1.6k citations · 11.3k across the 73 of their papers we have counts for
9 papers · 1 filter
Neural Combinatorial Optimization with Reinforcement Learning
Irwan Bello, Hieu Pham, Quoc V. Le +2
This paper presents a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning. We focus on the traveling salesman problem (TSP) and…
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt +2
Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance. Conventional wisdom attribut…
Adversarial Machine Learning at Scale
Alexey Kurakin, Ian Goodfellow, Samy Bengio
Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks wit…
Can Active Memory Replace Attention?
Łukasz Kaiser, Samy Bengio
Several mechanisms to focus attention of a neural network on selected parts of its input or memory have been used successfully in deep learning models in recent years. Attention ha…
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
Oriol Vinyals, Alexander Toshev, Samy Bengio +1
Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, w…
Reward Augmented Maximum Likelihood for Neural Structured Prediction
Mohammad Norouzi, Samy Bengio, Zhifeng Chen +4
A key problem in structured output prediction is direct optimization of the task reward function that matters for test evaluation. This paper presents a simple and computationally…
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, Samy Bengio
Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in…
Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio
Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference…
Revisiting Distributed Synchronous SGD
Jianmin Chen, Xinghao Pan, Rajat Monga +2
Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the c…