Deep Neural Network
Deep neural networks (DNNs) are complex computational models aiming to mimic the human brain's learning capabilities, primarily focusing on achieving high accuracy and efficiency in various tasks. Current research emphasizes understanding DNN training dynamics, including phenomena like neural collapse and the impact of architectural choices (e.g., convolutional, transformer, and operator networks) and training strategies (e.g., weight decay, knowledge distillation, active learning). This understanding is crucial for improving DNN performance, robustness (including against adversarial attacks and noisy data), and resource efficiency in diverse applications ranging from image recognition and natural language processing to scientific modeling and edge computing.
Papers
Efficient model predictive control for nonlinear systems modelled by deep neural networks
Jianglin Lan
Two-Phase Dynamics of Interactions Explains the Starting Point of a DNN Learning Over-Fitted Features
Junpeng Zhang, Qing Li, Liang Lin, Quanshi Zhang
A Machine Learning Approach for Simultaneous Demapping of QAM and APSK Constellations
Arwin Gansekoele, Alexios Balatsoukas-Stimming, Tom Brusse, Mark Hoogendoorn, Sandjai Bhulai, Rob van der Mei
Spectral complexity of deep neural networks
Simmaco Di Lillo, Domenico Marinucci, Michele Salvi, Stefano Vigogna
Flexible image analysis for law enforcement agencies with deep neural networks to determine: where, who and what
Henri Bouma, Bart Joosten, Maarten C Kruithof, Maaike H T de Boer, Alexandru Ginsca, Benjamin Labbe, Quoc T Vuong
Exploring the Low-Pass Filtering Behavior in Image Super-Resolution
Haoyu Deng, Zijing Xu, Yule Duan, Xiao Wu, Wenjie Shu, Liang-Jian Deng
Enhancing Clinically Significant Prostate Cancer Prediction in T2-weighted Images through Transfer Learning from Breast Cancer
Chi-en Amy Tai, Alexander Wong
NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images
Matthew Keller, Chi-en Amy Tai, Yuhao Chen, Pengcheng Xi, Alexander Wong
Fast Training Data Acquisition for Object Detection and Segmentation using Black Screen Luminance Keying
Thomas Pöllabauer, Volker Knauthe, André Boller, Arjan Kuijper, Dieter Fellner
ReActXGB: A Hybrid Binary Convolutional Neural Network Architecture for Improved Performance and Computational Efficiency
Po-Hsun Chu, Ching-Han Chen
Reimplementation of Learning to Reweight Examples for Robust Deep Learning
Parth Patil, Ben Boardley, Jack Gardner, Emily Loiselle, Deerajkumar Parthipan
Fast Evaluation of DNN for Past Dataset in Incremental Learning
Naoto Sato
Exploring the Interplay of Interpretability and Robustness in Deep Neural Networks: A Saliency-guided Approach
Amira Guesmi, Nishant Suresh Aswani, Muhammad Shafique
DisBeaNet: A Deep Neural Network to augment Unmanned Surface Vessels for maritime situational awareness
Srikanth Vemula, Eulises Franco, Michael Frye
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks
Xue Geng, Zhe Wang, Chunyun Chen, Qing Xu, Kaixin Xu, Chao Jin, Manas Gupta, Xulei Yang, Zhenghua Chen, Mohamed M. Sabry Aly, Jie Lin, Min Wu, Xiaoli Li
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification
Joseph Smith, Zheming Zuo, Jonathan Stonehouse, Boguslaw Obara
Anole: Adapting Diverse Compressed Models For Cross-Scene Prediction On Mobile Devices
Yunzhe Li, Hongzi Zhu, Zhuohong Deng, Yunlong Cheng, Liang Zhang, Shan Chang, Minyi Guo