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
Power side-channel leakage localization through adversarial training of deep neural networks
Jimmy Gammell, Anand Raghunathan, Kaushik Roy
Minimax optimality of deep neural networks on dependent data via PAC-Bayes bounds
Pierre Alquier, William Kengne
Dimensionality-induced information loss of outliers in deep neural networks
Kazuki Uematsu, Kosuke Haruki, Taiji Suzuki, Mitsuhiro Kimura, Takahiro Takimoto, Hideyuki Nakagawa
Enhancing CNN Classification with Lamarckian Memetic Algorithms and Local Search
Akhilbaran Ghosh, Rama Sai Adithya Kalidindi
CAVE: Classifying Abnormalities in Video Capsule Endoscopy
Ishita Harish, Saurav Mishra, Neha Bhadoria, Rithik Kumar, Madhav Arora, Syed Rameem Zahra, Ankur Gupta
ISDNN: A Deep Neural Network for Channel Estimation in Massive MIMO systems
Do Hai Son, Vu Tung Lam, Tran Thi Thuy Quynh
Emergence of Globally Attracting Fixed Points in Deep Neural Networks With Nonlinear Activations
Amir Joudaki, Thomas Hofmann
Learning the Regularization Strength for Deep Fine-Tuning via a Data-Emphasized Variational Objective
Ethan Harvey, Mikhail Petrov, Michael C. Hughes
DECADE: Towards Designing Efficient-yet-Accurate Distance Estimation Modules for Collision Avoidance in Mobile Advanced Driver Assistance Systems
Muhammad Zaeem Shahzad, Muhammad Abdullah Hanif, Muhammad Shafique
In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic Manipulators
Dmytro Humeniuk, Houssem Ben Braiek, Thomas Reid, Foutse Khomh
Subword Embedding from Bytes Gains Privacy without Sacrificing Accuracy and Complexity
Mengjiao Zhang, Jia Xu
Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning
Ronglong Fang, Yuesheng Xu
DeepVigor+: Scalable and Accurate Semi-Analytical Fault Resilience Analysis for Deep Neural Network
Mohammad Hasan Ahmadilivani, Jaan Raik, Masoud Daneshtalab, Maksim Jenihhin