17.9k citations · 79.2k across the 64 of their papers we have counts for
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Towards Principled Unsupervised Learning
Ilya Sutskever, Rafal Jozefowicz, Karol Gregor +3
General unsupervised learning is a long-standing conceptual problem in machine learning. Supervised learning is successful because it can be solved by the minimization of the train…
Adding Gradient Noise Improves Learning for Very Deep Networks
Arvind Neelakantan, Luke Vilnis, Quoc V. Le +4
Deep feedforward and recurrent networks have achieved impressive results in many perception and language processing applications. This success is partially attributed to architectu…
Neural GPUs Learn Algorithms
Łukasz Kaiser, Ilya Sutskever
Learning an algorithm from examples is a fundamental problem that has been widely studied. Recently it has been addressed using neural networks, in particular by Neural Turing Mach…
Neural Random-Access Machines
Karol Kurach, Marcin Andrychowicz, Ilya Sutskever
In this paper, we propose and investigate a new neural network architecture called Neural Random Access Machine. It can manipulate and dereference pointers to an external variable-…
Multi-task Sequence to Sequence Learning
Minh-Thang Luong, Quoc V. Le, Ilya Sutskever +2
Sequence to sequence learning has recently emerged as a new paradigm in supervised learning. To date, most of its applications focused on only one task and not much work explored t…
MuProp: Unbiased Backpropagation for Stochastic Neural Networks
Shixiang Gu, Sergey Levine, Ilya Sutskever +1
Deep neural networks are powerful parametric models that can be trained efficiently using the backpropagation algorithm. Stochastic neural networks combine the power of large param…
A Neural Transducer
Navdeep Jaitly, David Sussillo, Quoc V. Le +3
Sequence-to-sequence models have achieved impressive results on various tasks. However, they are unsuitable for tasks that require incremental predictions to be made as more data a…
Neural Programmer: Inducing Latent Programs with Gradient Descent
Arvind Neelakantan, Quoc V. Le, Ilya Sutskever
Deep neural networks have achieved impressive supervised classification performance in many tasks including image recognition, speech recognition, and sequence to sequence learning…
Reinforcement Learning Neural Turing Machines - Revised
Wojciech Zaremba, Ilya Sutskever
The Neural Turing Machine (NTM) is more expressive than all previously considered models because of its external memory. It can be viewed as a broader effort to use abstract extern…