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
Optimization and Deployment of Deep Neural Networks for PPG-based Blood Pressure Estimation Targeting Low-power Wearables
Alessio Burrello, Francesco Carlucci, Giovanni Pollo, Xiaying Wang, Massimo Poncino, Enrico Macii, Luca Benini, Daniele Jahier Pagliari
Convolutional Networks as Extremely Small Foundation Models: Visual Prompting and Theoretical Perspective
Jianqiao Wangni
Beyond Unconstrained Features: Neural Collapse for Shallow Neural Networks with General Data
Wanli Hong, Shuyang Ling
CARIn: Constraint-Aware and Responsive Inference on Heterogeneous Devices for Single- and Multi-DNN Workloads
Ioannis Panopoulos, Stylianos I. Venieris, Iakovos S. Venieris
DNN-GDITD: Out-of-distribution detection via Deep Neural Network based Gaussian Descriptor for Imbalanced Tabular Data
Priyanka Chudasama, Anil Surisetty, Aakarsh Malhotra, Alok Singh
Trust And Balance: Few Trusted Samples Pseudo-Labeling and Temperature Scaled Loss for Effective Source-Free Unsupervised Domain Adaptation
Andrea Maracani, Lorenzo Rosasco, Lorenzo Natale
Optical training of large-scale Transformers and deep neural networks with direct feedback alignment
Ziao Wang, Kilian Müller, Matthew Filipovich, Julien Launay, Ruben Ohana, Gustave Pariente, Safa Mokaadi, Charles Brossollet, Fabien Moreau, Alessandro Cappelli, Iacopo Poli, Igor Carron, Laurent Daudet, Florent Krzakala, Sylvain Gigan
Deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery
Yuhan Zheng, Jessie A Elliott, John V Reynolds, Sheraz R Markar, Bartłomiej W. Papież, ENSURE study group
Deep Feature Embedding for Tabular Data
Yuqian Wu, Hengyi Luo, Raymond S. T. Lee
Improving Time Series Classification with Representation Soft Label Smoothing
Hengyi Ma, Weitong Chen
Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning
Momin Abbas, Koushik Kar, Tianyi Chen
Network transferability of adversarial patches in real-time object detection
Jens Bayer, Stefan Becker, David Münch, Michael Arens
Noise-to-mask Ratio Loss for Deep Neural Network based Audio Watermarking
Martin Moritz, Toni Olán, Tuomas Virtanen
An Embedding is Worth a Thousand Noisy Labels
Francesco Di Salvo, Sebastian Doerrich, Ines Rieger, Christian Ledig
Logic interpretations of ANN partition cells
Ingo Schmitt
Diminishing Domain Mismatch for DNN-Based Acoustic Distance Estimation via Stochastic Room Reverberation Models
Tobias Gburrek, Adrian Meise, Joerg Schmalenstroeer, Reinhold Haeb-Umbach