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 - Page 11
Multiscale autonomous forecasting of plasma systems' dynamics using neural networks
Farbod Faraji, Maryam RezaA Critical Review of Predominant Bias in Neural Networks
Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu, Hanchen Xie, Mohamed E. Hussein, Wael AbdAlmageedNeural Networks Remember More: The Power of Parameter Isolation and Combination
Biqing Zeng, Zehan Li, Aladdin Ayesh
Learning Euler Factors of Elliptic Curves
Angelica Babei, François Charton, Edgar Costa, Xiaoyu Huang, Kyu-Hwan Lee, David Lowry-Duda, Ashvni Narayanan, Alexey PozdnyakovModern Hopfield Networks with Continuous-Time Memories
Saul Santos, António Farinhas, Daniel C. McNamee, André F.T. MartinsData Valuation using Neural Networks for Efficient Instruction Fine-Tuning
Ishika Agarwal, Dilek Hakkani-TurRecent Advances of NeuroDiffEq -- An Open-Source Library for Physics-Informed Neural Networks
Shuheng Liu, Pavlos Protopapas, David Sondak, Feiyu ChenOptimal lower Lipschitz bounds for ReLU layers, saturation, and phase retrieval
Daniel Freeman, Daniel Haider
ATM-Net: Adaptive Termination and Multi-Precision Neural Networks for Energy-Harvested Edge Intelligence
Neeraj Solanki, Sepehr Tabrizchi, Samin Sohrabi, Jason Schmidt, Arman RoohiApplication-oriented automatic hyperparameter optimization for spiking neural network prototyping
Vittorio FraDepth-Bounds for Neural Networks via the Braid Arrangement
Moritz Grillo, Christoph Hertrich, Georg LohoWhen do neural networks learn world models?
Tianren Zhang, Guanyu Chen, Feng ChenCFIRSTNET: Comprehensive Features for Static IR Drop Estimation with Neural Network
Yu-Tung Liu, Yu-Hao Cheng, Shao-Yu Wu, Hung-Ming Chen
Adaptive kernel predictors from feature-learning infinite limits of neural networks
Clarissa Lauditi, Blake Bordelon, Cengiz PehlevanAttention Learning is Needed to Efficiently Learn Parity Function
Yaomengxi Han, Debarghya GhoshdastidarMathematical reasoning and the computer
Kevin BuzzardLearnable Residual-based Latent Denoising in Semantic Communication
Mingkai Xu, Yongpeng Wu, Yuxuan Shi, Xiang-Gen Xia, Wenjun Zhang, Ping ZhangEnhancing Physics-Informed Neural Networks Through Feature Engineering
Shaghayegh Fazliani, Zachary Frangella, Madeleine UdellExploring Neural Network Pruning with Screening Methods
Mingyuan Wang, Yangzi Guo, Sida Liu, Yanwen Xiao