M. Caccia

Beyond Trivial Counterfactual Generations with Diverse Valuable Explanations

2021

Explainability of machine learning models has gained considerable attention within our research community given the importance of deploying more reliable machine-learning systems. Explanability can also be helpful for model debugging. In computer vision applications, most …

Learning where to learn: Gradient sparsity in meta and continual learning

2021 · Zurich Open Repository and Archive (University of Zurich)

Finding neural network weights that generalize well from small datasets is difficult. A promising approach is to learn a weight initialization such that a small number of weight changes results in low generalization error. We …

Language GANs Falling Short

2020 · International Conference on Learning Representations

Traditional natural language generation (NLG) models are trained using maximum likelihood estimation (MLE) which differs from the sample generation inference procedure. During training the ground truth tokens are passed to the model, however, during inference, …