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
A Spacetime Perspective on Dynamical Computation in Neural Information Processing Systems
T. Anderson Keller, Lyle Muller, Terrence J. Sejnowski, Max Welling
Efficient Visualization of Neural Networks with Generative Models and Adversarial Perturbations
Athanasios Karagounis
Relationship between Uncertainty in DNNs and Adversarial Attacks
Abigail Adeniran, Adewale Adeyemo
Hidden Activations Are Not Enough: A General Approach to Neural Network Predictions
Samuel Leblanc, Aiky Rasolomanana, Marco Armenta
FaFeSort: A Fast and Few-shot End-to-end Neural Network for Multi-channel Spike Sorting
Yuntao Han, Shiwei Wang
Universal approximation theorem for neural networks with inputs from a topological vector space
Vugar Ismailov
The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning
Manh V. Nguyen, Liang Zhao, Bobin Deng, William Severa, Honghui Xu, Shaoen Wu
A dynamic vision sensor object recognition model based on trainable event-driven convolution and spiking attention mechanism
Peng Zheng, Qian Zhou
ConvexECG: Lightweight and Explainable Neural Networks for Personalized, Continuous Cardiac Monitoring
Rayan Ansari, John Cao, Sabyasachi Bandyopadhyay, Sanjiv M. Narayan, Albert J. Rogers, Mert Pilanci
Neural Networks Generalize on Low Complexity Data
Sourav Chatterjee, Timothy Sudijono
Controllable Shape Modeling with Neural Generalized Cylinder
Xiangyu Zhu, Zhiqin Chen, Ruizhen Hu, Xiaoguang Han
A constrained optimization approach to improve robustness of neural networks
Shudian Zhao, Jan Kronqvist
Conformal Fields from Neural Networks
James Halverson, Joydeep Naskar, Jiahua Tian
Cartan moving frames and the data manifolds
Eliot Tron, Rita Fioresi, Nicolas Couellan, Stéphane Puechmorel
Hard-Label Cryptanalytic Extraction of Neural Network Models
Yi Chen, Xiaoyang Dong, Jian Guo, Yantian Shen, Anyu Wang, Xiaoyun Wang