Neural Network

Neural networks are computational models inspired by the structure and function of the brain, primarily aimed at approximating complex functions and solving diverse problems through learning from data. Current research emphasizes improving efficiency and robustness, exploring novel architectures like sinusoidal neural fields and hybrid models combining neural networks with radial basis functions, as well as developing methods for understanding and manipulating the internal representations learned by these networks, such as through hyper-representations of network weights. These advancements are driving progress in various fields, including computer vision, natural language processing, and scientific modeling, by enabling more accurate, efficient, and interpretable AI systems.

Papers

October 22, 2024

Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks
Anthony Baez, Wang Zhang, Ziwen Ma, Subhro Das, Lam M. Nguyen, Luca Daniel
Neural Network Physical System Projection Bias Residual Momentum Conservation Law Physical Law
Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading
Zimeng Lyu, Amulya Saxena, Rohaan Nadeem, Hao Zhang, Travis Desell
Neural Network Recurrent Neural Network Neural Architecture Search Stock Market Stock Price Forecasting Stock Return Prediction Market Return
Dynamic User Grouping based on Location and Heading in 5G NR Systems
Dino Pjanić, Korkut Emre Arslantürk, Xuesong Cai, Fredrik Tufvesson
Neural Network Clustering Method Location Information G Wireless Article Centered Factor User Clustering Optimal Grouping
Rethinking generalization of classifiers in separable classes scenarios and over-parameterized regimes
Julius Martinetz, Christoph Linse, Thomas Martinetz
Neural Network Strong Generalization Simple Classifier Empirical Risk Minimization Class Separability Parameterized Regime
LLM-Assisted Red Teaming of Diffusion Models through "Failures Are Fated, But Can Be Faded"
Som Sagar, Aditya Taparia, Ransalu Senanayake
Reinforcement Learning Neural Network Diffusion Model Red Teaming Leg Failure Partial Feedback Failure Transition

October 21, 2024

Gradient-Free Supervised Learning using Spike-Timing-Dependent Plasticity for Image Recognition
Wei Xie
Neural Network LeArning Abstract Supervised Learning Back Propagation Image Recognition Plasticity Rule Gradient Free Spike Timing Dependent Plasticity
Efficient Neural Network Training via Subset Pretraining
Jan Spörer, Bernhard Bermeitinger, Tomas Hrycej, Niklas Limacher, Siegfried Handschuh
Neural Network Second Order Stochastic Approximation Subset Selection Local Minimum
Identifying Sub-networks in Neural Networks via Functionally Similar Representations
Tian Gao, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Dennis Wei
Neural Network Meaningful Representation Neural Network Model Mechanistic Interpretability Intermediate Representation Neural Network Layer
Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium
Mehdi Yazdani-Jahromi, Ali Khodabandeh Yalabadi, AmirArsalan Rajabi, Aida Tayebi, Ivan Garibay, Ozlem Ozmen Garibay
Neural Network Bilevel Optimization Fairness Accuracy Fairness Aware Machine Fairness Objective Achieving Fairness Stackelberg Equilibrium Fairness Method
Theoretical Limitations of Ensembles in the Age of Overparameterization
Niclas Dern, John P. Cunningham, Geoff Pleiss
Neural Network Diverse Ensemble Deep Ensemble Speech Based Age Overparametrization Bound Parameterized Model Tree Based Ensemble Random Feature Regression
Learning How to Vote With Principles: Axiomatic Insights Into the Collective Decisions of Neural Networks
Levin Hornischer, Zoi Terzopoulou
Neural Network General Principle Axiomatic Approach Collective Decision
Metric as Transform: Exploring beyond Affine Transform for Interpretable Neural Network
Suman Sapkota
Neural Network Convolutional Neural Network Multi Layer Interpretable Way North Star Metric Affine Transformation Dot Product
Small Contributions, Small Networks: Efficient Neural Network Pruning Based on Relative Importance
Mostafa Hussien, Mahmoud Afifi, Kim Khoa Nguyen, Mohamed Cheriet
Neural Network Pruning Method Client Contribution Neural Network Pruning Tiny Network
Increasing Interpretability of Neural Networks By Approximating Human Visual Saliency
Aidan Boyd, Mohamed Trabelsi, Huseyin Uzunalioglu, Dan Kushnir
Neural Network Inherent Interpretability Human Generated Human Saliency Visual Saliency Human Interpretability Saliency Information
Karush-Kuhn-Tucker Condition-Trained Neural Networks (KKT Nets)
Shreya Arvind, Rishabh Pomaje, Rajshekhar V Bhat
Neural Network Loss Function Convex Optimization Dual Formulation Polynomial Network
On The Global Convergence Of Online RLHF With Neural Parametrization
Mudit Gaur, Amrit Singh Bedi, Raghu Pasupathy, Vaneet Aggarwal
Large Language Model Reinforcement Learning Neural Network Reinforcement Learning From Human Feedback Global Convergence Reward Learning AI Alignment
All You Need is an Improving Column: Enhancing Column Generation for Parallel Machine Scheduling via Transformers
Amira Hijazi, Osman Ozaltin, Reha Uzsoy
Neural Network Transformer Megatron Decepticons Column Generation Machine Scheduling

October 20, 2024

Integrating Symbolic Neural Networks with Building Physics: A Study and Proposal
Xia Chen, Guoquan Lv, Xinwei Zhuang, Carlos Duarte, Stefano Schiavon, Philipp Geyer
Neural Network Data Driven Study Feature Predictive Model Neuro Symbolic Kolmogorov Arnold Network Proposal Balance Refinement Knowledge Discovery Physic Guided Architecture
SNAP: Stopping Catastrophic Forgetting in Hebbian Learning with Sigmoidal Neuronal Adaptive Plasticity
Tianyi Xu, Patrick Zheng, Shiyan Liu, Sicheng Lyu, Isabeau Prémont-Schwarz
Neural Network Catastrophic Forgetting Snap Video
Fractional-order spike-timing-dependent gradient descent for multi-layer spiking neural networks
Yi Yang, Richard M. Voyles, Haiyan H. Zhang, Robert A. Nawrocki
Neural Network Spike Timing Dependent Plasticity Fractional Derivative Loss Gradient Fractional Order Gradient

Neural Network

Papers

Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks

Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading

Dynamic User Grouping based on Location and Heading in 5G NR Systems

Rethinking generalization of classifiers in separable classes scenarios and over-parameterized regimes

LLM-Assisted Red Teaming of Diffusion Models through "Failures Are Fated, But Can Be Faded"

Gradient-Free Supervised Learning using Spike-Timing-Dependent Plasticity for Image Recognition

Efficient Neural Network Training via Subset Pretraining

Identifying Sub-networks in Neural Networks via Functionally Similar Representations

Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium

Theoretical Limitations of Ensembles in the Age of Overparameterization

Learning How to Vote With Principles: Axiomatic Insights Into the Collective Decisions of Neural Networks

Metric as Transform: Exploring beyond Affine Transform for Interpretable Neural Network

Small Contributions, Small Networks: Efficient Neural Network Pruning Based on Relative Importance

Increasing Interpretability of Neural Networks By Approximating Human Visual Saliency

Karush-Kuhn-Tucker Condition-Trained Neural Networks (KKT Nets)

On The Global Convergence Of Online RLHF With Neural Parametrization

All You Need is an Improving Column: Enhancing Column Generation for Parallel Machine Scheduling via Transformers

Integrating Symbolic Neural Networks with Building Physics: A Study and Proposal

SNAP: Stopping Catastrophic Forgetting in Hebbian Learning with Sigmoidal Neuronal Adaptive Plasticity

Fractional-order spike-timing-dependent gradient descent for multi-layer spiking neural networks