5 papers · 1 filter
Simplicity Suffices for Parameter Noise Injection in Stochastic Gradient Descent
Benjamin Leblanc, Louis-Jacob Lebel, Teddy Kana +1
Injecting noise into the optimization process is a well-established technique for improving the training and generalization of deep neural networks. Yet, despite the breadth of exi…
A Framework for Bounding Deterministic Risk with PAC-Bayes: Applications to Majority Votes
Benjamin Leblanc, Pascal Germain
PAC-Bayes is a popular and efficient framework for obtaining generalization guarantees in situations involving uncountable hypothesis spaces. Unfortunately, in its classical formul…
Generalization Bounds via Meta-Learned Model Representations: PAC-Bayes and Sample Compression Hypernetworks
Benjamin Leblanc, Mathieu Bazinet, Nathaniel D'Amours +2
Both PAC-Bayesian and Sample Compress learning frameworks are instrumental for deriving tight (non-vacuous) generalization bounds for neural networks. We leverage these results in…
Seeking Interpretability and Explainability in Binary Activated Neural Networks
Benjamin Leblanc, Pascal Germain
We study the use of binary activated neural networks as interpretable and explainable predictors in the context of regression tasks on tabular data; more specifically, we provide g…
On the Relationship Between Interpretability and Explainability in Machine Learning
Benjamin Leblanc, Pascal Germain
Interpretability and explainability have gained more and more attention in the field of machine learning as they are crucial when it comes to high-stakes decisions and troubleshoot…