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
Theoretical characterisation of the Gauss-Newton conditioning in Neural Networks
Jim Zhao, Sidak Pal Singh, Aurelien Lucchi
Typicalness-Aware Learning for Failure Detection
Yijun Liu, Jiequan Cui, Zhuotao Tian, Senqiao Yang, Qingdong He, Xiaoling Wang, Jingyong Su
Fairness-Utilization Trade-off in Wireless Networks with Explainable Kolmogorov-Arnold Networks
Masoud Shokrnezhad, Hamidreza Mazandarani, Tarik Taleb
Improving DNN Modularization via Activation-Driven Training
Tuan Ngo, Abid Hassan, Saad Shafiq, Nenad Medvidovic
B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable
Shreyash Arya, Sukrut Rao, Moritz Böhle, Bernt Schiele
Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks
Yuyan Zhang, Derya Soydaner, Fatemeh Behrad, Lisa Koßmann, Johan Wagemans
Using Deep Neural Networks to Quantify Parking Dwell Time
Marcelo Eduardo Marques Ribas (1), Heloisa Benedet Mendes (1), Luiz Eduardo Soares de Oliveira (1), Luiz Antonio Zanlorensi (2), Paulo Ricardo Lisboa de Almeida (1) ((1) Department of Informatics - Federal University of Paraná, (2) DeepNeuronic)
EXACFS -- A CIL Method to mitigate Catastrophic Forgetting
S Balasubramanian, M Sai Subramaniam, Sai Sriram Talasu, P Yedu Krishna, Manepalli Pranav Phanindra Sai, Ravi Mukkamala, Darshan Gera
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-Net: 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