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
ReducedLUT: Table Decomposition with "Don't Care" Conditions
Oliver Cassidy, Marta Andronic, Samuel Coward, George A. Constantinides
NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation Learning
Xiong Zhang, Cheng Xie, Haoran Duan, Beibei Yu
Parallel Neural Computing for Scene Understanding from LiDAR Perception in Autonomous Racing
Suwesh Prasad Sah
Understanding Artificial Neural Network's Behavior from Neuron Activation Perspective
Yizhou Zhang, Yang Sui
Explainability in Neural Networks for Natural Language Processing Tasks
Melkamu Mersha, Mingiziem Bitewa, Tsion Abay, Jugal Kalita
Optimization of Convolutional Neural Network Hyperparameter for Medical Image Diagnosis using Metaheuristic Algorithms: A short Recent Review (2019-2022)
Qusay Shihab Hamad, Hussein Samma, Shahrel Azmin Suandi
Symplectic Neural Flows for Modeling and Discovery
Priscilla Canizares, Davide Murari, Carola-Bibiane Schönlieb, Ferdia Sherry, Zakhar Shumaylov
Towards Selection and Transition Between Behavior-Based Neural Networks for Automated Driving
Iqra Aslam, Igor Anpilogov, Andreas Rausch
Coupling Neural Networks and Physics Equations For Li-Ion Battery State-of-Charge Prediction
Giovanni Pollo, Alessio Burrello, Enrico Macii, Massimo Poncino, Sara Vinco, Daniele Jahier Pagliari
Distributed Inference on Mobile Edge and Cloud: A Data-Cartography based Clustering Approach
Divya Jyoti Bajpai, Manjesh Kumar Hanawal
Learn2Mix: Training Neural Networks Using Adaptive Data Integration
Shyam Venkatasubramanian, Vahid Tarokh
Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
Govinda Anantha Padmanabha, Cosmin Safta, Nikolaos Bouklas, Reese E. Jones
CBNN: 3-Party Secure Framework for Customized Binary Neural Networks Inference
Benchang Dong, Zhili Chen, Xin Chen, Shiwen Wei, Jie Fu, Huifa Li
Event-based backpropagation on the neuromorphic platform SpiNNaker2
Gabriel Béna, Timo Wunderlich, Mahmoud Akl, Bernhard Vogginger, Christian Mayr, Hector Andres Gonzales
Generative AI for Banks: Benchmarks and Algorithms for Synthetic Financial Transaction Data
Fabian Sven Karst, Sook-Yee Chong, Abigail A. Antenor, Enyu Lin, Mahei Manhai Li, Jan Marco Leimeister
Efficient Few-Shot Neural Architecture Search by Counting the Number of Nonlinear Functions
Youngmin Oh, Hyunju Lee, Bumsub Ham
Alignment-Free RGB-T Salient Object Detection: A Large-scale Dataset and Progressive Correlation Network
Kunpeng Wang, Keke Chen, Chenglong Li, Zhengzheng Tu, Bin Luo
Is AI Robust Enough for Scientific Research?
Jun-Jie Zhang, Jiahao Song, Xiu-Cheng Wang, Fu-Peng Li, Zehan Liu, Jian-Nan Chen, Haoning Dang, Shiyao Wang, Yiyan Zhang, Jianhui Xu, Chunxiang Shi, Fei Wang, Long-Gang Pang, Nan Cheng, Weiwei Zhang, Duo Zhang, Deyu Meng