Zobrazeno 1 - 10
of 470
pro vyhledávání: '"Goodfellow, P J"'
Publikováno v:
NeurIPS 2018 Proceedings
Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain. SSL algorithms based on deep neural networks have recently proven successful on standard benchmark tasks. Howev
Externí odkaz:
http://arxiv.org/abs/1804.09170
Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results
Externí odkaz:
http://arxiv.org/abs/1412.6572
Training neural networks involves solving large-scale non-convex optimization problems. This task has long been believed to be extremely difficult, with fear of local minima and other obstacles motivating a variety of schemes to improve optimization,
Externí odkaz:
http://arxiv.org/abs/1412.6544
Autor:
Goodfellow, Ian J.
Two recently introduced criteria for estimation of generative models are both based on a reduction to binary classification. Noise-contrastive estimation (NCE) is an estimation procedure in which a generative model is trained to be able to distinguis
Externí odkaz:
http://arxiv.org/abs/1412.6515
Autor:
Goodfellow, Ian J., Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, Bengio, Yoshua
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability
Externí odkaz:
http://arxiv.org/abs/1406.2661
Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, many machine learning models "forget" how to perform the first task. This is widely believed to be a s
Externí odkaz:
http://arxiv.org/abs/1312.6211
The recently introduced dropout training criterion for neural networks has been the subject of much attention due to its simplicity and remarkable effectiveness as a regularizer, as well as its interpretation as a training procedure for an exponentia
Externí odkaz:
http://arxiv.org/abs/1312.6197
Recognizing arbitrary multi-character text in unconstrained natural photographs is a hard problem. In this paper, we address an equally hard sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from Street View imagery. Tradition
Externí odkaz:
http://arxiv.org/abs/1312.6082
Publikováno v:
Proc. AAAI 2014, pp. 1199-1205
Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in classical digital computers, are implemented using expensive MCMC. Physi
Externí odkaz:
http://arxiv.org/abs/1312.5258
Autor:
Goodfellow, Ian J., Warde-Farley, David, Lamblin, Pascal, Dumoulin, Vincent, Mirza, Mehdi, Pascanu, Razvan, Bergstra, James, Bastien, Frédéric, Bengio, Yoshua
Pylearn2 is a machine learning research library. This does not just mean that it is a collection of machine learning algorithms that share a common API; it means that it has been designed for flexibility and extensibility in order to facilitate resea
Externí odkaz:
http://arxiv.org/abs/1308.4214