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 - Page 3
Theory-to-Practice Gap for Neural Networks and Neural Operators
Philipp Grohs, Samuel Lanthaler, Margaret TrautnerUniversity of Vienna●California Institute of TechnologyAdaptive Physics-informed Neural Networks: A Survey
Edgar Torres, Jonathan Schiefer, Mathias NiepertUniversity of Stuttgart●IMPRS-IS●Robert Bosch GmbH●NEC Labs Europe●SimTechSNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation
Hui Xue, Sarah M. Hooper, Iain Pierce, Rhodri H. Davies, John Stairs, Joseph Naegele, Adrienne E. Campbell-Washburn, Charlotte Manisty+4Health Futures●National Institutes of Health●University College London●Barts Health NHS Trust
PT-PINNs: A Parametric Engineering Turbulence Solver based on Physics-Informed Neural Networks
Liang Jiang, Yuzhou Cheng, Kun Luo, Jianren FanZhejiang University●Shanghai Institute for Advanced Study of Zhejiang UniversityEnhancing Martian Terrain Recognition with Deep Constrained Clustering
Tejas Panambur, Mario ParenteUniversity of Massachusetts
Towards Understanding the Benefits of Neural Network Parameterizations in Geophysical Inversions: A Study With Neural Fields
Anran Xu, Lindsey J. HeagyUniversity of British ColumbiaSemi-supervised Cervical Segmentation on Ultrasound by A Dual Framework for Neural Networks
Fangyijie Wang, Kathleen M. Curran, Guénolé SilvestreTaighde ´Eireann – Research Ireland Centre for Research Training in Machine Learning●University College Dublin●University College DublinModel-free front-to-end training of a large high performance laser neural network
Anas Skalli, Satoshi Sunada, Mirko Goldmann, Marcin Gebski, Stephan Reitzenstein, James A. Lott, Tomasz Czyszanowski, Daniel BrunnerCNRS UMR●Kanazawa University●Lodz University of Technology●Technical University of BerlinDebugging and Runtime Analysis of Neural Networks with VLMs (A Case Study)
Boyue Caroline Hu, Divya Gopinath, Corina S. Pasareanu, Nina Narodytska, Ravi Mangal, Susmit Jha
Depth Matters: Multimodal RGB-D Perception for Robust Autonomous Agents
Mihaela-Larisa Clement, Mónika Farsang, Felix Resch, Radu GrosuTechnische Universit ¨at Wien (TU Wien)Universal approximation property of neural stochastic differential equations
Anna P. Kwossek, David J. Prömel, Josef TeichmannQCPINN: Quantum Classical Physics-Informed Neural Networks for Solving PDEs
Afrah Farea, Saiful Khan, Mustafa Serdar CelebiIstanbul Technical University●Rutherford Appleton LaboratoryNeural Networks: According to the Principles of Grassmann Algebra
Z. Zarezadeh, N. ZarezadehUniversity of Rome Tor Vergata●Allameh Tabataba'i UniversityOn the Cone Effect in the Learning Dynamics
Zhanpeng Zhou, Yongyi Yang, Jie Ren, Mahito Sugiyama, Junchi YanShanghai Jiao Tong University●University of Michigan●National Institute of Informatics●SOKENDAISenseExpo: Efficient Autonomous Exploration with Prediction Information from Lightweight Neural Networks
Haojia Gao, Haohua Que, Hoiian Au, Weihao Shan, Mingkai Liu, Yusen Qin, Lei Mu, Rong Zhao, Xinghua Yang, Qi Wei, Fei QiaoBeijing University of Technology●Tsinghua University●Peking University●D-robotics●Beijing Forestry UniversityDnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup Tables
Sidi Yang, Binxiao Huang, Yulun Zhang, Dahai Yu, Yujiu Yang, Ngai WongThe University of Hong Kong●Shanghai Jiaotong University●TCL Corporate Research●Tsinghua University
Natural Quantization of Neural Networks
Richard Barney, Djamil Lakhdar-Hamina, Victor GalitskiUniversity of MarylandMixed precision accumulation for neural network inference guided by componentwise forward error analysis
El-Mehdi El Arar, Silviu-Ioan Filip (TARAN), Theo Mary (PEQUAN), Elisa Riccietti (ENS de Lyon)Universit´e de Rennes●LIP6●Universit´e Claude Bernard Lyon 1 LIPDynamic Power Flow Analysis and Fault Characteristics: A Graph Attention Neural Network
Tan Le, Van LeHampton University●Advanced Technology Center