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
Text classification optimization algorithm based on graph neural network
Erdi Gao, Haowei Yang, Dan Sun, Haohao Xia, Yuhan Ma, Yuanjing Zhu
SELD-Mamba: Selective State-Space Model for Sound Event Localization and Detection with Source Distance Estimation
Da Mu, Zhicheng Zhang, Haobo Yue, Zehao Wang, Jin Tang, Jianqin Yin
Fiber neural networks for the intelligent optical fiber communications
Yubin Zang, Zuxing Zhang, Simin Li, Fangzheng Zhang, Hongwei Chen
Activations Through Extensions: A Framework To Boost Performance Of Neural Networks
Chandramouli Kamanchi, Sumanta Mukherjee, Kameshwaran Sampath, Pankaj Dayama, Arindam Jati, Vijay Ekambaram, Dzung Phan
FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
Shiwei Li, Wenchao Xu, Haozhao Wang, Xing Tang, Yining Qi, Shijie Xu, Weihong Luo, Yuhua Li, Xiuqiang He, Ruixuan Li
Analysis of Argument Structure Constructions in a Deep Recurrent Language Model
Pegah Ramezani, Achim Schilling, Patrick Krauss
A Metric Driven Approach to Mixed Precision Training
Mitchelle Rasquinha, Gil Tabak
Efficient Graph Coloring with Neural Networks: A Physics-Inspired Approach for Large Graphs
Lorenzo Colantonio, Andrea Cacioppo, Federico Scarpati, Stefano Giagu
NeuralBeta: Estimating Beta Using Deep Learning
Yuxin Liu, Jimin Lin, Achintya Gopal
Hybrid Coordinate Descent for Efficient Neural Network Learning Using Line Search and Gradient Descent
Yen-Che Hsiao, Abhishek Dutta
UnifiedNN: Efficient Neural Network Training on the Cloud
Sifat Ut Taki, Arthi Padmanabhan, Spyridon Mastorakis
ZNorm: Z-Score Gradient Normalization for Deep Neural Networks
Juyoung Yun, Hoyoung Kim
Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation
Pedro Freire, Egor Manuylovich, Jaroslaw E. Prilepsky, Sergei K. Turitsy