Tootfinder

CKFNet: Neural Network Aided Cubature Kalman filtering
Jinhui Hu, Haiquan Zhao, Yi Peng
https://arxiv.org/abs/2508.09727 https://arxiv.org/pdf/2508.09727…

CKFNet: Neural Network Aided Cubature Kalman filtering
The cubature Kalman filter (CKF), while theoretically rigorous for nonlinear estimation, often suffers performance degradation due to model-environment mismatches in practice. To address this limitation, we propose CKFNet-a hybrid architecture that synergistically integrates recurrent neural networks (RNN) with the CKF framework while preserving its cubature principles. Unlike conventional model-driven approaches, CKFNet embeds RNN modules in the prediction phase to dynamically adapt to unmodel…

Optimised neural networks for online processing of ATLAS calorimeter data on FPGAs
Georges Aad, Raphael Bertrand, Lauri Laatu, Emmanuel Monnier, Arno Straessner, Nairit Sur, Johann C. Voigt
https://arxiv.org/abs/2510.11469

Optimised neural networks for online processing of ATLAS calorimeter data on FPGAs
A study of neural network architectures for the reconstruction of the energy deposited in the cells of the ATLAS liquid-argon calorimeters under high pile-up conditions expected at the HL-LHC is presented. These networks are designed to run on the FPGA-based readout hardware of the calorimeters under strict size and latency constraints. Several architectures, including Dense, Recurrent (RNN), and Convolutional (CNN) neural networks, are optimised using a Bayesian procedure that balances energy …

Recurrent State Encoders for Efficient Neural Combinatorial Optimization
Tim Dernedde, Daniela Thyssens, Lars Schmidt-Thieme
https://arxiv.org/abs/2509.05084 https://

Recurrent State Encoders for Efficient Neural Combinatorial Optimization
The primary paradigm in Neural Combinatorial Optimization (NCO) are construction methods, where a neural network is trained to sequentially add one solution component at a time until a complete solution is constructed. We observe that the typical changes to the state between two steps are small, since usually only the node that gets added to the solution is removed from the state. An efficient model should be able to reuse computation done in prior steps. To that end, we propose to train a recu…

Dynamically Slimmable Speech Enhancement Network with Metric-Guided Training
Haixin Zhao, Kaixuan Yang, Nilesh Madhu
https://arxiv.org/abs/2510.11395 https://

Dynamically Slimmable Speech Enhancement Network with Metric-Guided Training
To further reduce the complexity of lightweight speech enhancement models, we introduce a gating-based Dynamically Slimmable Network (DSN). The DSN comprises static and dynamic components. For architecture-independent applicability, we introduce distinct dynamic structures targeting the commonly used components, namely, grouped recurrent neural network units, multi-head attention, convolutional, and fully connected layers. A policy module adaptively governs the use of dynamic parts at a frame-w…

Microstructure sensitive recurrent neural network surrogate model of crystal plasticity
Michael D. Atkinson, Michael D. White, Adam J. Plowman, Pratheek Shanthraj
https://arxiv.org/abs/2510.06904

Microstructure sensitive recurrent neural network surrogate model of crystal plasticity
The development of next-generation structural materials for harsh environments requires rapid assessment of mechanical performance and its dependence on microstructure. While full-field crystal plasticity (CP) models provide detailed insights, the high computational cost limits their use with uncertainty quantification workflows and in component-scale simulation. Surrogate models based on recurrent neural networks (RNNs) have shown promise in reproducing history-dependent mechanical behaviour b…

An analog-electronic implementation of a harmonic oscillator recurrent neural network
Pedro Carvalho, Bernd Ulmann, Wolf Singer, Felix Effenberger
https://arxiv.org/abs/2509.04064

An analog-electronic implementation of a harmonic oscillator recurrent neural network
Oscillatory recurrent networks, such as the Harmonic Oscillator Recurrent Network (HORN) model, offer advantages in parameter efficiency, learning speed, and robustness relative to traditional non-oscillating architectures. Yet, while many implementations of physical neural networks exploiting attractor dynamics have been studied, implementations of oscillatory models in analog-electronic hardware that utilize the networks' transient dynamics so far are lacking. This study explores the feasibil…

Short-Term Gaze Prediction: Analysis of Individual Differences, Typical and Extreme-Case Errors
Kateryna Melnyk, Lee Friedman, Oleg Komogortsev
https://arxiv.org/abs/2509.07126 …

Short-Term Gaze Prediction: Analysis of Individual Differences, Typical and Extreme-Case Errors
Gaze prediction is a diverse field of study with multiple research focuses and practical applications. This article investigates how recurrent neural networks and transformers perform short-term gaze prediction. We used three models: a three-layer long-short-term memory (LSTM) network, a simple transformer-encoder model (TF), and a classification-predictor network (ClPr), which simultaneously classifies the signal into eye movement events and predicts the positions of gaze. The performance of t…

Heartbeat Stars Recognition Based on Recurrent Neural Networks - Method and Validation: #HeartbeatStars: https://english.cas.cn/newsroom/research_news/phys/202509/t20250930_1061224.shtml

Infant Cry Detection In Noisy Environment Using Blueprint Separable Convolutions and Time-Frequency Recurrent Neural Network
Haolin Yu, Yanxiong Li
https://arxiv.org/abs/2508.19308

Infant Cry Detection In Noisy Environment Using Blueprint Separable Convolutions and Time-Frequency Recurrent Neural Network
Infant cry detection is a crucial component of baby care system. In this paper, we propose a lightweight and robust method for infant cry detection. The method leverages blueprint separable convolutions to reduce computational complexity, and a time-frequency recurrent neural network for adaptive denoising. The overall framework of the method is structured as a multi-scale convolutional recurrent neural network, which is enhanced by efficient spatial attention mechanism and contrast-aware chann…

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…

Overcoming the curse of dimensionality: Enabling multi-layer photon transport with recurrent neural network
Daniel Carne, Ziqi Guo, Xiulin Ruan
https://arxiv.org/abs/2509.22890 …

Overcoming the curse of dimensionality: Enabling multi-layer photon transport with recurrent neural network
Monte Carlo simulations are commonly used to calculate photon reflectance, absorptance, and transmittance of multi-layer scattering and absorbing media, but they can quickly become prohibitively expensive as the number of layers increases. In this study, we show that although a plain neural network suffers from the curse of dimensionality and fails to yield acceptable predictions of multilayer media, we introduce a recurrent neural network (RNN) trained on the same Monte Carlo simulation datase…

Mapping surface height dynamics to subsurface flow physics in free-surface turbulent flow using a shallow recurrent decoder
Kristoffer S. Moen, J{\o}rgen R. Aarnes, Simen {\AA}. Ellingsen, J. Nathan Kutz
https://arxiv.org/abs/2510.06202

Mapping surface height dynamics to subsurface flow physics in free-surface turbulent flow using a shallow recurrent decoder
Near-surface turbulent flows beneath a free surface are reconstructed from sparse measurements of the surface height variation, by a novel neural network algorithm known as the SHallow REcurrent Decoder (SHRED). The reconstruction of turbulent flow fields from limited, partial, or indirect measurements remains a grand challenge in science and engineering. The central goal in such applications is to leverage easy-to-measure proxy variables in order to estimate quantities which have not been, and…

From Neural Activity to Computation: Biological Reservoirs for Pattern Recognition in Digit Classification
Ludovico Iannello, Luca Ciampi, Fabrizio Tonelli, Gabriele Lagani, Lucio Maria Calcagnile, Federico Cremisi, Angelo Di Garbo, Giuseppe Amato
https://arxiv.org/abs/2510.05637

From Neural Activity to Computation: Biological Reservoirs for Pattern Recognition in Digit Classification
In this paper, we present a biologically grounded approach to reservoir computing (RC), in which a network of cultured biological neurons serves as the reservoir substrate. This system, referred to as biological reservoir computing (BRC), replaces artificial recurrent units with the spontaneous and evoked activity of living neurons. A multi-electrode array (MEA) enables simultaneous stimulation and readout across multiple sites: inputs are delivered through a subset of electrodes, while the rem…

Adaptive Neural Quantum States: A Recurrent Neural Network Perspective
Jake McNaughton, Mohamed Hibat-Allah
https://arxiv.org/abs/2507.18700 https://arxiv.…

Adaptive Neural Quantum States: A Recurrent Neural Network Perspective
Neural-network quantum states (NQS) are powerful neural-network ansätzes that have emerged as promising tools for studying quantum many-body physics through the lens of the variational principle. These architectures are known to be systematically improvable by increasing the number of parameters. Here we demonstrate an Adaptive scheme to optimize NQSs, through the example of recurrent neural networks (RNN), using a fraction of the computation cost while reducing training fluctuations and impro…

Bundle Network: a Machine Learning-Based Bundle Method
Francesca Demelas, Joseph Le Roux, Antonio Frangioni, Mathieu Lacroix, Emiliano Traversi, Roberto Wolfler Calvo
https://arxiv.org/abs/2509.24736

Bundle Network: a Machine Learning-Based Bundle Method
This paper presents Bundle Network, a learning-based algorithm inspired by the Bundle Method for convex non-smooth minimization problems. Unlike classical approaches that rely on heuristic tuning of a regularization parameter, our method automatically learns to adjust it from data. Furthermore, we replace the iterative resolution of the optimization problem that provides the search direction-traditionally computed as a convex combination of gradients at visited points-with a recurrent neural mo…

A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
Miha O\v{z}bot, Igor \v{S}krjanc, Vitomir \v{S}truc
https://arxiv.org/abs/2510.00960 https://

A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
In the complex landscape of multivariate time series forecasting, achieving both accuracy and interpretability remains a significant challenge. This paper introduces the Fuzzy Transformer (Fuzzformer), a novel recurrent neural network architecture combined with multi-head self-attention and fuzzy inference systems to analyze multivariate stock market data and conduct long-term time series forecasting. The method leverages LSTM networks and temporal attention to condense multivariate data into i…

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 …

Fast Real-Time Pipeline for Robust Arm Gesture Recognition
Mil\'an Zsolt Bagladi, L\'aszl\'o Guly\'as, Gerg\H{o} Szalay
https://arxiv.org/abs/2509.25042 https://…

Fast Real-Time Pipeline for Robust Arm Gesture Recognition
This paper presents a real-time pipeline for dynamic arm gesture recognition based on OpenPose keypoint estimation, keypoint normalization, and a recurrent neural network classifier. The 1 x 1 normalization scheme and two feature representations (coordinate- and angle-based) are presented for the pipeline. In addition, an efficient method to improve robustness against camera angle variations is also introduced by using artificially rotated training data. Experiments on a custom traffic-control …

Neurocognitive Modeling for Text Generation: Deep Learning Architecture for EEG Data
Khushiyant
https://arxiv.org/abs/2509.07202 https://arxiv.org/pdf/2509…

Neurocognitive Modeling for Text Generation: Deep Learning Architecture for EEG Data
Text generating capabilities have undergone a substantial transformation with the introduction of large language models (LLMs). Electroencephalography (EEG)-based text production is still difficult, though, because it requires a lot of data and processing power. This paper introduces a new method that combines the use of the Gemma 2B LLM with a classifier-LLM architecture to incorporate a Recurrent Neural Network (RNN) encoder. Our approach drastically lowers the amount of data and compute powe…

Adaptive Market Intelligence: A Mixture of Experts Framework for Volatility-Sensitive Stock Forecasting
Diego Vallarino
https://arxiv.org/abs/2508.02686 https://

Adaptive Market Intelligence: A Mixture of Experts Framework for Volatility-Sensitive Stock Forecasting
This study develops and empirically validates a Mixture of Experts (MoE) framework for stock price prediction across heterogeneous volatility regimes using real market data. The proposed model combines a Recurrent Neural Network (RNN) optimized for high-volatility stocks with a linear regression model tailored to stable equities. A volatility-aware gating mechanism dynamically weights the contributions of each expert based on asset classification. Using a dataset of 30 publicly traded U.S. stoc…

Echoes of the past: A unified perspective on fading memory and echo states
Juan-Pablo Ortega, Florian Rossmannek
https://arxiv.org/abs/2508.19145 https://a…

Echoes of the past: A unified perspective on fading memory and echo states
Recurrent neural networks (RNNs) have become increasingly popular in information processing tasks involving time series and temporal data. A fundamental property of RNNs is their ability to create reliable input/output responses, often linked to how the network handles its memory of the information it processed. Various notions have been proposed to conceptualize the behavior of memory in RNNs, including steady states, echo states, state forgetting, input forgetting, and fading memory. Although…

Response function as a quantitative measure of consciousness in brain dynamics
Wenkang Du, Haiping Huang
https://arxiv.org/abs/2509.00730 https://arxiv.org…

Response function as a quantitative measure of consciousness in brain dynamics
Understanding the neural correlates of consciousness remains a central challenge in neuroscience. In this study, we investigate the relationship between consciousness and neural responsiveness by analyzing intracranial ECoG recordings from non-human primates across three distinct states: wakefulness, anesthesia, and recovery. Using a nonequilibrium recurrent neural network (RNN) model, we fit state-dependent cortical dynamics to extract the neural response function as a dynamics complexity indi…

Hebbian Memory-Augmented Recurrent Networks: Engram Neurons in Deep Learning
Daniel Szelogowski
https://arxiv.org/abs/2507.21474 https://arxiv.org/pdf/2507…

Hebbian Memory-Augmented Recurrent Networks: Engram Neurons in Deep Learning
Despite success across diverse tasks, current artificial recurrent network architectures rely primarily on implicit hidden-state memories, limiting their interpretability and ability to model long-range dependencies. In contrast, biological neural systems employ explicit, associative memory traces (i.e., engrams) strengthened through Hebbian synaptic plasticity and activated sparsely during recall. Motivated by these neurobiological insights, we introduce the Engram Neural Network (ENN), a nove…

i am determined to read the attention/transformer paper
i even printed it out

Temperature-Aware Recurrent Neural Operator for Temperature-Dependent Anisotropic Plasticity in HCP Materials
Yannick Hollenweger, Dennis M. Kochman, Burigede Liu
https://arxiv.org/abs/2508.18806

Temperature-Aware Recurrent Neural Operator for Temperature-Dependent Anisotropic Plasticity in HCP Materials
Neural network surrogate models for constitutive laws in computational mechanics have been in use for some time. In plasticity, these models often rely on gated recurrent units (GRUs) or long short-term memory (LSTM) cells, which excel at capturing path-dependent phenomena. However, they suffer from long training times and time-resolution-dependent predictions that extrapolate poorly. Moreover, most existing surrogates for macro- or mesoscopic plasticity handle only relatively simple material b…

Overcoming Latency Bottlenecks in On-Device Speech Translation: A Cascaded Approach with Alignment-Based Streaming MT
Zeeshan Ahmed, Frank Seide, Niko Moritz, Ju Lin, Ruiming Xie, Simone Merello, Zhe Liu, Christian Fuegen
https://arxiv.org/abs/2508.13358

Overcoming Latency Bottlenecks in On-Device Speech Translation: A Cascaded Approach with Alignment-Based Streaming MT
This paper tackles several challenges that arise when integrating Automatic Speech Recognition (ASR) and Machine Translation (MT) for real-time, on-device streaming speech translation. Although state-of-the-art ASR systems based on Recurrent Neural Network Transducers (RNN-T) can perform real-time transcription, achieving streaming translation in real-time remains a significant challenge. To address this issue, we propose a simultaneous translation approach that effectively balances translation…

A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
Miha O\v{z}bot, Igor \v{S}krjanc, Vitomir \v{S}truc
https://arxiv.org/abs/2510.00960 https://

A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
In the complex landscape of multivariate time series forecasting, achieving both accuracy and interpretability remains a significant challenge. This paper introduces the Fuzzy Transformer (Fuzzformer), a novel recurrent neural network architecture combined with multi-head self-attention and fuzzy inference systems to analyze multivariate stock market data and conduct long-term time series forecasting. The method leverages LSTM networks and temporal attention to condense multivariate data into i…

Deep Feedback Models
David Calhas, Arlindo L. Oliveira
https://arxiv.org/abs/2509.15905 https://arxiv.org/pdf/2509.15905

Deep Feedback Models
Deep Feedback Models (DFMs) are a new class of stateful neural networks that combine bottom up input with high level representations over time. This feedback mechanism introduces dynamics into otherwise static architectures, enabling DFMs to iteratively refine their internal state and mimic aspects of biological decision making. We model this process as a differential equation solved through a recurrent neural network, stabilized via exponential decay to ensure convergence. To evaluate their ef…

Hybrid Real- And Complex-Valued Neural Network Concept For Low-Complexity Phase-Aware Speech Enhancement
Luan Vin\'icius Fiorio, Alex Young, Ronald M. Aarts
https://arxiv.org/abs/2509.21185

Hybrid Real- And Complex-Valued Neural Network Concept For Low-Complexity Phase-Aware Speech Enhancement
In this paper, we propose hybrid real- and complex-valued neural networks for speech enhancement. Real- or complex-valued models are either inefficient or present high complexity. We devise a straightforward design method for extending a real-valued network into its hybrid counterpart. Based on speech intelligibility and quality metrics, we compare the real, complex, and hybrid versions of a convolutional and a convolutional-recurrent architecture. The hybrid network consistently outperforms it…

Replaced article(s) found for cond-mat.quant-gas. https://arxiv.org/list/cond-mat.quant-gas/new
[1/1]:
- Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice
Mohamed Hibat-Allah, Ejaaz Merali, Giacomo Torlai, Roger G Melko, Juan Carrasquilla…

Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback Regulation and Residual Connections
Zhuo Liu, Tao Chen
https://arxiv.org/abs/2508.11659

Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback Regulation and Residual Connections
Brain-like intelligent systems need brain-like learning methods. Equilibrium Propagation (EP) is a biologically plausible learning framework with strong potential for brain-inspired computing hardware. However, existing im-plementations of EP suffer from instability and prohibi-tively high computational costs. Inspired by the structure and dynamics of the brain, we propose a biologically plau-sible Feedback-regulated REsidual recurrent neural network (FRE-RNN) and study its learning performance…

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- ReMi: A Random Recurrent Neural Network Approach to Music Production
Hugo Chateau-Laurent, Tara Vanhatalo, Wei-Tung Pan, Xavier Hinaut

Stochastic synaptic dynamics under learning
Jakob Stubenrauch, Naomi Auer, Richard Kempter, Benjamin Lindner
https://arxiv.org/abs/2508.13846 https://arxiv…

Stochastic synaptic dynamics under learning
Learning is based on synaptic plasticity, which drives and is driven by neural activity. Because pre- and postsynaptic spiking activity is shaped by randomness, the synaptic weights follow a stochastic process, requiring a probabilistic framework to capture the noisy synaptic dynamics. We consider a paradigmatic supervised learning example: a presynaptic neural population impinging in a sequence of episodes on a recurrent network of integrate-and-fire neurons through synapses undergoing spike-t…

Multi-Speaker DOA Estimation in Binaural Hearing Aids using Deep Learning and Speaker Count Fusion
Farnaz Jazaeri, Homayoun Kamkar-Parsi, Fran\c{c}ois Grondin, Martin Bouchard
https://arxiv.org/abs/2509.21382

Multi-Speaker DOA Estimation in Binaural Hearing Aids using Deep Learning and Speaker Count Fusion
For extracting a target speaker voice, direction-of-arrival (DOA) estimation is crucial for binaural hearing aids operating in noisy, multi-speaker environments. Among the solutions developed for this task, a deep learning convolutional recurrent neural network (CRNN) model leveraging spectral phase differences and magnitude ratios between microphone signals is a popular option. In this paper, we explore adding source-count information for multi-sources DOA estimation. The use of dual-task trai…

Crosslisted article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback R...
Zhuo Liu, Tao Chen