776 citations · 2.2k across the 24 of their papers we have counts for
25 papers
Proximity Preserving Binary Code using Signed Graph-Cut
Inbal Lav, Shai Avidan, Yoram Singer +1
We introduce a binary embedding framework, called Proximity Preserving Code (PPC), which learns similarity and dissimilarity between data points to create a compact and affinity-pr…
Scalable Second Order Optimization for Deep Learning
Rohan Anil, Vineet Gupta, Tomer Koren +2
Optimization in machine learning, both theoretical and applied, is presently dominated by first-order gradient methods such as stochastic gradient descent. Second-order optimizatio…
Convolutional Bipartite Attractor Networks
Michael Iuzzolino, Yoram Singer, Michael C. Mozer
In human perception and cognition, a fundamental operation that brains perform is interpretation: constructing coherent neural states from noisy, incomplete, and intrinsically ambi…
Exponentiated Gradient Meets Gradient Descent
Udaya Ghai, Elad Hazan, Yoram Singer
The (stochastic) gradient descent and the multiplicative update method are probably the most popular algorithms in machine learning. We introduce and study a new regularization whi…
Identity Crisis: Memorization and Generalization under Extreme Overparameterization
Chiyuan Zhang, Samy Bengio, Moritz Hardt +2
We study the interplay between memorization and generalization of overparameterized networks in the extreme case of a single training example and an identity-mapping task. We exami…
Are All Layers Created Equal?
Chiyuan Zhang, Samy Bengio, Yoram Singer
Understanding deep neural networks is a major research objective with notable experimental and theoretical attention in recent years. The practical success of excessively large net…