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
Memory-Efficient Training for Deep Speaker Embedding Learning in Speaker Verification
Bei Liu, Yanmin Qian
Forward and Inverse Simulation of Pseudo-Two-Dimensional Model of Lithium-Ion Batteries Using Neural Networks
Myeong-Su Lee, Jaemin Oh, Dong-Chan Lee, KangWook Lee, Sooncheol Park, Youngjoon Hong
On the Feature Learning in Diffusion Models
Andi Han, Wei Huang, Yuan Cao, Difan Zou
Average-Over-Time Spiking Neural Networks for Uncertainty Estimation in Regression
Tao Sun, Sander Bohté
Improving the performance of weak supervision searches using data augmentation
Zong-En Chen, Cheng-Wei Chiang, Feng-Yang Hsieh
DeMo: Decoupled Momentum Optimization
Bowen Peng, Jeffrey Quesnelle, Diederik P. Kingma
Integrated Artificial Neurons from Metal Halide Perovskites
Jeroen J. de Boer, Bruno Ehrler
A Deep Learning Approach to Language-independent Gender Prediction on Twitter
Reyhaneh Hashempour, Barbara Plank, Aline Villavicencio, Renato Cordeiro de Amorim
Memristive Nanowire Network for Energy Efficient Audio Classification: Pre-Processing-Free Reservoir Computing with Reduced Latency
Akshaya Rajesh (1), Pavithra Ananthasubramanian (1), Nagarajan Raghavan (1), Ankush Kumar (1 and 2) ((1) nano-Macro Reliability Laboratory (nMRL), Engineering and Product Development Pillar, Singapore University of Technology and Design, 8, Somapah Road, 487372, Singapore, (2) Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee, Uttrakhand, 247667, India)
Pretrained Reversible Generation as Unsupervised Visual Representation Learning
Rongkun Xue, Jinouwen Zhang, Yazhe Niu, Dazhong Shen, Bingqi Ma, Yu Liu, Jing Yang
Towards a Mechanistic Explanation of Diffusion Model Generalization
Matthew Niedoba, Berend Zwartsenberg, Kevin Murphy, Frank Wood
ANDHRA Bandersnatch: Training Neural Networks to Predict Parallel Realities
Venkata Satya Sai Ajay Daliparthi
A spiking photonic neural network of 40.000 neurons, trained with rank-order coding for leveraging sparsity
Ria Talukder, Anas Skalli, Xavier Porte, Simon Thorpe, Daniel Brunner
GRU-PFG: Extract Inter-Stock Correlation from Stock Factors with Graph Neural Network
Yonggai Zhuang, Haoran Chen, Kequan Wang, Teng Fei
Boundary-Decoder network for inverse prediction of capacitor electrostatic analysis
Kart-Leong Lim, Rahul Dutta, Mihai Rotaru