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
Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning
David Schulte, Felix Hamborg, Alan Akbik
NeuralMAG: Fast and Generalizable Micromagnetic Simulation with Deep Neural Nets
Yunqi Cai, Jiangnan Li, Dong Wang
GL-NeRF: Gauss-Laguerre Quadrature Enables Training-Free NeRF Acceleration
Silong Yong, Yaqi Xie, Simon Stepputtis, Katia Sycara
Water quality polluted by total suspended solids classified within an Artificial Neural Network approach
I. Luviano Soto, Y. Concha Sánchez, A. Raya
Parallel Backpropagation for Inverse of a Convolution with Application to Normalizing Flows
Sandeep Nagar, Girish Varma
How Does Data Diversity Shape the Weight Landscape of Neural Networks?
Yang Ba, Michelle V. Mancenido, Rong Pan
Large Language Models Are Overparameterized Text Encoders
Thennal D K, Tim Fischer, Chris Biemann
How Do Training Methods Influence the Utilization of Vision Models?
Paul Gavrikov, Shashank Agnihotri, Margret Keuper, Janis Keuper
Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks
Vasileios Vatellis
Universal approximation results for neural networks with non-polynomial activation function over non-compact domains
Ariel Neufeld, Philipp Schmocker
Self Supervised Deep Learning for Robot Grasping
Danyal Saqib, Wajahat Hussain
Scaling Wearable Foundation Models
Girish Narayanswamy, Xin Liu, Kumar Ayush, Yuzhe Yang, Xuhai Xu, Shun Liao, Jake Garrison, Shyam Tailor, Jake Sunshine, Yun Liu, Tim Althoff, Shrikanth Narayanan, Pushmeet Kohli, Jiening Zhan, Mark Malhotra, Shwetak Patel, Samy Abdel-Ghaffar, Daniel McDuff
Partially Trained Graph Convolutional Networks Resist Oversmoothing
Dimitrios Kelesis, Dimitris Fotakis, Georgios Paliouras
PiLocNet: Physics-informed neural network on 3D localization with rotating point spread function
Mingda Lu, Zitian Ao, Chao Wang, Sudhakar Prasad, Raymond H. Chan
FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression
Zhenheng Tang, Xueze Kang, Yiming Yin, Xinglin Pan, Yuxin Wang, Xin He, Qiang Wang, Rongfei Zeng, Kaiyong Zhao, Shaohuai Shi, Amelie Chi Zhou, Bo Li, Bingsheng He, Xiaowen Chu
Towards Arbitrary QUBO Optimization: Analysis of Classical and Quantum-Activated Feedforward Neural Networks
Chia-Tso Lai, Carsten Blank, Peter Schmelcher, Rick Mukherjee
Loss Landscape Characterization of Neural Networks without Over-Parametrziation
Rustem Islamov, Niccolò Ajroldi, Antonio Orvieto, Aurelien Lucchi
Stable Diffusion with Continuous-time Neural Network
Andras Horvath
LPUF-AuthNet: A Lightweight PUF-Based IoT Authentication via Tandem Neural Networks and Split Learning
Brahim Mefgouda, Raviha Khan, Omar Alhussein, Hani Saleh, Hossien B. Eldeeb, Anshul Pandey, Sami Muhaidat