15 papers
Predicting Only from Selected Evidence: A Tempered Product-of-Experts Bottleneck for Auditable EEG Diagnosis
Yinghao Wang, Shujian Yu, Duc Han Le +3
Pretrained EEG backbones improve transfer performance, but downstream diagnosis heads remain hard to audit: predictions are made from unrestricted hidden states, whereas explanatio…
How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection
Lorenzo Guerra, Thomas Chapuis, Guillaume Duc +2
Provenance-based intrusion detection systems (PIDS) frequently report strong performance, but the conclusions drawn from these results can be highly sensitive to benchmarking choic…
Efficient Resource-Constrained Training of Transformers via Subspace Optimization
Le-Trung Nguyen, Enzo Tartaglione, Van-Tam Nguyen
As AI increasingly shapes daily life, energy consumption and data privacy have become pressing concerns. On-device learning trains models directly on edge devices, cutting energy c…
Layer Collapse Can be Induced by Unstructured Pruning
Zhu Liao, Victor Quétu, Van-Tam Nguyen +1
Unstructured pruning is a popular compression method for efficiently reducing model parameters. However, while it effectively decreases the number of parameters, it is commonly bel…
Study of Training Dynamics for Memory-Constrained Fine-Tuning
Aël Quélennec, Nour Hezbri, Pavlo Mozharovskyi +2
Memory-efficient training of deep neural networks has become increasingly important as models grow larger while deployment environments impose strict resource constraints. We propo…
HFMCA: Orthonormal Feature Learning for EEG-based Brain Decoding
Yinghao Wang, Lintao Xu, Shujian Yu +2
Electroencephalography (EEG) analysis is critical for brain-computer interfaces and neuroscience, but the intrinsic noise and high dimensionality of EEG signals hinder effective fe…