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

November 19, 2024

Exploring the Manifold of Neural Networks Using Diffusion Geometry
Elliott Abel, Peyton Crevasse, Yvan Grinspan, Selma Mazioud, Folu Ogundipe, Kristof Reimann, Ellie Schueler, Andrew J. Steindl, Ellen Zhang, Dhananjay Bhaskar, Siddharth Viswanath, Yanlei Zhang, Tim G. J. Rudner, Ian Adelstein, Smita Krishnaswamy
Neural Network Dimensionality Reduction Manifold Learning High Dimensional Data Low Dimensional Manifold Dimensional Manifold Manifold Hypothesis
Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment
Ekaterina Shumitskaya, Mikhail Pautov, Dmitriy Vatolin, Anastasia Antsiferova
Neural Network Adversarial Image Quality Assessment Randomized Smoothing Nr Iqa Blind Image
The Hermeneutic Turn of AI: Is the Machine Capable of Interpreting?
Remy Demichelis
Neural Network Deep Learning Artificial Intelligence New Machine Digital Computing Machine Translated Sensemaking Tool
SNN-Based Online Learning of Concepts and Action Laws in an Open World
Christel Grimaud (IRIT-LILaC), Dominique Longin (IRIT-LILaC), Andreas Herzig (IRIT-LILaC)
Neural Network Online Learning Open World Concept Identification Semantic Memory Motion Dynamic Situation Model Action Principle
Error-Feedback Model for Output Correction in Bilateral Control-Based Imitation Learning
Hiroshi Sato, Masashi Konosu, Sho Sakaino, Toshiaki Tsuji
Neural Network Imitation Learning Multi Layer Structured Output Error Feedback Information Asymmetry Feedback Mechanism

November 17, 2024

Variational Bayesian Bow tie Neural Networks with Shrinkage
Alisa Sheinkman, Sara Wade
Neural Network Variational Inference Deep Model Bayesian Perspective Variational Bayesian Inference Shrinkage Estimator Exact Bayesian Inference
Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction
Maneesha Perera, Julian De Hoog, Kasun Bandara, Saman Halgamuge
Neural Network Attention Based Weather Forecasting Solar Generation Cloud Classification Solar Forecasting

November 15, 2024

A Hard-Label Cryptanalytic Extraction of Non-Fully Connected Deep Neural Networks using Side-Channel Attacks
Benoit Coqueret, Mathieu Carbone, Olivier Sentieys, Gabriel Zaid
Neural Network Deep Neural Network Adversarial Example Network Programming Deep Network Side Channel Hard Label Attack
Adapting the Biological SSVEP Response to Artificial Neural Networks
Emirhan Böge, Yasemin Gunindi, Erchan Aptoula, Nihan Alp, Huseyin Ozkan
Neural Network Artificial Neural Network Neural Response State Visual Evoked Potential Auto Tagging Neuron Importance
Adaptive Physics-Guided Neural Network
David Shulman, Itai Dattner
Neural Network Adaptive Importance ResNet Based Thermal Image Dataset Physic Prediction
Physics-informed neural networks need a physicist to be accurate: the case of mass and heat transport in Fischer-Tropsch catalyst particles
Tymofii Nikolaienko, Harshil Patel, Aniruddha Panda, Subodh Madhav Joshi, Stanislav Jaso, Kaushic Kalyanaraman
Neural Network Physic Informed Neural Network Scientific Research Reaction Diffusion Finite Difference Heat Transfer Tandem Mass
Dense ReLU Neural Networks for Temporal-spatial Model
Zhi Zhang, Carlos Misael Madrid Padilla, Xiaokai Luo, Oscar Hernan Madrid Padilla, Daren Wang
Neural Network Spatio Temporal Rectified Linear Unit Dense Neural Network Intrinsic Dimensionality

November 14, 2024

Learning Parameter Sharing with Tensor Decompositions and Sparsity
Cem Üyük, Mike Lasby, Mohamed Yassin, Utku Evci, Yani Ioannou
Neural Network Vision Transformer Sparsity Increase Tensor Decomposition Parameter Sharing
Physics-informed neural networks (PINNs) for numerical model error approximation and superresolution
Bozhou Zhuang, Sashank Rana, Brandon Jones, Danny Smyl
Neural Network Super Resolution Physic Informed Neural Network Average Approximation Parameter Identification Model Error Numerical Approximation
Improving hp-Variational Physics-Informed Neural Networks for Steady-State Convection-Dominated Problems
Thivin Anandh, Divij Ghose, Himanshu Jain, Pratham Sunkad, Sashikumaar Ganesan, Volker John
Neural Network Physic Informed Neural Network Numerical Method Boundary Condition Self Stabilization Effect Advection Diffusion Reaction
Hybrid deep additive neural networks
Gyu Min Kim, Jeong Min Jeon
Neural Network Multi Layer Efficient Hybrid Hybrid Neural Network Neural Additive Model Traditional Neural Network
Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery
Ashim Dahal, Saydul Akbar Murad, Nick Rahimi
Neural Network Vision Transformer Semantic Segmentation Deep Convolutional Neural Network Remote Sensing Image State of the Art Segmentation

Neural Network

Papers

Exploring the Manifold of Neural Networks Using Diffusion Geometry

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment

The Hermeneutic Turn of AI: Is the Machine Capable of Interpreting?

SNN-Based Online Learning of Concepts and Action Laws in an Open World

Error-Feedback Model for Output Correction in Bilateral Control-Based Imitation Learning

BALI: Learning Neural Networks via Bayesian Layerwise Inference

Don't Be So Positive: Negative Step Sizes in Second-Order Methods

Making Sigmoid-MSE Great Again: Output Reset Challenges Softmax Cross-Entropy in Neural Network Classification

Variational Bayesian Bow tie Neural Networks with Shrinkage

Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction

A Hard-Label Cryptanalytic Extraction of Non-Fully Connected Deep Neural Networks using Side-Channel Attacks

Adapting the Biological SSVEP Response to Artificial Neural Networks

Adaptive Physics-Guided Neural Network

Physics-informed neural networks need a physicist to be accurate: the case of mass and heat transport in Fischer-Tropsch catalyst particles

Dense ReLU Neural Networks for Temporal-spatial Model

Learning Parameter Sharing with Tensor Decompositions and Sparsity

Physics-informed neural networks (PINNs) for numerical model error approximation and superresolution

Improving hp-Variational Physics-Informed Neural Networks for Steady-State Convection-Dominated Problems

Hybrid deep additive neural networks

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery