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

June 7, 2024

Robust quantum dots charge autotuning using neural networks uncertainty
Victor Yon, Bastien Galaup, Claude Rohrbacher, Joffrey Rivard, Clément Godfrin, Roy Li, Stefan Kubicek, Kristiaan De Greve, Louis Gaudreau, Eva Dupont-Ferrier, Yann Beilliard, Roger G. Melko, Dominique Drouin
Neural Network Quantum Dot Charge Tuning High Diagram to Diagram Variability Spin Qubit
Protein pathways as a catalyst to directed evolution of the topology of artificial neural networks
Oscar Lao, Konstantinos Zacharopoulos, Apostolos Fournaris, Rossano Schifanella, Ioannis Arapakis
Neural Network Specie Evolution Topology Problem Biological Pathway Evolutionary Process Inspired Design Phenotypic Data Protein Interaction Network
Concept Drift Detection using Ensemble of Integrally Private Models
Ayush K. Varshney, Vicenc Torra
Neural Network Diverse Ensemble Concept Drift Drift Detection Private Model Concept Drift Detection
Deep Learning Powered Estimate of The Extrinsic Parameters on Unmanned Surface Vehicles
Yi Shen, Hao Liu, Chang Zhou, Wentao Wang, Zijun Gao, Qi Wang
Neural Network Deep Learning Underwater Environment Unmanned Surface Sensor Calibration Extrinsic Calibration Parameter
Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks
Naresh Ravichandran, Anders Lansner, Pawel Herman
Neural Network Unsupervised Representation Learning Plasticity Rule Brain Inspired Associative Memory Feed Forward Layer Hebbian Plasticity Brain Computation
Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-informed Neural Networks Framework for Interface Problems
Sumanta Roy, Chandrasekhar Annavarapu, Pratanu Roy, Antareep Kumar Sarma
Neural Network Physic Informed Neural Network User Interface Elliptic Interface Problem
Revisiting Attention Weights as Interpretations of Message-Passing Neural Networks
Yong-Min Shin, Siqing Li, Xin Cao, Won-Yong Shin
Neural Network Graph Attention Message Passing Neural Network Attention Weight Attribution Score Edge Inference
MeGA: Merging Multiple Independently Trained Neural Networks Based on Genetic Algorithm
Daniel Yun
Neural Network Pre Trained Genetic Algorithm Pre Trained Network Gaussian Avatar

June 6, 2024

Provable Bounds on the Hessian of Neural Networks: Derivative-Preserving Reachability Analysis
Sina Sharifi, Mahyar Fazlyab
Neural Network Hessian Matrix Reachability Analysis Error Bound Taylor Expansion Differentiable Activation Function Quantitative Bound
Real-Time Spacecraft Pose Estimation Using Mixed-Precision Quantized Neural Network on COTS Reconfigurable MPSoC
Julien Posso, Guy Bois, Yvon Savaria
Neural Network Field Programmable Gate Array Multi FPGA Spacecraft Pose Estimation FPGA Resource
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
Can Yaras, Peng Wang, Laura Balzano, Qing Qu
Neural Network LeArning Abstract Adaptation Concern Low Rank Adaptation Language Model Fine Tuning Low Rank Matrix Completion Compressible Flow
Slicing Mutual Information Generalization Bounds for Neural Networks
Kimia Nadjahi, Kristjan Greenewald, Rickard Brüel Gabrielsson, Justin Solomon
Neural Network Generalization Bound Information Theoretic Generalization Bound Random Subspace
Data-driven discovery of self-similarity using neural networks
Ryota Watanabe, Takanori Ishii, Yuji Hirono, Hirokazu Maruoka
Neural Network Data Driven Discovery Self Similar Self Similarity
ReDistill: Residual Encoded Distillation for Peak Memory Reduction
Fang Chen, Gourav Datta, Mujahid Al Rafi, Hyeran Jeon, Meng Tang
Neural Network Mutual Distillation Residual Stream Peak Memory

June 5, 2024

TIDMAD: Time Series Dataset for Discovering Dark Matter with AI Denoising
J. T. Fry, Aobo Li, Lindley Winslow, Xinyi Hope Fu, Zhenghao Fu, Kaliroe M. W. Pappas
Neural Network LangId Magic Spell Time Series Datasets Dark Matter
GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications
Oisín M. Morrison, Federico Pichi, Jan S. Hesthaven
Neural Network Graph Convolutional Network Operator Learning Model Order Reduction Multifidelity Estimator
Grokking Modular Polynomials
Darshil Doshi, Tianyu He, Aritra Das, Andrey Gromov
Neural Network Multi Layer Modular Addition
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation
Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev
Neural Network Scientific Inference Bayesian Inference Comprehensive Investigation Simulation Based Inference Model Misspecification Amortized Bayesian Inference Neural Posterior Estimation
When Spiking neural networks meet temporal attention image decoding and adaptive spiking neuron
Xuerui Qiu, Zheng Luan, Zhaorui Wang, Rui-Jie Zhu
Neural Network Spiking Neural Network Temporal Attention Single Neuron Level Neural Network Output
Efficient User Sequence Learning for Online Services via Compressed Graph Neural Networks
Yucheng Wu, Liyue Chen, Yu Cheng, Shuai Chen, Jinyu Xu, Leye Wang
Neural Network Sequence to Sequence Sequence Modeling Online Service Sequence Representation Sequence Relationship

Neural Network

Papers

Robust quantum dots charge autotuning using neural networks uncertainty

Protein pathways as a catalyst to directed evolution of the topology of artificial neural networks

Concept Drift Detection using Ensemble of Integrally Private Models

Deep Learning Powered Estimate of The Extrinsic Parameters on Unmanned Surface Vehicles

Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks

Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-informed Neural Networks Framework for Interface Problems

Revisiting Attention Weights as Interpretations of Message-Passing Neural Networks

MeGA: Merging Multiple Independently Trained Neural Networks Based on Genetic Algorithm

Provable Bounds on the Hessian of Neural Networks: Derivative-Preserving Reachability Analysis

Real-Time Spacecraft Pose Estimation Using Mixed-Precision Quantized Neural Network on COTS Reconfigurable MPSoC

Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation

Slicing Mutual Information Generalization Bounds for Neural Networks

Data-driven discovery of self-similarity using neural networks

ReDistill: Residual Encoded Distillation for Peak Memory Reduction

TIDMAD: Time Series Dataset for Discovering Dark Matter with AI Denoising

GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications

Grokking Modular Polynomials

Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation

When Spiking neural networks meet temporal attention image decoding and adaptive spiking neuron

Efficient User Sequence Learning for Online Services via Compressed Graph Neural Networks