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
Opening the Black Box: predicting the trainability of deep neural networks with reconstruction entropy
Yanick Thurn, Ro Jefferson, Johanna Erdmenger
Neural networks in non-metric spaces
Luca Galimberti
Jacobian-Enhanced Neural Networks
Steven H. Berguin
LaCoOT: Layer Collapse through Optimal Transport
Victor Quétu, Nour Hezbri, Enzo Tartaglione
MFF-EINV2: Multi-scale Feature Fusion across Spectral-Spatial-Temporal Domains for Sound Event Localization and Detection
Da Mu, Zhicheng Zhang, Haobo Yue
HDNet: Physics-Inspired Neural Network for Flow Estimation based on Helmholtz Decomposition
Miao Qi, Ramzi Idoughi, Wolfgang Heidrich
A Mathematical Certification for Positivity Conditions in Neural Networks with Applications to Partial Monotonicity and Ethical AI
Alejandro Polo-Molina, David Alfaya, Jose Portela
Self-Distillation Learning Based on Temporal-Spatial Consistency for Spiking Neural Networks
Lin Zuo, Yongqi Ding, Mengmeng Jing, Kunshan Yang, Yunqian Yu
Hierarchical Neural Networks, p-Adic PDEs, and Applications to Image Processing
W. A. Zúñiga-Galindo, B. A. Zambrano-Luna, Baboucarr Dibba
Generalized W-Net: Arbitrary-style Chinese Character Synthesization
Haochuan Jiang, Guanyu Yang, Fei Cheng, Kaizhu Huang
Error Analysis and Numerical Algorithm for PDE Approximation with Hidden-Layer Concatenated Physics Informed Neural Networks
Yianxia Qian, Yongchao Zhang, Suchuan Dong
NeuroMoCo: A Neuromorphic Momentum Contrast Learning Method for Spiking Neural Networks
Yuqi Ma, Huamin Wang, Hangchi Shen, Xuemei Chen, Shukai Duan, Shiping Wen
W-Net: One-Shot Arbitrary-Style Chinese Character Generation with Deep Neural Networks
Haochuan Jiang, Guanyu Yang, Kaizhu Huang, Rui Zhang
Information Geometry of Evolution of Neural Network Parameters While Training
Abhiram Anand Thiruthummal, Eun-jin Kim, Sergiy Shelyag
Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at Initialization
Avrajit Ghosh, Xitong Zhang, Kenneth K. Sun, Qing Qu, Saiprasad Ravishankar, Rongrong Wang
Adapting Physics-Informed Neural Networks To Optimize ODEs in Mosquito Population Dynamics
Dinh Viet Cuong, Branislava Lalić, Mina Petrić, Binh Nguyen, Mark Roantree