163 citations · 1.4k across the 139 of their papers we have counts for
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Benchmarking Deep Learning Interpretability in Time Series Predictions
Aya Abdelsalam Ismail, Mohamed Gunady, Héctor Corrada Bravo +1
Saliency methods are used extensively to highlight the importance of input features in model predictions. These methods are mostly used in vision and language tasks, and their appl…
Certifying Confidence via Randomized Smoothing
Aounon Kumar, Alexander Levine, Soheil Feizi +1
Randomized smoothing has been shown to provide good certified-robustness guarantees for high-dimensional classification problems. It uses the probabilities of predicting the top tw…
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation
Yogesh Balaji, Rama Chellappa, Soheil Feizi
Optimal Transport (OT) distances such as Wasserstein have been used in several areas such as GANs and domain adaptation. OT, however, is very sensitive to outliers (samples with la…
Tight Second-Order Certificates for Randomized Smoothing
Alexander Levine, Aounon Kumar, Thomas Goldstein +1
Randomized smoothing is a popular way of providing robustness guarantees against adversarial attacks: randomly-smoothed functions have a universal Lipschitz-like bound, allowing fo…
Winning Lottery Tickets in Deep Generative Models
Neha Mukund Kalibhat, Yogesh Balaji, Soheil Feizi
The lottery ticket hypothesis suggests that sparse, sub-networks of a given neural network, if initialized properly, can be trained to reach comparable or even better performance t…
GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue
Pirazh Khorramshahi, Hossein Souri, Rama Chellappa +1
Building on the success of deep learning, Generative Adversarial Networks (GANs) provide a modern approach to learn a probability distribution from observed samples. GANs are often…