2,171 research outputs found
Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control
policies directly from high-dimensional sensory input using reinforcement
learning. The model is a convolutional neural network, trained with a variant
of Q-learning, whose input is raw pixels and whose output is a value function
estimating future rewards. We apply our method to seven Atari 2600 games from
the Arcade Learning Environment, with no adjustment of the architecture or
learning algorithm. We find that it outperforms all previous approaches on six
of the games and surpasses a human expert on three of them.Comment: NIPS Deep Learning Workshop 201
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Deep Neural Networks (DNN) have achieved state-of-the-art results in a wide
range of tasks, with the best results obtained with large training sets and
large models. In the past, GPUs enabled these breakthroughs because of their
greater computational speed. In the future, faster computation at both training
and test time is likely to be crucial for further progress and for consumer
applications on low-power devices. As a result, there is much interest in
research and development of dedicated hardware for Deep Learning (DL). Binary
weights, i.e., weights which are constrained to only two possible values (e.g.
-1 or 1), would bring great benefits to specialized DL hardware by replacing
many multiply-accumulate operations by simple accumulations, as multipliers are
the most space and power-hungry components of the digital implementation of
neural networks. We introduce BinaryConnect, a method which consists in
training a DNN with binary weights during the forward and backward
propagations, while retaining precision of the stored weights in which
gradients are accumulated. Like other dropout schemes, we show that
BinaryConnect acts as regularizer and we obtain near state-of-the-art results
with BinaryConnect on the permutation-invariant MNIST, CIFAR-10 and SVHN.Comment: Accepted at NIPS 2015, 9 pages, 3 figure
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