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

December 8, 2024

Depression detection from Social Media Bangla Text Using Recurrent Neural Networks
Sultan Ahmed, Salman Rakin, Mohammad Washeef Ibn Waliur, Nuzhat Binte Islam, Billal Hossain, Md. Mostofa Akbar
Neural Network Sentiment Analysis Social Medium Underlying Emotion User Sentiment Depression Detection Bangla Text Recurrent Event

December 6, 2024

December 5, 2024

December 4, 2024

December 3, 2024

Measuring Bias of Web-filtered Text Datasets and Bias Propagation Through Training
Youssef Mansour, Reinhard Heckel
Large Language Model Neural Network Data Set Training Data Text Datasets Computer Vision Datasets Classification Datasets Quantifying Bias

Neural Network

Papers

Depression detection from Social Media Bangla Text Using Recurrent Neural Networks

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts

Training neural networks without backpropagation using particles

Neighborhood Commonality-aware Evolution Network for Continuous Generalized Category Discovery

Stably unactivated neurons in ReLU neural networks

Generating Rectifiable Measures through Neural Networks

ACT-Bench: Towards Action Controllable World Models for Autonomous Driving

HeatFormer: A Neural Optimizer for Multiview Human Mesh Recovery

Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning

LossVal: Efficient Data Valuation for Neural Networks

CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor

JPC: Flexible Inference for Predictive Coding Networks in JAX

Patient-specific prediction of glioblastoma growth via reduced order modeling and neural networks

Soft Checksums to Flag Untrustworthy Machine Learning Surrogate Predictions and Application to Atomic Physics Simulations

Short-reach Optical Communications: A Real-world Task for Neuromorphic Hardware

Specification Generation for Neural Networks in Systems

SAVER: A Toolbox for Sampling-Based, Probabilistic Verification of Neural Networks

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks

Assessing the performance of CT image denoisers using Laguerre-Gauss Channelized Hotelling Observer for lesion detection

Measuring Bias of Web-filtered Text Datasets and Bias Propagation Through Training