5 citations · 9 across the 5 of their papers we have counts for
6 papers
On the Importance of Gradient Norm in PAC-Bayesian Bounds
Itai Gat, Yossi Adi, Alexander Schwing +1
Generalization bounds which assess the difference between the true risk and the empirical risk, have been studied extensively. However, to obtain bounds, current techniques use str…
Towards a Common Speech Analysis Engine
Hagai Aronowitz, Itai Gat, Edmilson Morais +2
Recent innovations in self-supervised representation learning have led to remarkable advances in natural language processing. That said, in the speech processing domain, self-super…
Speech Emotion Recognition using Self-Supervised Features
Edmilson Morais, Ron Hoory, Weizhong Zhu +3
Self-supervised pre-trained features have consistently delivered state-of-art results in the field of natural language processing (NLP); however, their merits in the field of speec…
Perceptual Score: What Data Modalities Does Your Model Perceive?
Itai Gat, Idan Schwartz, Alexander Schwing
Machine learning advances in the last decade have relied significantly on large-scale datasets that continue to grow in size. Increasingly, those datasets also contain different da…
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions
Daniel Rosenberg, Itai Gat, Amir Feder +1
Deep learning algorithms have shown promising results in visual question answering (VQA) tasks, but a more careful look reveals that they often do not understand the rich signal th…
Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies
Itai Gat, Idan Schwartz, Alexander Schwing +1
Many recent datasets contain a variety of different data modalities, for instance, image, question, and answer data in visual question answering (VQA). When training deep net class…