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
BitNet b1.58 Reloaded: State-of-the-art Performance Also on Smaller Networks
Jacob Nielsen, Peter Schneider-Kamp
Coding schemes in neural networks learning classification tasks
Alexander van Meegen, Haim Sompolinsky
Building on Efficient Foundations: Effectively Training LLMs with Structured Feedforward Layers
Xiuying Wei, Skander Moalla, Razvan Pascanu, Caglar Gulcehre
On Newton's Method to Unlearn Neural Networks
Nhung Bui, Xinyang Lu, Rachael Hwee Ling Sim, See-Kiong Ng, Bryan Kian Hsiang Low
LeYOLO, New Scalable and Efficient CNN Architecture for Object Detection
Lilian Hollard, Lucas Mohimont, Nathalie Gaveau, Luiz-Angelo Steffenel
Exploring Layerwise Adversarial Robustness Through the Lens of t-SNE
Inês Valentim, Nuno Antunes, Nuno Lourenço
Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition
Yang Wang, Haiyang Mei, Qirui Bao, Ziqi Wei, Mike Zheng Shou, Haizhou Li, Bo Dong, Xin Yang
Encoder-Decoder Neural Networks in Interpretation of X-ray Spectra
Jalmari Passilahti, Anton Vladyka, Johannes Niskanen
Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture
Zhengxin Yang, Wanling Gao, Luzhou Peng, Yunyou Huang, Fei Tang, Jianfeng Zhan
A Primal-Dual Framework for Transformers and Neural Networks
Tan M. Nguyen, Tam Nguyen, Nhat Ho, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher
Q-SNNs: Quantized Spiking Neural Networks
Wenjie Wei, Yu Liang, Ammar Belatreche, Yichen Xiao, Honglin Cao, Zhenbang Ren, Guoqing Wang, Malu Zhang, Yang Yang
GraphKAN: Enhancing Feature Extraction with Graph Kolmogorov Arnold Networks
Fan Zhang, Xin Zhang
Trusted Video Inpainting Localization via Deep Attentive Noise Learning
Zijie Lou, Gang Cao, Man Lin