiv.org/abs/2509.08499 https://

Heart Disease Prediction: A Comparative Study of Optimisers Performance in Deep Neural Networks
Optimization has been an important factor and topic of interest in training deep learning models, yet less attention has been given to how we select the optimizers we use to train these models. Hence, there is a need to dive deeper into how we select the optimizers we use for training and the metrics that determine this selection. In this work, we compare the performance of 10 different optimizers in training a simple Multi-layer Perceptron model using a heart disease dataset from Kaggle. We se…

Leveraging GNN to Enhance MEF Method in Predicting ENSO
Saghar Ganji, Mohammad Naisipour
https://arxiv.org/abs/2508.07410 https://arxiv.org/pdf/2508.07410

Leveraging GNN to Enhance MEF Method in Predicting ENSO
Reliable long-lead forecasting of the El Nino Southern Oscillation (ENSO) remains a long-standing challenge in climate science. The previously developed Multimodal ENSO Forecast (MEF) model uses 80 ensemble predictions by two independent deep learning modules: a 3D Convolutional Neural Network (3D-CNN) and a time-series module. In their approach, outputs of the two modules are combined using a weighting strategy wherein one is prioritized over the other as a function of global performance. Sepa…

Dato: A Task-Based Programming Model for Dataflow Accelerators
Shihan Fang, Hongzheng Chen, Niansong Zhang, Jiajie Li, Han Meng, Adrian Liu, Zhiru Zhang
https://arxiv.org/abs/2509.06794

Dato: A Task-Based Programming Model for Dataflow Accelerators
Recent deep learning workloads increasingly push computational demand beyond what current memory systems can sustain, with many kernels stalling on data movement rather than computation. While modern dataflow accelerators incorporate on-chip streaming to mitigate off-chip bandwidth limitations, existing programming models struggle to harness these capabilities effectively. Low-level interfaces provide fine-grained control but impose significant development overhead, whereas high-level tile-base…

Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data
Beata E. Kowal, Krzysztof M. Graczyk, Artur M. Ankowski, Rwik Dharmapal Banerjee, Jose L. Bonilla, Hemant Prasad, Jan T. Sobczyk
https://arxiv.org/abs/2508.00996

Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data
We present an updated deep neural network model for inclusive electron-carbon scattering. Using the bootstrap model [Phys.Rev.C 110 (2024) 2, 025501] as a prior, we incorporate recent experimental data, as well as older measurements in the deep inelastic scattering region, to derive a re-optimized posterior model. We examine the impact of these new inputs on model predictions and associated uncertainties. Finally, we evaluate the resulting cross-section predictions in the kinematic range releva…

Personalized Sleep Prediction via Deep Adaptive Spatiotemporal Modeling and Sparse Data
Xueyi Wang (Claudine), C. J. C. (Claudine), Lamoth, Elisabeth Wilhelm
https://arxiv.org/abs/2509.09018

Personalized Sleep Prediction via Deep Adaptive Spatiotemporal Modeling and Sparse Data
A sleep forecast allows individuals and healthcare providers to anticipate and proactively address factors influencing restful rest, ultimately improving mental and physical well-being. This work presents an adaptive spatial and temporal model (AdaST-Sleep) for predicting sleep scores. Our proposed model combines convolutional layers to capture spatial feature interactions between multiple features and recurrent neural network layers to handle longer-term temporal health-related data. A domain …

Language Model Perplexity Predicts Scientific Surprise and Transformative Impact
Zhen Zhang (School of Information Management, Nanjing University, Knowledge Lab, University of Chicago), James Evans (Department of Sociology, University of Chicago, Santa Fe Institute)
https://arxiv.org/abs/2509.05591…

Language Model Perplexity Predicts Scientific Surprise and Transformative Impact
Scientific breakthroughs typically emerge through the surprising violation of established research ideas, yet quantifying surprise has remained elusive because it requires a coherent model of all contemporary scientific worldviews. Deep neural networks like large language models (LLMs) are arbitrary function approximators tuned to consistently expect the expressions and ideas on which they were trained and those semantically nearby. This suggests that as LLMs improve at generating plausible tex…

A diffusion model for light scattering in ejecta
J. A. Don Jayamanne, J. -R. Burie, O. Durand, R. Pierrat, R. Carminati
https://arxiv.org/abs/2507.04972 ht…

A diffusion model for light scattering in ejecta
We derive a diffusion equation for light scattering from ejecta produced by extreme shocks on metallic samples. This model is easier to handle than a more conventional model based on the Radiative Transfer Equation (RTE), and is a relevant tool to analyze spectrograms obtained from Photon Doppler Velocimetry (PDV) measurements in the deep multiple scattering regime. We also determine the limits of validity of the diffusive model compared to the RTE, based on a detailed analysis of various eject…

Crosslisted article(s) found for q-fin.GN. https://arxiv.org/list/q-fin.GN/new
[1/1]:
- An Interpretable Deep Learning Model for General Insurance Pricing
Patrick J. Laub, Tu Pho, Bernard Wong

Deep Learning Based Reconstruction Methods for Electrical Impedance Tomography
Alexander Denker, Fabio Margotti, Jianfeng Ning, Kim Knudsen, Derick Nganyu Tanyu, Bangti Jin, Andreas Hauptmann, Peter Maass
https://arxiv.org/abs/2508.06281

Deep Learning Based Reconstruction Methods for Electrical Impedance Tomography
Electrical Impedance Tomography (EIT) is a powerful imaging modality widely used in medical diagnostics, industrial monitoring, and environmental studies. The EIT inverse problem is about inferring the internal conductivity distribution of the concerned object from the voltage measurements taken on its boundary. This problem is severely ill-posed, and requires advanced computational approaches for accurate and reliable image reconstruction. Recent innovations in both model-based reconstruction …

A Multi-Electronic-State Model to Interpret the Apparent Anomalous Arrhenius Curve of OH HO$_2$ $\to$ O$_2$ H$_2$O
Xingyu Zhang, Jinke Yu, Qingyong Meng
https://arxiv.org/abs/2507.05563

A Multi-Electronic-State Model to Interpret the Apparent Anomalous Arrhenius Curve of OH + HO$_2$ $\to$ O$_2$ + H$_2$O
A comprehensive multi-electronic-state model for OH + HO2 -> O2 + H2O has been developed through extensive multi-reference configuration interaction (MRCI) calculations, aiming to elucidate two key experimental observations: (1) an unusually deep and narrow ``well'' in the Arrhenius curve near 1100K and (2) a slightly negative temperature dependence in the range of 200K~500K. Moreover, the present model can serve as the basis for constructing multi-state Hamiltonian in multi-dimensional quantum…

README: Robust Error-Aware Digital Signature Framework via Deep Watermarking Model
Hyunwook Choi, Sangyun Won, Daeyeon Hwang, Junhyeok Choi
https://arxiv.org/abs/2507.04495

README: Robust Error-Aware Digital Signature Framework via Deep Watermarking Model
Deep learning-based watermarking has emerged as a promising solution for robust image authentication and protection. However, existing models are limited by low embedding capacity and vulnerability to bit-level errors, making them unsuitable for cryptographic applications such as digital signatures, which require over 2048 bits of error-free data. In this paper, we propose README (Robust Error-Aware Digital Signature via Deep WaterMarking ModEl), a novel framework that enables robust, verifiabl…

DRO-EDL-MPC: Evidential Deep Learning-Based Distributionally Robust Model Predictive Control for Safe Autonomous Driving
Hyeongchan Ham, Heejin Ahn
https://arxiv.org/abs/2507.05710

DRO-EDL-MPC: Evidential Deep Learning-Based Distributionally Robust Model Predictive Control for Safe Autonomous Driving
Safety is a critical concern in motion planning for autonomous vehicles. Modern autonomous vehicles rely on neural network-based perception, but making control decisions based on these inference results poses significant safety risks due to inherent uncertainties. To address this challenge, we present a distributionally robust optimization (DRO) framework that accounts for both aleatoric and epistemic perception uncertainties using evidential deep learning (EDL). Our approach introduces a novel…

Deep Learning and Model Independence
Martin King
https://arxiv.org/abs/2507.03438 https://arxiv.org/pdf/2507.03438

Deep Learning and Model Independence
The lack of evidence in favor of any new physics models means that the search for new physics beyond the Standard Model (BSM) is wide open, with no direction clearly more promising than any other. This marks a turn towards what can be called `model-independent' methods-strategies that reduce the influence of modelling assumptions by performing minimally-biased precision measurements, using effective field theories, or using Deep Learning methods (DL). In this paper, I present the novel and prom…

TayFCS: Towards Light Feature Combination Selection for Deep Recommender Systems
Xianquan Wang, Zhaocheng Du, Jieming Zhu, Chuhan Wu, Qinglin Jia, Zhenhua Dong
https://arxiv.org/abs/2507.03895

TayFCS: Towards Light Feature Combination Selection for Deep Recommender Systems
Feature interaction modeling is crucial for deep recommendation models. A common and effective approach is to construct explicit feature combinations to enhance model performance. However, in practice, only a small fraction of these combinations are truly informative. Thus it is essential to select useful feature combinations to reduce noise and manage memory consumption. While feature selection methods have been extensively studied, they are typically limited to selecting individual features. …

Replaced article(s) found for eess.AS. https://arxiv.org/list/eess.AS/new
[1/1]:
- A Self-Attention-Driven Deep Denoiser Model for Real Time Lung Sound Denoising in Noisy Environments
Samiul Based Shuvo, Syed Samiul Alam, Taufiq Hasan

NeurStore: Efficient In-database Deep Learning Model Management System
Siqi Xiang, Sheng Wang, Xiaokui Xiao, Cong Yue, Zhanhao Zhao, Beng Chin Ooi
https://arxiv.org/abs/2509.03228

NeurStore: Efficient In-database Deep Learning Model Management System
With the prevalence of in-database AI-powered analytics, there is an increasing demand for database systems to efficiently manage the ever-expanding number and size of deep learning models. However, existing database systems typically store entire models as monolithic files or apply compression techniques that overlook the structural characteristics of deep learning models, resulting in suboptimal model storage overhead. This paper presents NeurStore, a novel in-database model management system…

A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering
Shahana Yasmin Chowdhury, Bithi Banik, Md Tamjidul Hoque, Shreya Banerjee
https://arxiv.org/abs/2507.07046

A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering
Nowadays, speech emotion recognition (SER) plays a vital role in the field of human-computer interaction (HCI) and the evolution of artificial intelligence (AI). Our proposed DCRF-BiLSTM model is used to recognize seven emotions: neutral, happy, sad, angry, fear, disgust, and surprise, which are trained on five datasets: RAVDESS (R), TESS (T), SAVEE (S), EmoDB (E), and Crema-D (C). The model achieves high accuracy on individual datasets, including 97.83% on RAVDESS, 97.02% on SAVEE, 95.10% for …

From large-eddy simulations to deep learning: A U-net model for fast urban canopy flow predictions
Themistoklis Vargiemezis, Catherine Gorl\'e
https://arxiv.org/abs/2507.06533

From large-eddy simulations to deep learning: A U-net model for fast urban canopy flow predictions
Accurate prediction of wind flow fields in urban canopies is crucial for ensuring pedestrian comfort, safety, and sustainable urban design. Traditional methods using wind tunnels and Computational Fluid Dynamics, such as Large-Eddy Simulations (LES), are limited by high costs, computational demands, and time requirements. This study presents a deep neural network (DNN) approach for fast and accurate predictions of urban wind flow fields, reducing computation time from an order of 10 hours on 32…

Norway's Northern Lights project is seen as a model for efforts to pump carbon dioxide deep into wells, but high costs remain an obstacle
https://www.nytimes.com/2025/08/05/business/norway-cabon-capture-northern-lights.html

Norway’s Hedged Bet on Europe’s Energy Future: A Garbage Disposal for Emissions
A business called Northern Lights is seen as a model for efforts to pump carbon dioxide deep into wells, but high costs remain an obstacle.

Identifying Exoplanets with Deep Learning: A CNN and RNN Classifier for Kepler DR25 and Candidate Vetting
Bibin Thomas, Vittal Bhat M, Salman Arafath Mohammed, Abdul Wase Mohammed, Adis Abebaw Dessalegn, Mohit Mittal
https://arxiv.org/abs/2509.04793

Identifying Exoplanets with Deep Learning: A CNN and RNN Classifier for Kepler DR25 and Candidate Vetting
The rapid expansion of exoplanet survey missions such as Kepler, TESS, and the upcoming PLATO mission has generated massive light-curve datasets that challenge traditional vetting pipelines. We introduce a hybrid deep-learning framework that integrates convolutional networks, bidirectional LSTMs, and an attention mechanism to identify planetary transit signals with improved accuracy and interpretability. Trained on Kepler DR25 data, the model achieves F1 = $0.910 \pm 0.008$ (AUC--ROC = $0.984 \…

AquaCast: Urban Water Dynamics Forecasting with Precipitation-Informed Multi-Input Transformer
Golnoosh Abdollahinejad, Saleh Baghersalimi, Denisa-Andreea Constantinescu, Sergey Shevchik, David Atienza
https://arxiv.org/abs/2509.09458

AquaCast: Urban Water Dynamics Forecasting with Precipitation-Informed Multi-Input Transformer
This work addresses the challenge of forecasting urban water dynamics by developing a multi-input, multi-output deep learning model that incorporates both endogenous variables (e.g., water height or discharge) and exogenous factors (e.g., precipitation history and forecast reports). Unlike conventional forecasting, the proposed model, AquaCast, captures both inter-variable and temporal dependencies across all inputs, while focusing forecast solely on endogenous variables. Exogenous inputs are f…

Membership Inference Attacks with False Discovery Rate Control
Chenxu Zhao, Wei Qian, Aobo Chen, Mengdi Huai
https://arxiv.org/abs/2508.07066 https://arxiv…

Membership Inference Attacks with False Discovery Rate Control
Recent studies have shown that deep learning models are vulnerable to membership inference attacks (MIAs), which aim to infer whether a data record was used to train a target model or not. To analyze and study these vulnerabilities, various MIA methods have been proposed. Despite the significance and popularity of MIAs, existing works on MIAs are limited in providing guarantees on the false discovery rate (FDR), which refers to the expected proportion of false discoveries among the identified p…

Optimization Method of Multi-factor Investment Model Driven by Deep Learning for Risk Control
Ruisi Li, Xinhui Gu
https://arxiv.org/abs/2507.00332 https://…

Optimization Method of Multi-factor Investment Model Driven by Deep Learning for Risk Control
Propose a deep learning driven multi factor investment model optimization method for risk control. By constructing a deep learning model based on Long Short Term Memory (LSTM) and combining it with a multi factor investment model, we optimize factor selection and weight determination to enhance the model's adaptability and robustness to market changes. Empirical analysis shows that the LSTM model is significantly superior to the benchmark model in risk control indicators such as maximum retrace…

Computation-resource-efficient Task-oriented Communications
Jingwen Fu, Ming Xiao, Chao Ren, Mikael Skoglund
https://arxiv.org/abs/2507.07422 https://

Computation-resource-efficient Task-oriented Communications
The rapid development of deep-learning enabled task-oriented communications (TOC) significantly shifts the paradigm of wireless communications. However, the high computation demands, particularly in resource-constrained systems e.g., mobile phones and UAVs, make TOC challenging for many tasks. To address the problem, we propose a novel TOC method with two models: a static and a dynamic model. In the static model, we apply a neural network (NN) as a task-oriented encoder (TOE) when there is no c…

Hummingbird: A Smaller and Faster Large Language Model Accelerator on Embedded FPGA
Jindong Li, Tenglong Li, Ruiqi Chen, Guobin Shen, Dongcheng Zhao, Qian Zhang, Yi Zeng
https://arxiv.org/abs/2507.03308

Hummingbird: A Smaller and Faster Large Language Model Accelerator on Embedded FPGA
Deploying large language models (LLMs) on embedded devices remains a significant research challenge due to the high computational and memory demands of LLMs and the limited hardware resources available in such environments. While embedded FPGAs have demonstrated performance and energy efficiency in traditional deep neural networks, their potential for LLM inference remains largely unexplored. Recent efforts to deploy LLMs on FPGAs have primarily relied on large, expensive cloud-grade hardware a…

Two-dimensional Sparse Parallelism for Large Scale Deep Learning Recommendation Model Training
Xin Zhang, Quanyu Zhu, Liangbei Xu, Zain Huda, Wang Zhou, Jin Fang, Dennis van der Staay, Yuxi Hu, Jade Nie, Jiyan Yang, Chunzhi Yang
https://arxiv.org/abs/2508.03854

Two-dimensional Sparse Parallelism for Large Scale Deep Learning Recommendation Model Training
The increasing complexity of deep learning recommendation models (DLRM) has led to a growing need for large-scale distributed systems that can efficiently train vast amounts of data. In DLRM, the sparse embedding table is a crucial component for managing sparse categorical features. Typically, these tables in industrial DLRMs contain trillions of parameters, necessitating model parallelism strategies to address memory constraints. However, as training systems expand with massive GPUs, the tradi…

Towards Unveiling Predictive Uncertainty Vulnerabilities in the Context of the Right to Be Forgotten
Wei Qian, Chenxu Zhao, Yangyi Li, Wenqian Ye, Mengdi Huai
https://arxiv.org/abs/2508.07458

Towards Unveiling Predictive Uncertainty Vulnerabilities in the Context of the Right to Be Forgotten
Currently, various uncertainty quantification methods have been proposed to provide certainty and probability estimates for deep learning models' label predictions. Meanwhile, with the growing demand for the right to be forgotten, machine unlearning has been extensively studied as a means to remove the impact of requested sensitive data from a pre-trained model without retraining the model from scratch. However, the vulnerabilities of such generated predictive uncertainties with regard to dedic…

Deep Learning Atmospheric Models Reliably Simulate Out-of-Sample Land Heat and Cold Wave Frequencies
Zilu Meng, Gregory J. Hakim, Wenchang Yang, Gabriel A. Vecchi
https://arxiv.org/abs/2507.03176

Deep Learning Atmospheric Models Reliably Simulate Out-of-Sample Land Heat and Cold Wave Frequencies
Deep learning (DL)-based general circulation models (GCMs) are emerging as fast simulators, yet their ability to replicate extreme events outside their training range remains unknown. Here, we evaluate two such models -- the hybrid Neural General Circulation Model (NGCM) and purely data-driven Deep Learning Earth System Model (DL\textit{ESy}M) -- against a conventional high-resolution land-atmosphere model (HiRAM) in simulating land heatwaves and coldwaves. All models are forced with observed s…

Label-Efficient Chest X-ray Diagnosis via Partial CLIP Adaptation
Heet Nitinkumar Dalsania
https://arxiv.org/abs/2507.07254 https://a…

Label-Efficient Chest X-ray Diagnosis via Partial CLIP Adaptation
Modern deep learning implementations for medical imaging usually rely on large labeled datasets. These datasets are often difficult to obtain due to privacy concerns, high costs, and even scarcity of cases. In this paper, a label-efficient strategy is proposed for chest X-ray diagnosis that seeks to reflect real-world hospital scenarios. The experiments use the NIH Chest X-ray14 dataset and a pre-trained CLIP ViT-B/32 model. The model is adapted via partial fine-tuning of its visual encoder and…

Enhancing Environment Generalizability for Deep Learning-Based CSI Feedback
Haoyu Wang, Shuangfeng Han, Xiaoyun Wang, Zhi Sun
https://arxiv.org/abs/2507.06833

Enhancing Environment Generalizability for Deep Learning-Based CSI Feedback
Accurate and low-overhead channel state information (CSI) feedback is essential to boost the capacity of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. Deep learning-based CSI feedback significantly outperforms conventional approaches. Nevertheless, current deep learning-based CSI feedback algorithms exhibit limited generalizability to unseen environments, which obviously increases the deployment cost. In this paper, we first model the distribution shift …

Feature Space Analysis by Guided Diffusion Model
Kimiaki Shirahama, Miki Yanobu, Kaduki Yamashita, Miho Ohsaki
https://arxiv.org/abs/2509.07936 https://arx…

Feature Space Analysis by Guided Diffusion Model
One of the key issues in Deep Neural Networks (DNNs) is the black-box nature of their internal feature extraction process. Targeting vision-related domains, this paper focuses on analysing the feature space of a DNN by proposing a decoder that can generate images whose features are guaranteed to closely match a user-specified feature. Owing to this guarantee that is missed in past studies, our decoder allows us to evidence which of various attributes in an image are encoded into a feature by th…

Bayesian Double Descent
Nick Polson, Vadim Sokolov
https://arxiv.org/abs/2507.07338 https://arxiv.org/pdf/2507.07338

Bayesian Double Descent
Double descent is a phenomenon of over-parameterized statistical models. Our goal is to view double descent from a Bayesian perspective. Over-parameterized models such as deep neural networks have an interesting re-descending property in their risk characteristics. This is a recent phenomenon in machine learning and has been the subject of many studies. As the complexity of the model increases, there is a U-shaped region corresponding to the traditional bias-variance trade-off, but then as the …

Joint Transcription of Acoustic Guitar Strumming Directions and Chords
Sebastian Murgul, Johannes Schimper, Michael Heizmann
https://arxiv.org/abs/2508.07973 https://

Joint Transcription of Acoustic Guitar Strumming Directions and Chords
Automatic transcription of guitar strumming is an underrepresented and challenging task in Music Information Retrieval (MIR), particularly for extracting both strumming directions and chord progressions from audio signals. While existing methods show promise, their effectiveness is often hindered by limited datasets. In this work, we extend a multimodal approach to guitar strumming transcription by introducing a novel dataset and a deep learning-based transcription model. We collect 90 min of r…

DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective
Shuo Shao, Yiming Li, Mengren Zheng, Zhiyang Hu, Yukun Chen, Boheng Li, Yu He, Junfeng Guo, Tianwei Zhang, Dacheng Tao, Zhan Qin
https://arxiv.org/abs/2507.05622

DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective
The widespread application of Deep Learning across diverse domains hinges critically on the quality and composition of training datasets. However, the common lack of disclosure regarding their usage raises significant privacy and copyright concerns. Dataset auditing techniques, which aim to determine if a specific dataset was used to train a given suspicious model, provide promising solutions to addressing these transparency gaps. While prior work has developed various auditing methods, their r…

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models
Jonathan Teagan
https://arxiv.org/abs/2509.07813 https://arxiv.org/pdf/2509…

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models
This study applies a range of forecasting techniques,including ARIMA, Prophet, Long Short Term Memory networks (LSTM), Temporal Convolutional Networks (TCN), and XGBoost, to model and predict Russian equipment losses during the ongoing war in Ukraine. Drawing on daily and monthly open-source intelligence (OSINT) data from WarSpotting, we aim to assess trends in attrition, evaluate model performance, and estimate future loss patterns through the end of 2025. Our findings show that deep learning …

Anticipating AMOC transitions via deep learning
Wenjie Zhang, Yu Huang, Sebastian Bathiany, Yechul Shin, Maya Ben-Yami, Suiping Zhou, Niklas Boers
https://arxiv.org/abs/2509.06450

Anticipating AMOC transitions via deep learning
Key components of the Earth system can undergo abrupt and potentially irreversible transitions when the magnitude or rate of external forcing exceeds critical thresholds. In this study, we use the example of the Atlantic Meridional Overturning Circulation (AMOC) to demonstrate the challenges associated with anticipating such transitions when the system is susceptible to bifurcation-induced, rate-induced, and noise-induced tipping. Using a calibrated AMOC box model, we conduct large ensemble sim…

Transverse momentum Resummation and Analytic continuation into the Deep Infrared
Andrea Simonelli
https://arxiv.org/abs/2509.06843 https://arxiv.org/pdf/25…

Transverse momentum Resummation and Analytic continuation into the Deep Infrared
Extracting information on the three-dimensional structure of hadrons from transverse-momentum data is a challenging task, requiring the separation of perturbative contributions under QCD control from genuinely non-perturbative effects, unavoidably affected by phenomenological bias. Standard approaches model transverse-momentum-dependent (TMD) observables in impact-parameter space and compare them with data after Fourier inversion, generating an interplay between perturbative terms, non-perturba…

Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems
Doan Hieu Nguyen, Xuan Tung Nguyen, Seon-Geun Jeong, Trinh Van Chien, Lajos Hanzo, Won Joo Hwang
https://arxiv.org/abs/2507.06585

Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems
A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic sum throughput. The existing model-based solutions found in the literature are inefficient and/or computationally demanding. Similarly, conventional deep neural networks may struggle in the face of high-dimensional inputs, require complex architectures, and their convergence is slow due to training numerous …

Exploring a Specialized Eccentricity-Based Deep Neural Network Model to Simulate Visual Attention
Manvi Jain
https://arxiv.org/abs/2507.05031 https://

Exploring a Specialized Eccentricity-Based Deep Neural Network Model to Simulate Visual Attention
While visual attention theories abound, neurodevelopmental research remains constrained by infants' unreliable responses and limited attention spans. Through collaboration with Project Prakash, we accessed a unique population: patients gaining vision later in life. This cohort enables investigation of visual process development in cognitively mature, cooperative participants rather than infants. We collected data from pre-operation patients, post-operation patients tracked longitudinally (1, 3,…

Unveiling the Underwater World: CLIP Perception Model-Guided Underwater Image Enhancement
Jiangzhong Cao, Zekai Zeng, Xu Zhang, Huan Zhang, Chunling Fan, Gangyi Jiang, Weisi Lin
https://arxiv.org/abs/2507.06234

Unveiling the Underwater World: CLIP Perception Model-Guided Underwater Image Enhancement
High-quality underwater images are essential for both machine vision tasks and viewers with their aesthetic appeal.However, the quality of underwater images is severely affected by light absorption and scattering. Deep learning-based methods for Underwater Image Enhancement (UIE) have achieved good performance. However, these methods often overlook considering human perception and lack sufficient constraints within the solution space. Consequently, the enhanced images often suffer from diminish…

Robust Model Predictive Control Design for Autonomous Vehicles with Perception-based Observers
Nariman Niknejad, Gokul S. Sankar, Bahare Kiumarsi, Hamidreza Modares
https://arxiv.org/abs/2509.05201

Robust Model Predictive Control Design for Autonomous Vehicles with Perception-based Observers
This paper presents a robust model predictive control (MPC) framework that explicitly addresses the non-Gaussian noise inherent in deep learning-based perception modules used for state estimation. Recognizing that accurate uncertainty quantification of the perception module is essential for safe feedback control, our approach departs from the conventional assumption of zero-mean noise quantification of the perception error. Instead, it employs set-based state estimation with constrained zonotop…

Deep Learning-based Human Gesture Channel Modeling for Integrated Sensing and Communication Scenarios
Zhengyu Zhang, Neeraj Varshney, Jelena Senic, Raied Caromi, Samuel Berweger, Camillo Gentile, Enrico M. Vitucci, Ruisi He, Vittorio Degli-Esposti
https://arxiv.org/abs/2507.06588

Deep Learning-based Human Gesture Channel Modeling for Integrated Sensing and Communication Scenarios
With the development of Integrated Sensing and Communication (ISAC) for Sixth-Generation (6G) wireless systems, contactless human recognition has emerged as one of the key application scenarios. Since human gesture motion induces subtle and random variations in wireless multipath propagation, how to accurately model human gesture channels has become a crucial issue for the design and validation of ISAC systems. To this end, this paper proposes a deep learning-based human gesture channel modelin…

Hypergraph-Guided Regex Filter Synthesis for Event-Based Anomaly Detection
Margarida Ferreira, Victor Nicolet, Luan Pham, Joey Dodds, Daniel Kroening, Ines Lynce, Ruben Martins
https://arxiv.org/abs/2509.06911

Hypergraph-Guided Regex Filter Synthesis for Event-Based Anomaly Detection
We propose HyGLAD, a novel algorithm that automatically builds a set of interpretable patterns that model event data. These patterns can then be used to detect event-based anomalies in a stationary system, where any deviation from past behavior may indicate malicious activity. The algorithm infers equivalence classes of entities with similar behavior observed from the events, and then builds regular expressions that capture the values of those entities. As opposed to deep-learning approaches, t…

uGMM-NN: Univariate Gaussian Mixture Model Neural Network
Zakeria Sharif Ali
https://arxiv.org/abs/2509.07569 https://arxiv.org/pdf/2509.07569

uGMM-NN: Univariate Gaussian Mixture Model Neural Network
This paper introduces the Univariate Gaussian Mixture Model Neural Network (uGMM-NN), a novel neural architecture that embeds probabilistic reasoning directly into the computational units of deep networks. Unlike traditional neurons, which apply weighted sums followed by fixed nonlinearities, each uGMM-NN node parameterizes its activations as a univariate Gaussian mixture, with learnable means, variances, and mixing coefficients. This design enables richer representations by capturing multimoda…

NestGNN: A Graph Neural Network Framework Generalizing the Nested Logit Model for Travel Mode Choice
Yuqi Zhou, Zhanhong Cheng, Lingqian Hu, Yuheng Bu, Shenhao Wang
https://arxiv.org/abs/2509.07123

NestGNN: A Graph Neural Network Framework Generalizing the Nested Logit Model for Travel Mode Choice
Nested logit (NL) has been commonly used for discrete choice analysis, including a wide range of applications such as travel mode choice, automobile ownership, or location decisions. However, the classical NL models are restricted by their limited representation capability and handcrafted utility specification. While researchers introduced deep neural networks (DNNs) to tackle such challenges, the existing DNNs cannot explicitly capture inter-alternative correlations in the discrete choice cont…

DuoServe-MoE: Dual-Phase Expert Prefetch and Cache Scheduling for Efficient MoE LLM Inference
Yuning Zhang, Grant Pinkert, Nan Yang, Yanli Li, Dong Yuan
https://arxiv.org/abs/2509.07379

DuoServe-MoE: Dual-Phase Expert Prefetch and Cache Scheduling for Efficient MoE LLM Inference
Large Language Models (LLMs) have demonstrated impressive performance across a wide range of deep learning tasks. Mixture of Experts (MoE) further enhances their capabilities by increasing model width through sparsely activated expert branches, which keeps inference computation efficient. However, the large number of expert weights introduces significant GPU memory pressure, especially in resource-constrained environments such as single-GPU servers. More importantly, MoE inference consists of t…

Wrist bone segmentation in X-ray images using CT-based simulations
Youssef ElTantawy, Alexia Karantana, Xin Chen
https://arxiv.org/abs/2507.07131 https://

Wrist bone segmentation in X-ray images using CT-based simulations
Plain X-ray is one of the most common image modalities for clinical diagnosis (e.g. bone fracture, pneumonia, cancer screening, etc.). X-ray image segmentation is an essential step for many computer-aided diagnostic systems, yet it remains challenging. Deep-learning-based methods have achieved superior performance in medical image segmentation tasks but often require a large amount of high-quality annotated data for model training. Providing such an annotated dataset is not only time-consuming …

MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization
Mohammed Tiouti, Mohamed Bal-Ghaoui
https://arxiv.org/abs/2509.09387 https://

MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization
Effective model and hyperparameter selection remains a major challenge in deep learning, often requiring extensive expertise and computation. While AutoML and large language models (LLMs) promise automation, current LLM-based approaches rely on trial and error and expensive APIs, which provide limited interpretability and generalizability. We propose MetaLLMiX, a zero-shot hyperparameter optimization framework combining meta-learning, explainable AI, and efficient LLM reasoning. By leveraging h…

Modular PE-Structured Learning for Cross-Task Wireless Communications
Yuxuan Duan, Chenyang Yang
https://arxiv.org/abs/2509.08614 https://arxiv.org/pdf/250…

Modular PE-Structured Learning for Cross-Task Wireless Communications
Recent trends in learning wireless policies attempt to develop deep neural networks (DNNs) for handling multiple tasks with a single model. Existing approaches often rely on large models, which are hard to pre-train and fine-tune at the wireless edge. In this work, we challenge this paradigm by leveraging the structured knowledge of wireless problems -- specifically, permutation equivariant (PE) properties. We design three types of PE-aware modules, two of which are Transformer-style sub-layers…

GUIDe: Generative and Uncertainty-Informed Inverse Design for On-Demand Nonlinear Functional Responses
Haoxuan Dylan Mu, Mingjian Tang, Wei Gao, Wei "Wayne" Chen
https://arxiv.org/abs/2509.05641

GUIDe: Generative and Uncertainty-Informed Inverse Design for On-Demand Nonlinear Functional Responses
Inverse design problems are pervasive in engineering, particularly when dealing with nonlinear system responses, such as in mechanical behavior or spectral analysis. The inherent intractability, non-existence, or non-uniqueness of their solutions, and the need for swift exploration of the solution space necessitate the adoption of machine learning and data-driven approaches, such as deep generative models. Here, we show that both deep generative model-based and optimization-based methods can yi…

SGD Convergence under Stepsize Shrinkage in Low-Precision Training
Vincent-Daniel Yun
https://arxiv.org/abs/2508.07142 https://arxiv.org/pdf/2508.07142

SGD Convergence under Stepsize Shrinkage in Low-Precision Training
Low-precision training has become essential for reducing the computational and memory costs of large-scale deep learning. However, quantization of gradients introduces both magnitude shrinkage and additive noise, which can alter the convergence behavior of stochastic gradient descent (SGD). In this work, we study the convergence of SGD under a gradient shrinkage model, where each stochastic gradient is scaled by a factor $q_k \in (0,1]$ and perturbed by zero-mean quantization noise. We show tha…

A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Behnoud Shafiezadeh, Amir Mashmool, Farshad Eshghi, Manoochehr Kelarestaghi
https://arxiv.org/abs/2509.06868

A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Automatic License-Plate Recognition (ALPR) plays a pivotal role in Intelligent Transportation Systems (ITS) as a fundamental element of Smart Cities. However, due to its high variability, ALPR faces challenging issues more efficiently addressed by deep learning techniques. In this paper, a selective Generative Adversarial Network (GAN) is proposed for deblurring in the preprocessing step, coupled with the state-of-the-art You-Only-Look-Once (YOLO)v5 object detection architectures for License-Pl…

SPENCER: Self-Adaptive Model Distillation for Efficient Code Retrieval
Wenchao Gu, Zongyi Lyu, Yanlin Wang, Hongyu Zhang, Cuiyun Gao, Michael R. Lyu
https://arxiv.org/abs/2508.00546

SPENCER: Self-Adaptive Model Distillation for Efficient Code Retrieval
Code retrieval aims to provide users with desired code snippets based on users' natural language queries. With the development of deep learning technologies, adopting pre-trained models for this task has become mainstream. Considering the retrieval efficiency, most of the previous approaches adopt a dual-encoder for this task, which encodes the description and code snippet into representation vectors, respectively. However, the model structure of the dual-encoder tends to limit the model's perf…

EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization
Dingyi Rong, Haotian Lu, Wenzhuo Zheng, Fan Zhang, Shuangjia Zheng, Ning Liu
https://arxiv.org/abs/2506.09496

EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization
Designing protein sequences with optimal energetic stability is a key challenge in protein inverse folding, as current deep learning methods are primarily trained by maximizing sequence recovery rates, often neglecting the energy of the generated sequences. This work aims to overcome this limitation by developing a model that directly generates low-energy, stable protein sequences. We propose EnerBridge-DPO, a novel inverse folding framework focused on generating low-energy, high-stability prot…

A Novel Active Learning Approach to Label One Million Unknown Malware Variants
Ahmed Bensaoud, Jugal Kalita
https://arxiv.org/abs/2507.02959 https://

A Novel Active Learning Approach to Label One Million Unknown Malware Variants
Active learning for classification seeks to reduce the cost of labeling samples by finding unlabeled examples about which the current model is least certain and sending them to an annotator/expert to label. Bayesian theory can provide a probabilistic view of deep neural network models by asserting a prior distribution over model parameters and estimating the uncertainties by posterior distribution over these parameters. This paper proposes two novel active learning approaches to label one milli…

Integrating Pathology Foundation Models and Spatial Transcriptomics for Cellular Decomposition from Histology Images
Yutong Sun, Sichen Zhu, Peng Qiu
https://arxiv.org/abs/2507.07013

Integrating Pathology Foundation Models and Spatial Transcriptomics for Cellular Decomposition from Histology Images
The rapid development of digital pathology and modern deep learning has facilitated the emergence of pathology foundation models that are expected to solve general pathology problems under various disease conditions in one unified model, with or without fine-tuning. In parallel, spatial transcriptomics has emerged as a transformative technology that enables the profiling of gene expression on hematoxylin and eosin (H&E) stained histology images. Spatial transcriptomics unlocks the unprecedented…

PriceFM: Foundation Model for Probabilistic Electricity Price Forecasting
Runyao Yu, Chenhui Gu, Jochen Stiasny, Qingsong Wen, Wasim Sarwar Dilov, Lianlian Qi, Jochen L. Cremer
https://arxiv.org/abs/2508.04875

PriceFM: Foundation Model for Probabilistic Electricity Price Forecasting
Electricity price forecasting in Europe presents unique challenges due to the continent's increasingly integrated and physically interconnected power market. While recent advances in deep learning and foundation models have led to substantial improvements in general time series forecasting, most existing approaches fail to capture the complex spatial interdependencies and uncertainty inherent in electricity markets. In this paper, we address these limitations by introducing a comprehensive and …

MultiSurv: A Multimodal Deep Survival Framework for Prostrate and Bladder Cancer
Noorul Wahab, Ethar Alzaid, Jiaqi Lv, Adam Shephard, Shan E Ahmed Raza
https://arxiv.org/abs/2509.05037

MultiSurv: A Multimodal Deep Survival Framework for Prostrate and Bladder Cancer
Accurate prediction of time-to-event outcomes is a central challenge in oncology, with significant implications for treatment planning and patient management. In this work, we present MultiSurv, a multimodal deep survival model utilising DeepHit with a projection layer and inter-modality cross-attention, which integrates heterogeneous patient data, including clinical, MRI, RNA-seq and whole-slide pathology features. The model is designed to capture complementary prognostic signals across modali…

Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling
Yan Sun, Faming Liang
https://arxiv.org/abs/2508.01217 https://arxiv.org/…

Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling
Deep learning has revolutionized modern data science. However, how to accurately quantify the uncertainty of predictions from large-scale deep neural networks (DNNs) remains an unresolved issue. To address this issue, we introduce a novel post-processing approach. This approach feeds the output from the last hidden layer of a pre-trained large-scale DNN model into a stochastic neural network (StoNet), then trains the StoNet with a sparse penalty on a validation dataset and constructs prediction…

Context-Aware Deep Learning for Robust Channel Extrapolation in Fluid Antenna Systems
Yanliang Jin, Runze Yu, Yuan Gao, Shengli Liu, Xiaoli Chu, Kai-Kit Wong, Chan-Byoung Chae
https://arxiv.org/abs/2507.04435

Context-Aware Deep Learning for Robust Channel Extrapolation in Fluid Antenna Systems
Fluid antenna systems (FAS) offer remarkable spatial flexibility but face significant challenges in acquiring high-resolution channel state information (CSI), leading to considerable overhead. To address this issue, we propose CANet, a robust deep learning model for channel extrapolation in FAS. CANet combines context-adaptive modeling with a cross-scale attention mechanism and is built on a ConvNeXt v2 backbone to improve extrapolation accuracy for unobserved antenna ports. To further enhance …

PUUMA (Placental patch and whole-Uterus dual-branch U-Mamba-based Architecture): Functional MRI Prediction of Gestational Age at Birth and Preterm Risk
Diego Fajardo-Rojas, Levente Baljer, Jordina Aviles Verdera, Megan Hall, Daniel Cromb, Mary A. Rutherford, Lisa Story, Emma C. Robinson, Jana Hutter
https://arxiv.org/abs/2509.07042

PUUMA (Placental patch and whole-Uterus dual-branch U-Mamba-based Architecture): Functional MRI Prediction of Gestational Age at Birth and Preterm Risk
Preterm birth is a major cause of mortality and lifelong morbidity in childhood. Its complex and multifactorial origins limit the effectiveness of current clinical predictors and impede optimal care. In this study, a dual-branch deep learning architecture (PUUMA) was developed to predict gestational age (GA) at birth using T2* fetal MRI data from 295 pregnancies, encompassing a heterogeneous and imbalanced population. The model integrates both global whole-uterus and local placental features. I…

CTA: Cross-Task Alignment for Better Test Time Training
Samuel Barbeau, Pedram Fekri, David Osowiechi, Ali Bahri, Moslem YazdanpanahMasih Aminbeidokhti, Christian Desrosiers
https://arxiv.org/abs/2507.05221

CTA: Cross-Task Alignment for Better Test Time Training
Deep learning models have demonstrated exceptional performance across a wide range of computer vision tasks. However, their performance often degrades significantly when faced with distribution shifts, such as domain or dataset changes. Test-Time Training (TTT) has emerged as an effective method to enhance model robustness by incorporating an auxiliary unsupervised task during training and leveraging it for model updates at test time. In this work, we introduce CTA (Cross-Task Alignment), a nov…

ACE and Diverse Generalization via Selective Disagreement
Oliver Daniels, Stuart Armstrong, Alexandre Maranh\~ao, Mahirah Fairuz Rahman, Benjamin M. Marlin, Rebecca Gorman
https://arxiv.org/abs/2509.07955

ACE and Diverse Generalization via Selective Disagreement
Deep neural networks are notoriously sensitive to spurious correlations - where a model learns a shortcut that fails out-of-distribution. Existing work on spurious correlations has often focused on incomplete correlations,leveraging access to labeled instances that break the correlation. But in cases where the spurious correlations are complete, the correct generalization is fundamentally \textit{underspecified}. To resolve this underspecification, we propose learning a set of concepts that are…

Stochastic forest transition model dynamics and parameter estimation via deep learning
Satoshi Kumabe, Tianyu Song, Ton Viet Ta
https://arxiv.org/abs/2507.21486 https://

Stochastic forest transition model dynamics and parameter estimation via deep learning
Forest transitions, characterized by dynamic shifts between forest, agricultural, and abandoned lands, are complex phenomena. This study developed a stochastic differential equation model to capture the intricate dynamics of these transitions. We established the existence of global positive solutions for the model and conducted numerical analyses to assess the impact of model parameters on deforestation incentives. To address the challenge of parameter estimation, we proposed a novel deep learn…

WiFo-CF: Wireless Foundation Model for CSI Feedback
Liu Xuanyu, Gao Shijian, Liu Boxun, Cheng Xiang, Yang Liuqing
https://arxiv.org/abs/2508.04068 https://…

WiFo-CF: Wireless Foundation Model for CSI Feedback
Deep learning-based channel state information (CSI) feedback schemes demonstrate strong compression capabilities but are typically constrained to fixed system configurations, limiting their generalization and flexibility. To address this challenge, WiFo-CF, a novel wireless foundation model tailored for CSI feedback, is proposed, uniquely accommodating heterogeneous configurations such as varying channel dimensions, feedback rates, and data distributions within a unified framework through its k…

EfficientNet in Digital Twin-based Cardiac Arrest Prediction and Analysis
Qasim Zia, Avais Jan, Zafar Iqbal, Muhammad Mumtaz Ali, Mukarram Ali, Murray Patterson
https://arxiv.org/abs/2509.07388

EfficientNet in Digital Twin-based Cardiac Arrest Prediction and Analysis
Cardiac arrest is one of the biggest global health problems, and early identification and management are key to enhancing the patient's prognosis. In this paper, we propose a novel framework that combines an EfficientNet-based deep learning model with a digital twin system to improve the early detection and analysis of cardiac arrest. We use compound scaling and EfficientNet to learn the features of cardiovascular images. In parallel, the digital twin creates a realistic and individualized card…

MRI-Based Brain Tumor Detection through an Explainable EfficientNetV2 and MLP-Mixer-Attention Architecture
Mustafa Yurdakul, \c{S}akir Ta\c{s}demir
https://arxiv.org/abs/2509.06713

MRI-Based Brain Tumor Detection through an Explainable EfficientNetV2 and MLP-Mixer-Attention Architecture
Brain tumors are serious health problems that require early diagnosis due to their high mortality rates. Diagnosing tumors by examining Magnetic Resonance Imaging (MRI) images is a process that requires expertise and is prone to error. Therefore, the need for automated diagnosis systems is increasing day by day. In this context, a robust and explainable Deep Learning (DL) model for the classification of brain tumors is proposed. In this study, a publicly available Figshare dataset containing 3,…

MedMambaLite: Hardware-Aware Mamba for Medical Image Classification
Romina Aalishah, Mozhgan Navardi, Tinoosh Mohsenin
https://arxiv.org/abs/2508.05049 https://

MedMambaLite: Hardware-Aware Mamba for Medical Image Classification
AI-powered medical devices have driven the need for real-time, on-device inference such as biomedical image classification. Deployment of deep learning models at the edge is now used for applications such as anomaly detection and classification in medical images. However, achieving this level of performance on edge devices remains challenging due to limitations in model size and computational capacity. To address this, we present MedMambaLite, a hardware-aware Mamba-based model optimized throug…

Accuracy-Constrained CNN Pruning for Efficient and Reliable EEG-Based Seizure Detection
Mounvik K, N Harshit
https://arxiv.org/abs/2509.05190 https://arxiv…

Accuracy-Constrained CNN Pruning for Efficient and Reliable EEG-Based Seizure Detection
Deep learning models, especially convolutional neural networks (CNNs), have shown considerable promise for biomedical signals such as EEG-based seizure detection. However, these models come with challenges, primarily due to their size and compute requirements in environments where real-time detection or limited resources are available. In this study, we present a lightweight one-dimensional CNN model with structured pruning to improve efficiency and reliability. The model was trained with mild …

Heterogeneous Causal Learning for Optimizing Aggregated Functions in User Growth
Shuyang Du, Jennifer Zhang, Will Y. Zou
https://arxiv.org/abs/2507.05510 h…

Heterogeneous Causal Learning for Optimizing Aggregated Functions in User Growth
User growth is a major strategy for consumer internet companies. To optimize costly marketing campaigns and maximize user engagement, we propose a novel treatment effect optimization methodology to enhance user growth marketing. By leveraging deep learning, our algorithm learns from past experiments to optimize user selection and reward allocation, maximizing campaign impact while minimizing costs. Unlike traditional prediction methods, our model directly models uplifts in key business metrics.…

From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification
Comte Valentin, Gemma Piella, Mario Ceresa, Miguel A. Gonzalez Ballester
https://arxiv.org/abs/2507.05783

From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification
Cardiac diseases are among the leading causes of morbidity and mortality worldwide, which requires accurate and timely diagnostic strategies. In this study, we introduce an innovative approach that combines deep learning image registration with physics-informed regularization to predict the biomechanical properties of moving cardiac tissues and extract features for disease classification. We utilize the energy strain formulation of Neo-Hookean material to model cardiac tissue deformations, opti…

Explainable AI in Deep Learning-Based Prediction of Solar Storms
Adam O. Rawashdeh, Jason T. L. Wang, Katherine G. Herbert
https://arxiv.org/abs/2508.16543 https://

Explainable AI in Deep Learning-Based Prediction of Solar Storms
A deep learning model is often considered a black-box model, as its internal workings tend to be opaque to the user. Because of the lack of transparency, it is challenging to understand the reasoning behind the model's predictions. Here, we present an approach to making a deep learning-based solar storm prediction model interpretable, where solar storms include solar flares and coronal mass ejections (CMEs). This deep learning model, built based on a long short-term memory (LSTM) network with a…