Tootfinder

livemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…

Deep learning the sources of MJO predictability: a spectral view of learned features
Lin Yao, Da Yang, James P. C. Duncan, Ashesh Chattopadhyay, Pedram Hassanzadeh, Wahid Bhimji, Bin Yu
https://arxiv.org/abs/2510.03582

Deep learning the sources of MJO predictability: a spectral view of learned features
The Madden-Julian oscillation (MJO) is a planetary-scale, intraseasonal tropical rainfall phenomenon crucial for global weather and climate; however, its dynamics and predictability remain poorly understood. Here, we leverage deep learning (DL) to investigate the sources of MJO predictability, motivated by a central difference in MJO theories: which spatial scales are essential for driving the MJO? We first develop a deep convolutional neural network (DCNN) to forecast the MJO indices (RMM and …

Toward Co-adapting Machine Learning Job Shape and Cluster Topology
Shawn Shuoshuo Chen, Daiyaan Arfeen, Minlan Yu, Peter Steenkiste, Srinivasan Seshan
https://arxiv.org/abs/2510.03891

Toward Co-adapting Machine Learning Job Shape and Cluster Topology
Allocating resources to distributed machine learning jobs in multi-tenant torus-topology clusters must meet each job's specific placement and communication requirements, which are typically described using shapes. There is an inherent tension between minimizing network contention and maximizing cluster utilization when placing various-shaped jobs. While existing schedulers typically optimize for one objective at the expense of the other, we demonstrate that both can be achieved simultaneously. …

Fair Minimum Labeling: Efficient Temporal Network Activations for Reachability and Equity
Lutz Oettershagen, Othon Michail
https://arxiv.org/abs/2510.03899 https://

Fair Minimum Labeling: Efficient Temporal Network Activations for Reachability and Equity
Balancing resource efficiency and fairness is critical in networked systems that support modern learning applications. We introduce the Fair Minimum Labeling (FML) problem: the task of designing a minimum-cost temporal edge activation plan that ensures each group of nodes in a network has sufficient access to a designated target set, according to specified coverage requirements. FML captures key trade-offs in systems where edge activations incur resource costs and equitable access is essential,…

livemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…

We’ve published new research from the EU co-funded project NGSOTI: “Learning from large-scale IPv4 blackhole: Behavioral analysis of SNMP traffic”.
Over a 12-month period (Nov 2024–Oct 2025), our network telescope captured ~634 million unsolicited SNMP queries from more than 153,000 unique IPv4 sources scanning an unused /18 block.
The origins of the traffic are globally distributed, with notable concentrations from Indonesia, China, the United States, Germany, Chile and others, …

💫 Fast frequency reconstruction using Deep Learning for event recognition in ring laser data
#laser

Fast frequency reconstruction using Deep Learning for event recognition in ring laser data
The reconstruction of a frequency with minimal delay from a sinusoidal signal is a common task in several fields; for example Ring Laser Gyroscopes, since their output signal is a beat frequency. While conventional methods require several seconds of data, we present a neural network approach capable of reconstructing frequencies of several hundred Hertz within approximately 10 milliseconds. This enables rapid trigger generation. The method outperforms standard Fourier-based techniques, improvin…

Thank you all for 3,000 followers on here! Here’s a photo of Danny to celebrate the occasion ☺️
3 years into being part of Mastodon, I continue to be impressed with how wonderful the people here are and how much this social network actually FEELS social. People replying to one another, having conversations, learning things, sharing moments of joy, making friends.
Mastodon brought my business clients, helped me gain confidence in my own voice, freed me from dependence on big tech and algorithms, rekindled my interests, introduced me to incredible people and projects, and served as a source of hope in humanity in the times when cynicism and nihilism felt all but inevitable.
I love our little corner of the internet, and am so glad that it’s still here despite everyone who professed it was doomed to fade into irrelevance.
Thank you to everyone reading these words for being here on the Fedi. The world is a little better thanks to your choice to support an independent web.

I need to read it properly, but this looks 🔥 https://arxiv.org/abs/2511.16652

Evolution Strategies at the Hyperscale
We introduce Evolution Guided General Optimization via Low-rank Learning (EGGROLL), an evolution strategies (ES) algorithm designed to scale backprop-free optimization to large population sizes for modern large neural network architectures with billions of parameters. ES is a set of powerful blackbox optimisation methods that can handle non-differentiable or noisy objectives with excellent scaling potential through parallelisation. Na{ï}ve ES becomes prohibitively expensive at scale due to the…

livemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…

Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network
Vu Duc Anh Nguyen, Ziyue Li
https://arxiv.org/abs/2510.09350 https://…

Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network
The operational efficiency of railway networks, a cornerstone of modern economies, is persistently undermined by the cascading effects of train delays. Accurately forecasting this delay propagation is a critical challenge for real-time traffic management. While recent research has leveraged Graph Neural Networks (GNNs) to model the network structure of railways, a significant gap remains in developing frameworks that provide multi-step autoregressive forecasts at a network-wide scale, while sim…

livemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…

Attack-Specialized Deep Learning with Ensemble Fusion for Network Anomaly Detection
Nisith Dissanayake (University of Moratuwa), Uthayasanker Thayasivam (University of Moratuwa)
https://arxiv.org/abs/2510.12455

Attack-Specialized Deep Learning with Ensemble Fusion for Network Anomaly Detection
The growing scale and sophistication of cyberattacks pose critical challenges to network security, particularly in detecting diverse intrusion types within imbalanced datasets. Traditional intrusion detection systems (IDS) often struggle to maintain high accuracy across both frequent and rare attacks, leading to increased false negatives for minority classes. To address this, we propose a hybrid anomaly detection framework that integrates specialized deep learning models with an ensemble meta-c…

Efficient tensor-network simulations of weakly-measured quantum circuits
Darren Pereira, Leonardo Banchi
https://arxiv.org/abs/2510.07211 https://arxiv.org…

Efficient tensor-network simulations of weakly-measured quantum circuits
We present a tensor-network-based method for simulating a weakly-measured quantum circuit. In particular, we use a Markov chain to efficiently sample measurements and contract the tensor network, propagating their effect forward along the spatial direction. Applications of our algorithm include validating quantum computers (capable of mid-circuit measurements) in regimes of easy classical simulability, and studying generative-machine-learning applications, where sampling from complex stochastic…

Physics-Informed High-order Graph Dynamics Identification Learning for Predicting Complex Networks Long-term Dynamics
Bicheng Wang, Jinping Wang, Yibo Sue
https://arxiv.org/abs/2510.09082

Physics-Informed High-order Graph Dynamics Identification Learning for Predicting Complex Networks Long-term Dynamics
Learning complex network dynamics is fundamental to understanding, modelling and controlling real-world complex systems. There are two main problems in the task of predicting the dynamic evolution of complex networks: on the one hand, existing methods usually use simple graphs to describe the relationships in complex networks; however, this approach can only capture pairwise relationships, while there may be rich non-pairwise structured relationships in the network. First-order GNNs have diffic…

Biology-driven assessment of deep learning super-resolution imaging of the porosity network in dentin
Lauren Anderson, Lucas Chatelain, Nicolas Tremblay, Kathryn Grandfield, David Rousseau, Aur\'elien Gourrier
https://arxiv.org/abs/2510.08407

Biology-driven assessment of deep learning super-resolution imaging of the porosity network in dentin
The mechanosensory system of teeth is currently believed to partly rely on Odontoblast cells stimulation by fluid flow through a porosity network extending through dentin. Visualizing the smallest sub-microscopic porosity vessels therefore requires the highest achievable resolution from confocal fluorescence microscopy, the current gold standard. This considerably limits the extent of the field of view to very small sample regions. To overcome this limitation, we tested different deep learning …

Zero-Shot Forecasting of Network Dynamics through Weight Flow Matching
Shihe Zhou, Ruikun Li, Huandong Wang, Yong Li
https://arxiv.org/abs/2510.07957 https://

Zero-Shot Forecasting of Network Dynamics through Weight Flow Matching
Forecasting state evolution of network systems, such as the spread of information on social networks, is significant for effective policy interventions and resource management. However, the underlying propagation dynamics constantly shift with new topics or events, which are modeled as changing coefficients of the underlying dynamics. Deep learning models struggle to adapt to these out-of-distribution shifts without extensive new data and retraining. To address this, we present Zero-Shot Foreca…

deep-REMAP: Probabilistic Parameterization of Stellar Spectra Using Regularized Multi-Task Learning
Sankalp Gilda
https://arxiv.org/abs/2510.09362 https://…

deep-REMAP: Probabilistic Parameterization of Stellar Spectra Using Regularized Multi-Task Learning
In the era of exploding survey volumes, traditional methods of spectroscopic analysis are being pushed to their limits. In response, we develop deep-REMAP, a novel deep learning framework that utilizes a regularized, multi-task approach to predict stellar atmospheric parameters from observed spectra. We train a deep convolutional neural network on the PHOENIX synthetic spectral library and use transfer learning to fine-tune the model on a small subset of observed FGK dwarf spectra from the MARV…

A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Services
Vincenzo Norman Vitale, Antonia Maria Tulino, Andreas F. Molisch, Jaime Llorca
https://arxiv.org/abs/2510.11535

A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Services
Timely delivery of delay-sensitive information over dynamic, heterogeneous networks is increasingly essential for a range of interactive applications, such as industrial automation, self-driving vehicles, and augmented reality. However, most existing network control solutions target only average delay performance, falling short of providing strict End-to-End (E2E) peak latency guarantees. This paper addresses the challenge of reliably delivering packets within application-imposed deadlines by l…

Over 1,300 people from 130 countries just spent a month mastering climate action together.
The En-ROADS Climate Ambassador Camp brought participants through 13,800 hours of training on the Climate Solutions Simulator—diving into climate dynamics, testing solutions, and roleplaying real-world scenarios.
Now they're leading climate events in their communities and working toward certification.

En-ROADS Climate Ambassador Camp 2025: A Month of Learning and Leadership
This year’s live training equipped a global network of climate leaders with the tools and confidence to drive change.

"All in all, these machine learning models are so good that they’ve almost completely supplanted traditional methods for detecting and phase-picking earthquakes, especially for smaller magnitudes."
"In the last five years, an AI-based workflow has almost completely replaced one of the fundamental tasks in seismology for the better."
(It's not LLMs)

“Like putting on glasses for the first time”—how AI improves earthquake detection
AI is “comically good” at detecting small earthquakes—here’s why that matters.

livemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…

Bilevel optimization for learning hyperparameters: Application to solving PDEs and inverse problems with Gaussian processes
Nicholas H. Nelsen, Houman Owhadi, Andrew M. Stuart, Xianjin Yang, Zongren Zou
https://arxiv.org/abs/2510.05568

Bilevel optimization for learning hyperparameters: Application to solving PDEs and inverse problems with Gaussian processes
Methods for solving scientific computing and inference problems, such as kernel- and neural network-based approaches for partial differential equations (PDEs), inverse problems, and supervised learning tasks, depend crucially on the choice of hyperparameters. Specifically, the efficacy of such methods, and in particular their accuracy, stability, and generalization properties, strongly depends on the choice of hyperparameters. While bilevel optimization offers a principled framework for hyperpa…

Pretraining in Actor-Critic Reinforcement Learning for Robot Motion Control
Jiale Fan, Andrei Cramariuc, Tifanny Portela, Marco Hutter
https://arxiv.org/abs/2510.12363 https://

Pretraining in Actor-Critic Reinforcement Learning for Robot Motion Control
The pretraining-finetuning paradigm has facilitated numerous transformative advancements in artificial intelligence research in recent years. However, in the domain of reinforcement learning (RL) for robot motion control, individual skills are often learned from scratch despite the high likelihood that some generalizable knowledge is shared across all task-specific policies belonging to a single robot embodiment. This work aims to define a paradigm for pretraining neural network models that enc…

MS-Mix: Unveiling the Power of Mixup for Multimodal Sentiment Analysis
Hongyu Zhu, Lin Chen, Mounim A. El-Yacoubi, Mingsheng Shang
https://arxiv.org/abs/2510.11579 https://

MS-Mix: Unveiling the Power of Mixup for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) aims to identify and interpret human emotions by integrating information from heterogeneous data sources such as text, video, and audio. While deep learning models have advanced in network architecture design, they remain heavily limited by scarce multimodal annotated data. Although Mixup-based augmentation improves generalization in unimodal tasks, its direct application to MSA introduces critical challenges: random mixing often amplifies label ambiguity and…

Structured Cooperative Multi-Agent Reinforcement Learning: a Bayesian Network Perspective
Shahbaz P Qadri Syed, He Bai
https://arxiv.org/abs/2510.09937 https://

Structured Cooperative Multi-Agent Reinforcement Learning: a Bayesian Network Perspective
The empirical success of multi-agent reinforcement learning (MARL) has motivated the search for more efficient and scalable algorithms for large scale multi-agent systems. However, existing state-of-the-art algorithms do not fully exploit inter-agent coupling information to develop MARL algorithms. In this paper, we propose a systematic approach to leverage structures in the inter-agent couplings for efficient model-free reinforcement learning. We model the cooperative MARL problem via a Bayesi…

SS-DPPN: A self-supervised dual-path foundation model for the generalizable cardiac audio representation
Ummy Maria Muna, Md Mehedi Hasan Shawon, Md Jobayer, Sumaiya Akter, Md Rakibul Hasan, Md. Golam Rabiul Alam
https://arxiv.org/abs/2510.10719

SS-DPPN: A self-supervised dual-path foundation model for the generalizable cardiac audio representation
The automated analysis of phonocardiograms is vital for the early diagnosis of cardiovascular disease, yet supervised deep learning is often constrained by the scarcity of expert-annotated data. In this paper, we propose the Self-Supervised Dual-Path Prototypical Network (SS-DPPN), a foundation model for cardiac audio representation and classification from unlabeled data. The framework introduces a dual-path contrastive learning based architecture that simultaneously processes 1D waveforms and …

Active Control of Turbulent Airfoil Flows Using Adjoint-based Deep Learning
Xuemin Liu, Tom Hickling, Jonathan F. MacArt
https://arxiv.org/abs/2510.07106 https://

Active Control of Turbulent Airfoil Flows Using Adjoint-based Deep Learning
We train active neural-network flow controllers using a deep learning PDE augmentation method to optimize lift-to-drag ratios in turbulent airfoil flows at Reynolds number $5\times10^4$ and Mach number 0.4. Direct numerical simulation and large eddy simulation are employed to model compressible, unconfined flow over two- and three-dimensional semi-infinite NACA 0012 airfoils at angles of attack $α= 5^\circ$, $10^\circ$, and $15^\circ$. Control actions, implemented through a blowing/suction jet…

Channel-Aware Deep Learning for Superimposed Pilot Power Allocation and Receiver Design
Run Gu, Renjie Xie, Wei Xu, Zhaohui Yang, Kaibin Huang
https://arxiv.org/abs/2510.11294 h…

Channel-Aware Deep Learning for Superimposed Pilot Power Allocation and Receiver Design
Superimposed pilot (SIP) schemes face significant challenges in effectively superimposing and separating pilot and data signals, especially in multiuser mobility scenarios with rapidly varying channels. To address these challenges, we propose a novel channel-aware learning framework for SIP schemes, termed CaSIP, that jointly optimizes pilot-data power (PDP) allocation and a receiver network for pilot-data interference (PDI) elimination, by leveraging channel path gain information, a form of la…

Learning Operators through Coefficient Mappings in Fixed Basis Spaces
Chuqi Chen, Yang Xiang, Weihong Zhang
https://arxiv.org/abs/2510.10350 https://arxiv.…

Learning Operators through Coefficient Mappings in Fixed Basis Spaces
Operator learning has emerged as a powerful paradigm for approximating solution operators of partial differential equations (PDEs) and other functional mappings. \textcolor{red}{}{Classical approaches} typically adopt a pointwise-to-pointwise framework, where input functions are sampled at prescribed locations and mapped directly to solution values. We propose the Fixed-Basis Coefficient to Coefficient Operator Network (FB-C2CNet), which learns operators in the coefficient space induced by pres…

Curriculum Learning with Synthetic Data for Enhanced Pulmonary Nodule Detection in Chest Radiographs
Pranav Sambhu, Om Guin, Madhav Sambhu, Jinho Cha
https://arxiv.org/abs/2510.07681

Curriculum Learning with Synthetic Data for Enhanced Pulmonary Nodule Detection in Chest Radiographs
This study evaluates whether integrating curriculum learning with diffusion-based synthetic augmentation can enhance the detection of difficult pulmonary nodules in chest radiographs, particularly those with low size, brightness, and contrast, which often challenge conventional AI models due to data imbalance and limited annotation. A Faster R-CNN with a Feature Pyramid Network (FPN) backbone was trained on a hybrid dataset comprising expert-labeled NODE21 (1,213 patients; 52.4 percent male; me…

Hardware-Efficient CNNs: Interleaved Approximate FP32 Multipliers for Kernel Computation
Bindu G Gowda (International Institute of Information Technology Bangalore), Yogesh Goyal (International Institute of Information Technology Bangalore), Yash Gupta (International Institute of Information Technology Bangalore), Madhav Rao (International Institute of Information Technology Bangalore)

Hardware-Efficient CNNs: Interleaved Approximate FP32 Multipliers for Kernel Computation
Single-precision floating point (FP32) data format, defined by the IEEE 754 standard, is widely employed in scientific computing, signal processing, and deep learning training, where precision is critical. However, FP32 multiplication is computationally expensive and requires complex hardware, especially for precisely handling mantissa multiplication. In practical applications like neural network inference, perfect accuracy is not always necessary, minor multiplication errors often have little …

Residual-Informed Learning of Solutions to Algebraic Loops
Felix Brandt, Andreas Heuermann, Philip Hannebohm, Bernhard Bachmann
https://arxiv.org/abs/2510.09317 https://

Residual-Informed Learning of Solutions to Algebraic Loops
This paper presents a residual-informed machine learning approach for replacing algebraic loops in equation-based Modelica models with neural network surrogates. A feedforward neural network is trained using the residual (error) of the algebraic loop directly in its loss function, eliminating the need for a supervised dataset. This training strategy also resolves the issue of ambiguous solutions, allowing the surrogate to converge to a consistent solution rather than averaging multiple valid on…

MIARec: Mutual-influence-aware Heterogeneous Network Embedding for Scientific Paper Recommendation
Wenjin Xie, Tao Jia
https://arxiv.org/abs/2510.12054 https://

MIARec: Mutual-influence-aware Heterogeneous Network Embedding for Scientific Paper Recommendation
With the rapid expansion of scientific literature, scholars increasingly demand precise and high-quality paper recommendations. Among various recommendation methodologies, graph-based approaches have garnered attention by effectively exploiting the structural characteristics inherent in scholarly networks. However, these methods often overlook the asymmetric academic influence that is prevalent in scholarly networks when learning graph representations. To address this limitation, this study pro…

A Diffusion-based Generative Machine Learning Paradigm for Contingency Screening
Quan Tran, Suresh S. Muknahallipatna, Dongliang Duan, Nga Nguyen
https://arxiv.org/abs/2510.04470

A Diffusion-based Generative Machine Learning Paradigm for Contingency Screening
Contingency screening is a crucial part of electric power systems all the time. Power systems frequently encounter multiple challenging operational dilemmas that could lead to the instability of power systems. Contingency analysis is effort-consuming by utilizing traditional numerical analysis methods. It is commonly addressed by generating a whopping number of possible contingencies or manipulating network parameters to determine the worst scenarios. This paper proposes a novel approach that d…

Replaced article(s) found for physics.optics. https://arxiv.org/list/physics.optics/new
[1/1]:
- LLM4Laser: Large Language Models Automate the Design of Lasers
Renjie Li, Ceyao Zhang, Sixuan Mao, Xiyuan Zhou, Feng Yin, Sergios Theodoridis, Zhaoyu Zhang
https://arxiv.org/abs/2104.12145
- Room-temperature valley-selective emission in Si-MoSe2 heterostructures enabled by high-quality-f...
Feng Pan, et al.
https://arxiv.org/abs/2409.09806 https://mastoxiv.page/@arXiv_physicsoptics_bot/113152185040115763
- 1T'-MoTe$_2$ as an integrated saturable absorber for photonic machine learning
Maria Carolina Volpato, Henrique G. Rosa, Tom Reep, Pierre-Louis de Assis, Newton Cesario Frateschi
https://arxiv.org/abs/2507.16140 https://mastoxiv.page/@arXiv_physicsoptics_bot/114901571498004090
- NeOTF: Guidestar-free neural representation for broadband dynamic imaging through scattering
Yunong Sun, Fei Xia
https://arxiv.org/abs/2507.22328 https://mastoxiv.page/@arXiv_physicsoptics_bot/114947052118796753
- Structured Random Models for Phase Retrieval with Optical Diffusers
Zhiyuan Hu, Fakhriyya Mammadova, Juli\'an Tachella, Michael Unser, Jonathan Dong
https://arxiv.org/abs/2510.14490 https://mastoxiv.page/@arXiv_physicsoptics_bot/115388901264416806
- Memory Effects in Time-Modulated Radiative Heat Transfer
Riccardo Messina, Philippe Ben-Abdallah
https://arxiv.org/abs/2510.19378 https://mastoxiv.page/@arXiv_physicsoptics_bot/115422659227231796
- Mie-tronics supermodes and symmetry breaking in nonlocal metasurfaces
Thanh Xuan Hoang, Ayan Nussupbekov, Jie Ji, Daniel Leykam, Jaime Gomez Rivas, Yuri Kivshar
https://arxiv.org/abs/2511.03560 https://mastoxiv.page/@arXiv_physicsoptics_bot/115502066008543828
- Integrated soliton microcombs beyond the turnkey limit
Wang, Xu, Wang, Zhu, Luo, Luo, Wang, Ni, Yang, Gong, Xiao, Li, Yang
https://arxiv.org/abs/2511.06909 https://mastoxiv.page/@arXiv_physicsoptics_bot/115530791701071777
- Ising accelerator with a reconfigurable interferometric photonic processor
Rausell-Campo, Al Kayed, P\'erez-L\'ppez, Aadhi, Shastri, Francoy
https://arxiv.org/abs/2511.13284 https://mastoxiv.page/@arXiv_physicsoptics_bot/115570439939074488
- Superradiance in dense atomic samples
I. M. de Ara\'ujo, H. Sanchez, L. F. Alves da Silva, M. H. Y. Moussa
https://arxiv.org/abs/2504.20242 https://mastoxiv.page/@arXiv_quantph_bot/114425762810828336
- Fluctuation-induced Hall-like lateral forces in a chiral-gain environment
Daigo Oue, M\'ario G. Silveirinha
https://arxiv.org/abs/2507.14754 https://mastoxiv.page/@arXiv_condmatmeshall_bot/114896308178114535
- Tensor-network approach to quantum optical state evolution beyond the Fock basis
Nikolay Kapridov, Egor Tiunov, Dmitry Chermoshentsev
https://arxiv.org/abs/2511.15295 https://mastoxiv.page/@arXiv_quantph_bot/115581390666689204
- OmniLens : Blind Lens Aberration Correction via Large LensLib Pre-Training and Latent PSF Repres...
Jiang, Qian, Gao, Sun, Yang, Yi, Li, Yang, Van Gool, Wang
https://arxiv.org/abs/2511.17126 https://mastoxiv.page/@arXiv_eessIV_bot/115603729319581340
toXiv_bot_toot

Learning Polynomial Activation Functions for Deep Neural Networks
Linghao Zhang, Jiawang Nie, Tingting Tang
https://arxiv.org/abs/2510.03682 https://arxiv.…

Learning Polynomial Activation Functions for Deep Neural Networks
Activation functions are crucial for deep neural networks. This novel work frames the problem of training neural network with learnable polynomial activation functions as a polynomial optimization problem, which is solvable by the Moment-SOS hierarchy. This work represents a fundamental departure from the conventional paradigm of training deep neural networks, which relies on local optimization methods like backpropagation and gradient descent. Numerical experiments are presented to demonstrate…

N-Parties Private Structure and Parameter Learning for Sum-Product Networks
Xenia Heilmann, Ernst Althaus, Mattia Cerrato, Nick Johannes Peter Rassau, Mohammad Sadeq Dousti, Stefan Kramer
https://arxiv.org/abs/2510.05946

N-Parties Private Structure and Parameter Learning for Sum-Product Networks
A sum-product network (SPN) is a graphical model that allows several types of probabilistic inference to be performed efficiently. In this paper, we propose a privacy-preserving protocol which tackles structure generation and parameter learning of SPNs. Additionally, we provide a protocol for private inference on SPNs, subsequent to training. To preserve the privacy of the participants, we derive our protocol based on secret sharing, which guarantees privacy in the honest-but-curious setting ev…

Comparative Evaluation of Neural Network Architectures for Generalizable Human Spatial Preference Prediction in Unseen Built Environments
Maral Doctorarastoo, Katherine A. Flanigan, Mario Berg\'es, Christopher McComb
https://arxiv.org/abs/2510.10954

Comparative Evaluation of Neural Network Architectures for Generalizable Human Spatial Preference Prediction in Unseen Built Environments
The capacity to predict human spatial preferences within built environments is instrumental for developing Cyber-Physical-Social Infrastructure Systems (CPSIS). A significant challenge in this domain is the generalizability of preference models, particularly their efficacy in predicting preferences within environmental configurations not encountered during training. While deep learning models have shown promise in learning complex spatial and contextual dependencies, it remains unclear which ne…

TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction
Nathalie Soybelman, Francesco A. Di Bello, Nilotpal Kakati, Eilam Gross
https://arxiv.org/abs/2510.08162

TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction
Event reconstruction at the LHC, the task of assigning observed physics objects to their true origins, is a central challenge for precision measurements and searches. Many existing machine learning approaches address this problem but rely on a single event topology, restricting their applicability to realistic analyses where multiple signal and background processes with different structures are present. To overcome this, we present TIGER, a novel hierarchical graph network that is fundamentally…

Replaced article(s) found for cs.GT. https://arxiv.org/list/cs.GT/new
[1/1]:
- Cumulative Games: Who is the current player?
Urban Larsson, Reshef Meir, Yair Zick
https://arxiv.org/abs/2005.06326
- Contest Design with Threshold Objectives
Edith Elkind, Abheek Ghosh, Paul W. Goldberg
https://arxiv.org/abs/2109.03179
- Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications
V. Udaya Sankar, Vishisht Srihari Rao, Y. Narahari
https://arxiv.org/abs/2401.05683 https://mastoxiv.page/@arXiv_csGT_bot/111741115483021453
- Charting the Shapes of Stories with Game Theory
Daskalakis, Gemp, Jiang, Leme, Papadimitriou, Piliouras
https://arxiv.org/abs/2412.05747 https://mastoxiv.page/@arXiv_csGT_bot/113627246220336424
- Computing Evolutionarily Stable Strategies in Multiplayer Games
Sam Ganzfried
https://arxiv.org/abs/2511.20859 https://mastoxiv.page/@arXiv_csGT_bot/115620508246637361
- Autodeleveraging: Impossibilities and Optimization
Tarun Chitra
https://arxiv.org/abs/2512.01112 https://mastoxiv.page/@arXiv_csGT_bot/115649040881525135
- Static Pricing Guarantees for Queueing Systems
Jacob Bergquist, Adam N. Elmachtoub
https://arxiv.org/abs/2305.09168 https://mastoxiv.page/@arXiv_csDS_bot/110382625621173269
- Game of arrivals at a two queue network with heterogeneous customer routes
Agniv Bandyopadhyay, Sandeep Juneja
https://arxiv.org/abs/2310.18149 https://mastoxiv.page/@arXiv_csPF_bot/111322112226936579
- Characterization of Priority-Neutral Matching Lattices
Clayton Thomas
https://arxiv.org/abs/2404.02142 https://mastoxiv.page/@arXiv_econTH_bot/112205968984928881
- Seven kinds of equivalent models for generalized coalition logics
Zixuan Chen, Fengkui Ju
https://arxiv.org/abs/2501.05466 https://mastoxiv.page/@arXiv_csLO_bot/113819715349259373
- Matching Markets Meet LLMs: Algorithmic Reasoning with Ranked Preferences
Hadi Hosseini, Samarth Khanna, Ronak Singh
https://arxiv.org/abs/2506.04478 https://mastoxiv.page/@arXiv_csAI_bot/114635186215388479
toXiv_bot_toot

A Universal Deep Learning Force Field for Molecular Dynamic Simulation and Vibrational Spectra Prediction
Shengjiao Ji, Yujin Zhang, Zihan Zou, Bin Jiang, Jun Jiang, Yi Luo, Wei Hu
https://arxiv.org/abs/2510.04227

A Universal Deep Learning Force Field for Molecular Dynamic Simulation and Vibrational Spectra Prediction
Accurate and efficient simulation of infrared (IR) and Raman spectra is essential for molecular identification and structural analysis. Traditional quantum chemistry methods based on the harmonic approximation neglect anharmonicity and nuclear quantum effects, while ab initio molecular dynamics (AIMD) remains computationally expensive. Here, we integrate our deep equivariant tensor attention network (DetaNet) with a velocity-Verlet integrator to enable fast and accurate machine learning molecul…

livemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…

Universal super-resolution framework for imaging of quantum dots
Dominik Va\v{s}inka, Jaewon Lee, Charlie Stalker, Victor Mitryakhin, Ivan Solovev, Sven Stephan, Sven H\"ofling, Falk Eilenberger, Seth Ariel Tongay, Christian Schneider, Miroslav Je\v{z}ek, Ana Predojevi\'c
https://arxiv.org/abs/2510.06076

Universal super-resolution framework for imaging of quantum dots
We present a universal deep-learning method that reconstructs super-resolved images of quantum emitters from a single camera frame measurement. Trained on physics-based synthetic data spanning diverse point-spread functions, aberrations, and noise, the network generalizes across experimental conditions without system-specific retraining. We validate the approach on low- and high-density In(Ga)As quantum dots and strain-induced dots in 2D monolayer WSe$_2$, resolving overlapping emitters even un…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Look-Ahead Reasoning on Learning Platforms
Haiqing Zhu, Tijana Zrnic, Celestine Mendler-D\"unner
https://arxiv.org/abs/2511.14745 https://mastoxiv.page/@arXiv_csLG_bot/115575981129228810
- Deep Gaussian Process Proximal Policy Optimization
Matthijs van der Lende, Juan Cardenas-Cartagena
https://arxiv.org/abs/2511.18214 https://mastoxiv.page/@arXiv_csLG_bot/115610315210502140
- Spectral Concentration at the Edge of Stability: Information Geometry of Kernel Associative Memory
Akira Tamamori
https://arxiv.org/abs/2511.23083 https://mastoxiv.page/@arXiv_csLG_bot/115644325602130493
- xGR: Efficient Generative Recommendation Serving at Scale
Sun, Liu, Zhang, Wu, Yang, Liang, Li, Ma, Liang, Ren, Zhang, Liu, Zhang, Qian, Yang
https://arxiv.org/abs/2512.11529 https://mastoxiv.page/@arXiv_csLG_bot/115723008170311172
- Credit Risk Estimation with Non-Financial Features: Evidence from a Synthetic Istanbul Dataset
Atalay Denknalbant, Emre Sezdi, Zeki Furkan Kutlu, Polat Goktas
https://arxiv.org/abs/2512.12783 https://mastoxiv.page/@arXiv_csLG_bot/115729287232895097
- The Semantic Illusion: Certified Limits of Embedding-Based Hallucination Detection in RAG Systems
Debu Sinha
https://arxiv.org/abs/2512.15068 https://mastoxiv.page/@arXiv_csLG_bot/115740048142898391
- Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library
Stritzel, H\"uhnerbein, Rauch, Zarate, Fleischmann, Buck, Lischka, Frey
https://arxiv.org/abs/2512.16715 https://mastoxiv.page/@arXiv_csLG_bot/115745910810427061
- Differentially private Bayesian tests
Abhisek Chakraborty, Saptati Datta
https://arxiv.org/abs/2401.15502 https://mastoxiv.page/@arXiv_statML_bot/111843467510507382
- SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Reda Alami, Alexey Naumov, Eric Moulines
https://arxiv.org/abs/2402.04114
- Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk
https://arxiv.org/abs/2408.07588 https://mastoxiv.page/@arXiv_statML_bot/112965266196097314
- Non-Perturbative Trivializing Flows for Lattice Gauge Theories
Mathis Gerdes, Pim de Haan, Roberto Bondesan, Miranda C. N. Cheng
https://arxiv.org/abs/2410.13161 https://mastoxiv.page/@arXiv_heplat_bot/113327593338897860
- Dynamic PET Image Prediction Using a Network Combining Reversible and Irreversible Modules
Sun, Zhang, Xia, Sun, Chen, Yang, Liu, Zhu, Liu
https://arxiv.org/abs/2410.22674 https://mastoxiv.page/@arXiv_eessIV_bot/113401026110345647
- Targeted Learning for Variable Importance
Xiaohan Wang, Yunzhe Zhou, Giles Hooker
https://arxiv.org/abs/2411.02221 https://mastoxiv.page/@arXiv_statML_bot/113429912435819479
- Refined Analysis of Federated Averaging and Federated Richardson-Romberg
Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines
https://arxiv.org/abs/2412.01389 https://mastoxiv.page/@arXiv_statML_bot/113588027268311334
- Embedding-Driven Data Distillation for 360-Degree IQA With Residual-Aware Refinement
Abderrezzaq Sendjasni, Seif-Eddine Benkabou, Mohamed-Chaker Larabi
https://arxiv.org/abs/2412.12667 https://mastoxiv.page/@arXiv_csCV_bot/113672538318570349
- 3D Cell Oversegmentation Correction via Geo-Wasserstein Divergence
Peter Chen, Bryan Chang, Olivia A Creasey, Julie Beth Sneddon, Zev J Gartner, Yining Liu
https://arxiv.org/abs/2502.01890 https://mastoxiv.page/@arXiv_csCV_bot/113949981686723660
- DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement Learning Agents
Shashank Sharma, Janina Hoffmann, Vinay Namboodiri
https://arxiv.org/abs/2502.01956 https://mastoxiv.page/@arXiv_csRO_bot/113949997485625086
- Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
Diana Koldasbayeva, Alexey Zaytsev
https://arxiv.org/abs/2502.03480
- GraphCompNet: A Position-Aware Model for Predicting and Compensating Shape Deviations in 3D Printing
Juheon Lee (Rachel), Lei (Rachel), Chen, Juan Carlos Catana, Hui Wang, Jun Zeng
https://arxiv.org/abs/2502.09652 https://mastoxiv.page/@arXiv_csCV_bot/114017924551186136
- LookAhead Tuning: Safer Language Models via Partial Answer Previews
Liu, Wang, Luo, Yuan, Sun, Liang, Zhang, Zhou, Hooi, Deng
https://arxiv.org/abs/2503.19041 https://mastoxiv.page/@arXiv_csCL_bot/114227502448008352
- Constraint-based causal discovery with tiered background knowledge and latent variables in single...
Christine W. Bang, Vanessa Didelez
https://arxiv.org/abs/2503.21526 https://mastoxiv.page/@arXiv_statML_bot/114238919468512990
toXiv_bot_toot

Identifying non-equilibrium fluctuations in Intracellular Motion Using Recurrent Neural Networks
Tomas Basile, Natascha Leijnse, Malte Slot Lauridsen, Younes Farhangi Barooji, Amin Doostmohammadi, Karel Proesmans
https://arxiv.org/abs/2510.04485

Identifying non-equilibrium fluctuations in Intracellular Motion Using Recurrent Neural Networks
Distinguishing active from passive dynamics is a fundamental challenge in understanding the motion of living cells and other active matter systems. Here, we introduce a framework that combines physical modeling, analytical theory, and machine learning to identify and characterize active fluctuations from trajectory data. We train a long short-term memory (LSTM) neural network on synthetic trajectories generated from well-defined stochastic models of active particles, enabling it to classify mot…

livemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…

Spatially-informed transformers: Injecting geostatistical covariance biases into self-attention for spatio-temporal forecasting
Yuri Calleo
https://arxiv.org/abs/2512.17696 https://arxiv.org/pdf/2512.17696 https://arxiv.org/html/2512.17696
arXiv:2512.17696v1 Announce Type: new
Abstract: The modeling of high-dimensional spatio-temporal processes presents a fundamental dichotomy between the probabilistic rigor of classical geostatistics and the flexible, high-capacity representations of deep learning. While Gaussian processes offer theoretical consistency and exact uncertainty quantification, their prohibitive computational scaling renders them impractical for massive sensor networks. Conversely, modern transformer architectures excel at sequence modeling but inherently lack a geometric inductive bias, treating spatial sensors as permutation-invariant tokens without a native understanding of distance. In this work, we propose a spatially-informed transformer, a hybrid architecture that injects a geostatistical inductive bias directly into the self-attention mechanism via a learnable covariance kernel. By formally decomposing the attention structure into a stationary physical prior and a non-stationary data-driven residual, we impose a soft topological constraint that favors spatially proximal interactions while retaining the capacity to model complex dynamics. We demonstrate the phenomenon of ``Deep Variography'', where the network successfully recovers the true spatial decay parameters of the underlying process end-to-end via backpropagation. Extensive experiments on synthetic Gaussian random fields and real-world traffic benchmarks confirm that our method outperforms state-of-the-art graph neural networks. Furthermore, rigorous statistical validation confirms that the proposed method delivers not only superior predictive accuracy but also well-calibrated probabilistic forecasts, effectively bridging the gap between physics-aware modeling and data-driven learning.
toXiv_bot_toot

Network Topology and Information Efficiency of Multi-Agent Systems: Study based on MARL
Xinren Zhang, Sixi Cheng, Zixin Zhong, Jiadong Yu
https://arxiv.org/abs/2510.07888 https:…

Network Topology and Information Efficiency of Multi-Agent Systems: Study based on MARL
Multi-agent systems (MAS) solve complex problems through coordinated autonomous entities with individual decision-making capabilities. While Multi-Agent Reinforcement Learning (MARL) enables these agents to learn intelligent strategies, it faces challenges of non-stationarity and partial observability. Communications among agents offer a solution, but questions remain about its optimal structure and evaluation. This paper explores two underexamined aspects: communication topology and informatio…

livemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…

GNN-enhanced Traffic Anomaly Detection for Next-Generation SDN-Enabled Consumer Electronics
Guan-Yan Yang, Farn Wang, Kuo-Hui Yeh
https://arxiv.org/abs/2510.07109 https://

GNN-enhanced Traffic Anomaly Detection for Next-Generation SDN-Enabled Consumer Electronics
Consumer electronics (CE) connected to the Internet of Things are susceptible to various attacks, including DDoS and web-based threats, which can compromise their functionality and facilitate remote hijacking. These vulnerabilities allow attackers to exploit CE for broader system attacks while enabling the propagation of malicious code across the CE network, resulting in device failures. Existing deep learning-based traffic anomaly detection systems exhibit high accuracy in traditional network …

Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Arthur G. Rattew, Po-Wei Huang, Naixu Guo, Lirand\"e Pira, Patrick Rebentrost
https://arxiv.org/abs/2510.07195

Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Fault-tolerant Quantum Processing Units (QPUs) promise to deliver exponential speed-ups in select computational tasks, yet their integration into modern deep learning pipelines remains unclear. In this work, we take a step towards bridging this gap by presenting the first fully-coherent quantum implementation of a multilayer neural network with non-linear activation functions. Our constructions mirror widely used deep learning architectures based on ResNet, and consist of residual blocks with m…

livemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- The Diffusion Duality
Sahoo, Deschenaux, Gokaslan, Wang, Chiu, Kuleshov
https://arxiv.org/abs/2506.10892 https://mastoxiv.page/@arXiv_csLG_bot/114675526577078472
- Multimodal Representation Learning and Fusion
Jin, Ge, Xie, Luo, Song, Bi, Liang, Guan, Yeong, Song, Hao
https://arxiv.org/abs/2506.20494 https://mastoxiv.page/@arXiv_csLG_bot/114749113025183688
- The kernel of graph indices for vector search
Mariano Tepper, Ted Willke
https://arxiv.org/abs/2506.20584 https://mastoxiv.page/@arXiv_csLG_bot/114749118923266356
- OptScale: Probabilistic Optimality for Inference-time Scaling
Youkang Wang, Jian Wang, Rubing Chen, Xiao-Yong Wei
https://arxiv.org/abs/2506.22376 https://mastoxiv.page/@arXiv_csLG_bot/114771735361664528
- Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods
Fabian Akkerman, Julien Ferry, Christian Artigues, Emmanuel Hebrard, Thibaut Vidal
https://arxiv.org/abs/2507.18242 https://mastoxiv.page/@arXiv_csLG_bot/114913322736512937
- MolMark: Safeguarding Molecular Structures through Learnable Atom-Level Watermarking
Runwen Hu, Peilin Chen, Keyan Ding, Shiqi Wang
https://arxiv.org/abs/2508.17702 https://mastoxiv.page/@arXiv_csLG_bot/115095014405732247
- Dual-Distilled Heterogeneous Federated Learning with Adaptive Margins for Trainable Global Protot...
Fatema Siddika, Md Anwar Hossen, Wensheng Zhang, Anuj Sharma, Juan Pablo Mu\~noz, Ali Jannesari
https://arxiv.org/abs/2508.19009 https://mastoxiv.page/@arXiv_csLG_bot/115100269482762688
- STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
Gary Simethy, Daniel Ortiz-Arroyo, Petar Durdevic
https://arxiv.org/abs/2508.19011 https://mastoxiv.page/@arXiv_csLG_bot/115100270137397046
- EEGDM: Learning EEG Representation with Latent Diffusion Model
Shaocong Wang, Tong Liu, Yihan Li, Ming Li, Kairui Wen, Pei Yang, Wenqi Ji, Minjing Yu, Yong-Jin Liu
https://arxiv.org/abs/2508.20705 https://mastoxiv.page/@arXiv_csLG_bot/115111565155687451
- Data-Free Continual Learning of Server Models in Model-Heterogeneous Cloud-Device Collaboration
Xiao Zhang, Zengzhe Chen, Yuan Yuan, Yifei Zou, Fuzhen Zhuang, Wenyu Jiao, Yuke Wang, Dongxiao Yu
https://arxiv.org/abs/2509.25977 https://mastoxiv.page/@arXiv_csLG_bot/115298721327100391
- Fine-Tuning Masked Diffusion for Provable Self-Correction
Jaeyeon Kim, Seunggeun Kim, Taekyun Lee, David Z. Pan, Hyeji Kim, Sham Kakade, Sitan Chen
https://arxiv.org/abs/2510.01384 https://mastoxiv.page/@arXiv_csLG_bot/115309690976554356
- A Generic Machine Learning Framework for Radio Frequency Fingerprinting
Alex Hiles, Bashar I. Ahmad
https://arxiv.org/abs/2510.09775 https://mastoxiv.page/@arXiv_csLG_bot/115372387779061015
- ASecond-Order SpikingSSM for Wearables
Kartikay Agrawal, Abhijeet Vikram, Vedant Sharma, Vaishnavi Nagabhushana, Ayon Borthakur
https://arxiv.org/abs/2510.14386 https://mastoxiv.page/@arXiv_csLG_bot/115389079527543821
- Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning
Heming Zou, Yixiu Mao, Yun Qu, Qi Wang, Xiangyang Ji
https://arxiv.org/abs/2510.16882 https://mastoxiv.page/@arXiv_csLG_bot/115412243355962887
- Seeing Structural Failure Before it Happens: An Image-Based Physics-Informed Neural Network (PINN...
Omer Jauhar Khan, Sudais Khan, Hafeez Anwar, Shahzeb Khan, Shams Ul Arifeen
https://arxiv.org/abs/2510.23117 https://mastoxiv.page/@arXiv_csLG_bot/115451891042176876
- Training Deep Physics-Informed Kolmogorov-Arnold Networks
Spyros Rigas, Fotios Anagnostopoulos, Michalis Papachristou, Georgios Alexandridis
https://arxiv.org/abs/2510.23501 https://mastoxiv.page/@arXiv_csLG_bot/115451942159737549
- Semi-Supervised Preference Optimization with Limited Feedback
Seonggyun Lee, Sungjun Lim, Seojin Park, Soeun Cheon, Kyungwoo Song
https://arxiv.org/abs/2511.00040 https://mastoxiv.page/@arXiv_csLG_bot/115490555013124989
- Towards Causal Market Simulators
Dennis Thumm, Luis Ontaneda Mijares
https://arxiv.org/abs/2511.04469 https://mastoxiv.page/@arXiv_csLG_bot/115507943827841017
- Incremental Generation is Necessary and Sufficient for Universality in Flow-Based Modelling
Hossein Rouhvarzi, Anastasis Kratsios
https://arxiv.org/abs/2511.09902 https://mastoxiv.page/@arXiv_csLG_bot/115547587245365920
- Optimizing Mixture of Block Attention
Guangxuan Xiao, Junxian Guo, Kasra Mazaheri, Song Han
https://arxiv.org/abs/2511.11571 https://mastoxiv.page/@arXiv_csLG_bot/115564541392410174
- Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs
Shasha Zhou, Mingyu Huang, Jack Cole, Charles Britton, Ming Yin, Jan Wolber, Ke Li
https://arxiv.org/abs/2511.12817 https://mastoxiv.page/@arXiv_csLG_bot/115570877730326947
toXiv_bot_toot

Delay Independent Safe Control with Neural Networks: Positive Lur'e Certificates for Risk Aware Autonomy
Hamidreza Montazeri Hedesh, Milad Siami
https://arxiv.org/abs/2510.06661

Delay Independent Safe Control with Neural Networks: Positive Lur'e Certificates for Risk Aware Autonomy
We present a risk-aware safety certification method for autonomous, learning enabled control systems. Focusing on two realistic risks, state/input delays and interval matrix uncertainty, we model the neural network (NN) controller with local sector bounds and exploit positivity structure to derive linear, delay-independent certificates that guarantee local exponential stability across admissible uncertainties. To benchmark performance, we adopt and implement a state-of-the-art IQC NN verificati…

Automated Evolutionary Optimization for Resource-Efficient Neural Network Training
Ilia Revin, Leon Strelkov, Vadim A. Potemkin, Ivan Kireev, Andrey Savchenko
https://arxiv.org/abs/2510.09566

Automated Evolutionary Optimization for Resource-Efficient Neural Network Training
There are many critical challenges in optimizing neural network models, including distributed computing, compression techniques, and efficient training, regardless of their application to specific tasks. Solving such problems is crucial because the need for scalable and resource-efficient models is increasing. To address these challenges, we have developed a new automated machine learning (AutoML) framework, Parameter Efficient Training with Robust Automation (PETRA). It applies evolutionary op…

Lightweight CNN-Based Wi-Fi Intrusion Detection Using 2D Traffic Representations
Rayed Suhail Ahmad, Rehan Ahmad, Quamar Niyaz
https://arxiv.org/abs/2510.11898 https://

Lightweight CNN-Based Wi-Fi Intrusion Detection Using 2D Traffic Representations
Wi-Fi networks are ubiquitous in both home and enterprise environments, serving as a primary medium for Internet access and forming the backbone of modern IoT ecosystems. However, their inherent vulnerabilities, combined with widespread adoption, create opportunities for malicious actors to gain unauthorized access or compromise sensitive data stored on connected devices. To address these challenges, we propose a deep learning based network intrusion detection system (NIDS) for Wi-Fi environmen…

Power Mechanism: Private Tabular Representation Release for Model Agnostic Consumption
Praneeth Vepakomma, Kaustubh Ponkshe
https://arxiv.org/abs/2510.05581 https://

Power Mechanism: Private Tabular Representation Release for Model Agnostic Consumption
Traditional collaborative learning approaches are based on sharing of model weights between clients and a server. However, there are advantages to resource efficiency through schemes based on sharing of embeddings (activations) created from the data. Several differentially private methods were developed for sharing of weights while such mechanisms do not exist so far for sharing of embeddings. We propose Ours to learn a privacy encoding network in conjunction with a small utility generation net…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/3]:
- Fraud detection in credit card transactions using Quantum-Assisted Restricted Boltzmann Machines
Jo\~ao Marcos Cavalcanti de Albuquerque Neto, Gustavo Castro do Amaral, Guilherme Penello Tempor\~ao
https://arxiv.org/abs/2512.17660 https://mastoxiv.page/@arXiv_quantph_bot/115762703945731580
- Vidarc: Embodied Video Diffusion Model for Closed-loop Control
Feng, Xiang, Mao, Tan, Zhang, Huang, Zheng, Liu, Su, Zhu
https://arxiv.org/abs/2512.17661 https://mastoxiv.page/@arXiv_csRO_bot/115762650859932523
- Imputation Uncertainty in Interpretable Machine Learning Methods
Pegah Golchian, Marvin N. Wright
https://arxiv.org/abs/2512.17689 https://mastoxiv.page/@arXiv_statML_bot/115762577479255577
- Revisiting the Broken Symmetry Phase of Solid Hydrogen: A Neural Network Variational Monte Carlo ...
Shengdu Chai, Chen Lin, Xinyang Dong, Yuqiang Li, Wanli Ouyang, Lei Wang, X. C. Xie
https://arxiv.org/abs/2512.17703 https://mastoxiv.page/@arXiv_condmatstrel_bot/115762481116668454
- Breast Cancer Neoadjuvant Chemotherapy Treatment Response Prediction Using Aligned Longitudinal M...
Rahul Ravi, Ruizhe Li, Tarek Abdelfatah, Stephen Chan, Xin Chen
https://arxiv.org/abs/2512.17759 https://mastoxiv.page/@arXiv_eessIV_bot/115762481771898369
- MedNeXt-v2: Scaling 3D ConvNeXts for Large-Scale Supervised Representation Learning in Medical Im...
Roy, Kirchhoff, Ulrich, Rokuss, Wald, Isensee, Maier-Hein
https://arxiv.org/abs/2512.17774 https://mastoxiv.page/@arXiv_eessIV_bot/115762492258209812
- Domain-Aware Quantum Circuit for QML
Gurinder Singh, Thaddeus Pellegrini, Kenneth M. Merz, Jr
https://arxiv.org/abs/2512.17800 https://mastoxiv.page/@arXiv_quantph_bot/115762723607200478
- Visually Prompted Benchmarks Are Surprisingly Fragile
Feng, Lian, Dunlap, Shu, Wang, Wang, Darrell, Suhr, Kanazawa
https://arxiv.org/abs/2512.17875 https://mastoxiv.page/@arXiv_csCV_bot/115762781936221554
- Learning vertical coordinates via automatic differentiation of a dynamical core
Tim Whittaker, Seth Taylor, Elsa Cardoso-Bihlo, Alejandro Di Luca, Alex Bihlo
https://arxiv.org/abs/2512.17877 https://mastoxiv.page/@arXiv_physicsaoph_bot/115762405092703069
- RadarGen: Automotive Radar Point Cloud Generation from Cameras
Tomer Borreda, Fangqiang Ding, Sanja Fidler, Shengyu Huang, Or Litany
https://arxiv.org/abs/2512.17897 https://mastoxiv.page/@arXiv_csCV_bot/115762783246540528
- Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomy
Gahlawat, Aboudonia, Banik, Hovakimyan, Matni, Ames, Zardini, Speranzon
https://arxiv.org/abs/2512.17899 https://mastoxiv.page/@arXiv_eessSY_bot/115762532257741954
- Re-Depth Anything: Test-Time Depth Refinement via Self-Supervised Re-lighting
Ananta R. Bhattarai, Helge Rhodin
https://arxiv.org/abs/2512.17908 https://mastoxiv.page/@arXiv_csCV_bot/115762785868778349
toXiv_bot_toot

Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference
Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo, Merim Dzaferagic, John D. Kelleher
https://arxiv.org/abs/2510.08303

Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference
As AI becomes a native component of 6G network control, AI models must adapt to continuously changing conditions, including the introduction of new features and measurements driven by multi-vendor deployments, hardware upgrades, and evolving service requirements. To address this growing need for flexible learning in non-stationary environments, this vision paper highlights Adaptive Random Forests (ARFs) as a reliable solution for dynamic feature adaptation in communication network scenarios. We…

Transfer Learning on Edge Connecting Probability Estimation under Graphon Model
Yuyao Wang, Yu-Hung Cheng, Debarghya Mukherjee, Huimin Cheng
https://arxiv.org/abs/2510.05527 htt…

Transfer Learning on Edge Connecting Probability Estimation under Graphon Model
Graphon models provide a flexible nonparametric framework for estimating latent connectivity probabilities in networks, enabling a range of downstream applications such as link prediction and data augmentation. However, accurate graphon estimation typically requires a large graph, whereas in practice, one often only observes a small-sized network. One approach to addressing this issue is to adopt a transfer learning framework, which aims to improve estimation in a small target graph by leveragi…