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
FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models
Zhongyu Zhao, Menghang Dong, Rongyu Zhang, Wenzhao Zheng, Yunpeng Zhang, Huanrui Yang, Dalong Du, Kurt Keutzer, Shanghang Zhang
EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logic
Victor Verreet, Lennert De Smet, Luc De Raedt, Emanuele Sansone
The Unreasonable Effectiveness of Solving Inverse Problems with Neural Networks
Philipp Holl, Nils Thuerey
Training Large-Scale Optical Neural Networks with Two-Pass Forward Propagation
Amirreza Ahmadnejad, Somayyeh Koohi
RandomNet: Clustering Time Series Using Untrained Deep Neural Networks
Xiaosheng Li, Wenjie Xi, Jessica Lin
Physics-Informed Neural Network for Predicting Out-of-Training-Range TCAD Solution with Minimized Domain Expertise
Albert Lu, Yu Foon Chau, Hiu Yung Wong
Graph neural network surrogate for strategic transport planning
Nikita Makarov, Santhanakrishnan Narayanan, Constantinos Antoniou
Operator Feature Neural Network for Symbolic Regression
Yusong Deng, Min Wu, Lina Yu, Jingyi Liu, Shu Wei, Yanjie Li, Weijun Li
DPSNN: Spiking Neural Network for Low-Latency Streaming Speech Enhancement
Tao Sun, Sander Bohté
Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics
Alireza Mousavi-Hosseini, Denny Wu, Murat A. Erdogdu
Event-Stream Super Resolution using Sigma-Delta Neural Network
Waseem Shariff, Joe Lemley, Peter Corcoran
Physics-informed graph neural networks for flow field estimation in carotid arteries
Julian Suk, Dieuwertje Alblas, Barbara A. Hutten, Albert Wiegman, Christoph Brune, Pim van Ooij, Jelmer M. Wolterink
Model Based and Physics Informed Deep Learning Neural Network Structures
Ali Mohammad-Djafari, Ning Chu, Li Wang, Caifang Cai, Liang Yu
Coherence Awareness in Diffractive Neural Networks
Matan Kleiner, Lior Michaeli, Tomer Michaeli
Hierarchical Structured Neural Network for Retrieval
Kaushik Rangadurai, Siyang Yuan, Minhui Huang, Yiqun Liu, Golnaz Ghasemiesfeh, Yunchen Pu, Xinfeng Xie, Xingfeng He, Fangzhou Xu, Andrew Cui, Vidhoon Viswanathan, Yan Dong, Liang Xiong, Lin Yang, Liang Wang, Jiyan Yang, Chonglin Sun
Deep Inertia $L_p$ Half-Quadratic Splitting Unrolling Network for Sparse View CT Reconstruction
Yu Guo, Caiying Wu, Yaxin Li, Qiyu Jin, Tieyong Zeng
Artificial Neural Network and Deep Learning: Fundamentals and Theory
M. M. Hammad
StringNET: Neural Network based Variational Method for Transition Pathways
Jiayue Han, Shuting Gu, Xiang Zhou
Neural Networks as Spin Models: From Glass to Hidden Order Through Training
Richard Barney, Michael Winer, Victor Galitski