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
Towards White Box Deep Learning
Maciej Satkiewicz
Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment
Atah Nuh Mih, Alireza Rahimi, Asfia Kawnine, Francis Palma, Monica Wachowicz, Rickey Dubay, Hung Cao
Analyzing Data Augmentation for Medical Images: A Case Study in Ultrasound Images
Adam Tupper, Christian Gagné
DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers
Xiao Ma, Shengfeng He, Hezhe Qiao, Dong Ma
Extracting Explanations, Justification, and Uncertainty from Black-Box Deep Neural Networks
Paul Ardis, Arjuna Flenner
DeepCSHAP: Utilizing Shapley Values to Explain Deep Complex-Valued Neural Networks
Florian Eilers, Xiaoyi Jiang
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models
Mohammad Lashkari, Amin Gheibi
Cyclic Data Parallelism for Efficient Parallelism of Deep Neural Networks
Louis Fournier, Edouard Oyallon
Advancing Security in AI Systems: A Novel Approach to Detecting Backdoors in Deep Neural Networks
Khondoker Murad Hossain, Tim Oates
Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations
Charles Edison Tripp, Jordan Perr-Sauer, Jamil Gafur, Amabarish Nag, Avi Purkayastha, Sagi Zisman, Erik A. Bensen
Robustifying and Boosting Training-Free Neural Architecture Search
Zhenfeng He, Yao Shu, Zhongxiang Dai, Bryan Kian Hsiang Low
Backdoor Attack with Mode Mixture Latent Modification
Hongwei Zhang, Xiaoyin Xu, Dongsheng An, Xianfeng Gu, Min Zhang
LookupFFN: Making Transformers Compute-lite for CPU inference
Zhanpeng Zeng, Michael Davies, Pranav Pulijala, Karthikeyan Sankaralingam, Vikas Singh