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
Universal Quantum Tomography With Deep Neural Networks
Nhan T. Luu, Thang C. Truong, Duong T. Luu
Statistical signatures of abstraction in deep neural networks
Carlo Orientale Caputo, Matteo Marsili
Formal Verification of Deep Neural Networks for Object Detection
Yizhak Y. Elboher, Avraham Raviv, Yael Leibovich Weiss, Omer Cohen, Roy Assa, Guy Katz, Hillel Kugler
Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural Networks
Beatrice Alessandra Motetti, Matteo Risso, Alessio Burrello, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks
Tianhao Wei, Luca Marzari, Kai S. Yun, Hanjiang Hu, Peizhi Niu, Xusheng Luo, Changliu Liu
TEAL: New Selection Strategy for Small Buffers in Experience Replay Class Incremental Learning
Shahar Shaul-Ariel, Daphna Weinshall
Malaria Cell Detection Using Deep Neural Networks
Saurabh Sawant, Anurag Singh
Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness
Erh-Chung Chen, Pin-Yu Chen, I-Hsin Chung, Che-Rung Lee
FRED: Flexible REduction-Distribution Interconnect and Communication Implementation for Wafer-Scale Distributed Training of DNN Models
Saeed Rashidi, William Won, Sudarshan Srinivasan, Puneet Gupta, Tushar Krishna
Spiking Convolutional Neural Networks for Text Classification
Changze Lv, Jianhan Xu, Xiaoqing Zheng
Dimensions underlying the representational alignment of deep neural networks with humans
Florian P. Mahner, Lukas Muttenthaler, Umut Güçlü, Martin N. Hebart
Autoencoder based approach for the mitigation of spurious correlations
Srinitish Srinivasan, Karthik Seemakurthy
Quantum-tunnelling deep neural networks for sociophysical neuromorphic AI
Ivan S. Maksymov
Sparse deep neural networks for nonparametric estimation in high-dimensional sparse regression
Dongya Wu, Xin Li
Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization
Yaoyu Zhang, Leyang Zhang, Zhongwang Zhang, Zhiwei Bai