10.8k citations
- Université de MontréalCA36 papers
- McGill UniversityCA25 papers
- Centre National de la Recherche ScientifiqueFR20 papers
- Stanford UniversityUS14 papers
- École de Technologie SupérieureCA13 papers
- Louisiana State UniversityUS13 papers
- Cornell UniversityUS12 papers
- Georgia Institute of TechnologyUS12 papers
- Sorbonne UniversitéFR12 papers
- Université Paris-SaclayFR12 papers
- University College LondonGB12 papers
- California Institute of TechnologyUS11 papers
4 papers · 2 filters
Understanding Interventional TreeSHAP : How and Why it Works
Gabriel Laberge, Yann Pequignot
Shapley values are ubiquitous in interpretable Machine Learning due to their strong theoretical background and efficient implementation in the SHAP library. Computing these values…
Careful What You Wish For: on the Extraction of Adversarially Trained Models
Kacem Khaled, Gabriela Nicolescu, Felipe Gohring de Magalhães
Recent attacks on Machine Learning (ML) models such as evasion attacks with adversarial examples and models stealing through extraction attacks pose several security and privacy th…
DiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment
Ahmed Haj Yahmed, Houssem Ben Braiek, Foutse Khomh +2
Quantization is one of the most applied Deep Neural Network (DNN) compression strategies, when deploying a trained DNN model on an embedded system or a cell phone. This is owing to…
MemSE: Fast MSE Prediction for Noisy Memristor-Based DNN Accelerators
Jonathan Kern, Sébastien Henwood, Gonçalo Mordido +4
Memristors enable the computation of matrix-vector multiplications (MVM) in memory and, therefore, show great potential in highly increasing the energy efficiency of deep neural ne…