Unsupervised or Indirectly Supervised Learning
MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin A. Raffel
Neural Information Processing Systems http://nips.cc/
Author Response for The Unreasonable Effectiveness of Big Models for Semi Supervised Learning
We thank the reviewers for feedback, as well as efforts in reviewing. We respond to each comment below. Overall, there is no significant contribution to unsupervised pre-training. " The fact that our main contribution is a detailed procedure, rather than a theorem, architecture, or other artifact, We believe our contributions are significant. Indeed, R3 recognizes that "the simple semi-supervised framework is still I think it will inspire several future works." " While we believe ImageNet is a much more These results can be further improved with better augmentations during fine-tuning and an extra distillation step.
Particle Cloud Generationwith Message Passing Generative Adversarial Networks
Thepolarangle isusedto computethepseudorapidity = log ( tan ( /2)).
Learning
Whiletheseapproaches arewidely used inpractice andachieveimpressiveempirical gains, their theoretical understanding largely lags behind. Towards bridging this gap, we present a unifying perspectivewhere several such approaches can beviewed asimposing a regularization on the representation via alearnable function using unlabeled data. Wepropose adiscriminativetheoretical framework for analyzing the sample complexity of these approaches, which generalizes the framework of [3] to allow learnable regularization functions.
