3.6k citations · 10.6k across the 54 of their papers we have counts for
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Scalable Diffusion for Materials Generation
Sherry Yang, KwangHwan Cho, Amil Merchant +4
Generative models trained on internet-scale data are capable of generating novel and realistic texts, images, and videos. A natural next question is whether these models can advanc…
What does a deep neural network confidently perceive? The effective dimension of high certainty class manifolds and their low confidence boundaries
Stanislav Fort, Ekin Dogus Cubuk, Surya Ganguli +1
Deep neural network classifiers partition input space into high confidence regions for each class. The geometry of these class manifolds (CMs) is widely studied and intimately rela…
No One Representation to Rule Them All: Overlapping Features of Training Methods
Raphael Gontijo-Lopes, Yann Dauphin, Ekin D. Cubuk
Despite being able to capture a range of features of the data, high accuracy models trained with supervision tend to make similar predictions. This seemingly implies that high-perf…
Learn2Hop: Learned Optimization on Rough Landscapes
Amil Merchant, Luke Metz, Sam Schoenholz +1
Optimization of non-convex loss surfaces containing many local minima remains a critical problem in a variety of domains, including operations research, informatics, and material d…
Affinity and Diversity: Quantifying Mechanisms of Data Augmentation
Raphael Gontijo-Lopes, Sylvia J. Smullin, Ekin D. Cubuk +1
Though data augmentation has become a standard component of deep neural network training, the underlying mechanism behind the effectiveness of these techniques remains poorly under…
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li +6
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combin…