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
Approximation of the Proximal Operator of the $\ell_\infty$ Norm Using a Neural Network
Kathryn Linehan, Radu Balan
Makeup-Guided Facial Privacy Protection via Untrained Neural Network Priors
Fahad Shamshad, Muzammal Naseer, Karthik Nandakumar
Atmospheric Transport Modeling of CO$_2$ with Neural Networks
Vitus Benson, Ana Bastos, Christian Reimers, Alexander J. Winkler, Fanny Yang, Markus Reichstein
Learning Regularization for Graph Inverse Problems
Moshe Eliasof, Md Shahriar Rahim Siddiqui, Carola-Bibiane Schönlieb, Eldad Haber
Modeling Human Strategy for Flattening Wrinkled Cloth Using Neural Networks
Nilay Kant, Ashrut Aryal, Rajiv Ranganathan, Ranjan Mukherjee, Charles Owen
Query languages for neural networks
Martin Grohe, Christoph Standke, Juno Steegmans, Jan Van den Bussche
Parseval Convolution Operators and Neural Networks
Michael Unser, Stanislas Ducotterd
Symplectic Neural Networks Based on Dynamical Systems
Benjamin K Tapley
Neural Horizon Model Predictive Control -- Increasing Computational Efficiency with Neural Networks
Hendrik Alsmeier, Anton Savchenko, Rolf Findeisen
Parallel-in-Time Solutions with Random Projection Neural Networks
Marta M. Betcke, Lisa Maria Kreusser, Davide Murari
PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks
Xiyue Zhang, Benjie Wang, Marta Kwiatkowska, Huan Zhang
Temporal Reversed Training for Spiking Neural Networks with Generalized Spatio-Temporal Representation
Lin Zuo, Yongqi Ding, Wenwei Luo, Mengmeng Jing, Xianlong Tian, Kunshan Yang
Data-Driven Fire Modeling: Learning First Arrival Times and Model Parameters with Neural Networks
Xin Tong, Bryan Quaife
LEVIS: Large Exact Verifiable Input Spaces for Neural Networks
Mohamad Fares El Hajj Chehade, Brian Wesley Bell, Russell Bent, Hao Zhu, Wenting Li
NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance
Raphael T. Husistein, Markus Reiher, Marco Eckhoff
Case Study: Runtime Safety Verification of Neural Network Controlled System
Frank Yang, Sinong Simon Zhan, Yixuan Wang, Chao Huang, Qi Zhu
FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models
Zhongyu Zhao, Menghang Dong, Rongyu Zhang, Wenzhao Zheng, Yunpeng Zhang, Huanrui Yang, Dalong Du, Kurt Keutzer, Shanghang Zhang
EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logic
Victor Verreet, Lennert De Smet, Luc De Raedt, Emanuele Sansone
The Unreasonable Effectiveness of Solving Inverse Problems with Neural Networks
Philipp Holl, Nils Thuerey