4,865 research outputs found
Modeling Human Categorization of Natural Images Using Deep Feature Representations
Over the last few decades, psychologists have developed sophisticated formal
models of human categorization using simple artificial stimuli. In this paper,
we use modern machine learning methods to extend this work into the realm of
naturalistic stimuli, enabling human categorization to be studied over the
complex visual domain in which it evolved and developed. We show that
representations derived from a convolutional neural network can be used to
model behavior over a database of >300,000 human natural image classifications,
and find that a group of models based on these representations perform well,
near the reliability of human judgments. Interestingly, this group includes
both exemplar and prototype models, contrasting with the dominance of exemplar
models in previous work. We are able to improve the performance of the
remaining models by preprocessing neural network representations to more
closely capture human similarity judgments.Comment: 13 pages, 7 figures, 6 tables. Preliminary work presented at CogSci
201
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks
While the use of bottom-up local operators in convolutional neural networks
(CNNs) matches well some of the statistics of natural images, it may also
prevent such models from capturing contextual long-range feature interactions.
In this work, we propose a simple, lightweight approach for better context
exploitation in CNNs. We do so by introducing a pair of operators: gather,
which efficiently aggregates feature responses from a large spatial extent, and
excite, which redistributes the pooled information to local features. The
operators are cheap, both in terms of number of added parameters and
computational complexity, and can be integrated directly in existing
architectures to improve their performance. Experiments on several datasets
show that gather-excite can bring benefits comparable to increasing the depth
of a CNN at a fraction of the cost. For example, we find ResNet-50 with
gather-excite operators is able to outperform its 101-layer counterpart on
ImageNet with no additional learnable parameters. We also propose a parametric
gather-excite operator pair which yields further performance gains, relate it
to the recently-introduced Squeeze-and-Excitation Networks, and analyse the
effects of these changes to the CNN feature activation statistics.Comment: NeurIPS 201
Atari games and Intel processors
The asynchronous nature of the state-of-the-art reinforcement learning
algorithms such as the Asynchronous Advantage Actor-Critic algorithm, makes
them exceptionally suitable for CPU computations. However, given the fact that
deep reinforcement learning often deals with interpreting visual information, a
large part of the train and inference time is spent performing convolutions. In
this work we present our results on learning strategies in Atari games using a
Convolutional Neural Network, the Math Kernel Library and TensorFlow 0.11rc0
machine learning framework. We also analyze effects of asynchronous
computations on the convergence of reinforcement learning algorithms
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