12,890 research outputs found
A Deep Incremental Boltzmann Machine for Modeling Context in Robots
Context is an essential capability for robots that are to be as adaptive as
possible in challenging environments. Although there are many context modeling
efforts, they assume a fixed structure and number of contexts. In this paper,
we propose an incremental deep model that extends Restricted Boltzmann
Machines. Our model gets one scene at a time, and gradually extends the
contextual model when necessary, either by adding a new context or a new
context layer to form a hierarchy. We show on a scene classification benchmark
that our method converges to a good estimate of the contexts of the scenes, and
performs better or on-par on several tasks compared to other incremental models
or non-incremental models.Comment: 6 pages, 5 figures, International Conference on Robotics and
Automation (ICRA 2018
Training Restricted Boltzmann Machines on Word Observations
The restricted Boltzmann machine (RBM) is a flexible tool for modeling
complex data, however there have been significant computational difficulties in
using RBMs to model high-dimensional multinomial observations. In natural
language processing applications, words are naturally modeled by K-ary discrete
distributions, where K is determined by the vocabulary size and can easily be
in the hundreds of thousands. The conventional approach to training RBMs on
word observations is limited because it requires sampling the states of K-way
softmax visible units during block Gibbs updates, an operation that takes time
linear in K. In this work, we address this issue by employing a more general
class of Markov chain Monte Carlo operators on the visible units, yielding
updates with computational complexity independent of K. We demonstrate the
success of our approach by training RBMs on hundreds of millions of word
n-grams using larger vocabularies than previously feasible and using the
learned features to improve performance on chunking and sentiment
classification tasks, achieving state-of-the-art results on the latter
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