14.1k citations · 22.2k across the 18 of their papers we have counts for
17 papers · 1 filter
Gaussian-Bernoulli RBMs Without Tears
Renjie Liao, Simon Kornblith, Mengye Ren +2
We revisit the challenging problem of training Gaussian-Bernoulli restricted Boltzmann machines (GRBMs), introducing two innovations. We propose a novel Gibbs-Langevin sampling alg…
Teaching with Commentaries
Aniruddh Raghu, Maithra Raghu, Simon Kornblith +2
Effective training of deep neural networks can be challenging, and there remain many open questions on how to best learn these models. Recently developed methods to improve neural…
Big Self-Supervised Models are Strong Semi-Supervised Learners
Ting Chen, Simon Kornblith, Kevin Swersky +2
One paradigm for learning from few labeled examples while making best use of a large amount of unlabeled data is unsupervised pretraining followed by supervised fine-tuning. Althou…
Deflecting Adversarial Attacks
Yao Qin, Nicholas Frosst, Colin Raffel +2
There has been an ongoing cycle where stronger defenses against adversarial attacks are subsequently broken by a more advanced defense-aware attack. We present a new approach towar…
A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi +1
This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms wit…
Subclass Distillation
Rafael Müller, Simon Kornblith, Geoffrey Hinton
After a large "teacher" neural network has been trained on labeled data, the probabilities that the teacher assigns to incorrect classes reveal a lot of information about the way i…