Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Gross, Sam"'
Publikováno v:
Journal of Machine Learning Research 21 (2020) 1-41
Current large-scale auto-regressive language models display impressive fluency and can generate convincing text. In this work we start by asking the question: Can the generations of these models be reliably distinguished from real text by statistical
Externí odkaz:
http://arxiv.org/abs/2004.10188
Autor:
Paszke, Adam, Gross, Sam, Massa, Francisco, Lerer, Adam, Bradbury, James, Chanan, Gregory, Killeen, Trevor, Lin, Zeming, Gimelshein, Natalia, Antiga, Luca, Desmaison, Alban, Köpf, Andreas, Yang, Edward, DeVito, Zach, Raison, Martin, Tejani, Alykhan, Chilamkurthy, Sasank, Steiner, Benoit, Fang, Lu, Bai, Junjie, Chintala, Soumith
Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that suppor
Externí odkaz:
http://arxiv.org/abs/1912.01703
Energy-based models (EBMs), a.k.a. un-normalized models, have had recent successes in continuous spaces. However, they have not been successfully applied to model text sequences. While decreasing the energy at training samples is straightforward, min
Externí odkaz:
http://arxiv.org/abs/1906.03351
Autor:
Ott, Myle, Edunov, Sergey, Baevski, Alexei, Fan, Angela, Gross, Sam, Ng, Nathan, Grangier, David, Auli, Michael
fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports distrib
Externí odkaz:
http://arxiv.org/abs/1904.01038
Publikováno v:
International Conference on Machine Learning (ICML), 2019
Counterfactual Regret Minimization (CFR) is the leading framework for solving large imperfect-information games. It converges to an equilibrium by iteratively traversing the game tree. In order to deal with extremely large games, abstraction is typic
Externí odkaz:
http://arxiv.org/abs/1811.00164
Training convolutional networks (CNN's) that fit on a single GPU with minibatch stochastic gradient descent has become effective in practice. However, there is still no effective method for training large CNN's that do not fit in the memory of a few
Externí odkaz:
http://arxiv.org/abs/1704.06363
We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator whose task is to fill in the hole, based on the surrounding pixels. The
Externí odkaz:
http://arxiv.org/abs/1611.06430
Autor:
Zagoruyko, Sergey, Lerer, Adam, Lin, Tsung-Yi, Pinheiro, Pedro O., Gross, Sam, Chintala, Soumith, Dollár, Piotr
The recent COCO object detection dataset presents several new challenges for object detection. In particular, it contains objects at a broad range of scales, less prototypical images, and requires more precise localization. To address these challenge
Externí odkaz:
http://arxiv.org/abs/1604.02135
Wooden blocks are a common toy for infants, allowing them to develop motor skills and gain intuition about the physical behavior of the world. In this paper, we explore the ability of deep feed-forward models to learn such intuitive physics. Using a
Externí odkaz:
http://arxiv.org/abs/1603.01312
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