33,100 research outputs found
Memory-Efficient Topic Modeling
As one of the simplest probabilistic topic modeling techniques, latent
Dirichlet allocation (LDA) has found many important applications in text
mining, computer vision and computational biology. Recent training algorithms
for LDA can be interpreted within a unified message passing framework. However,
message passing requires storing previous messages with a large amount of
memory space, increasing linearly with the number of documents or the number of
topics. Therefore, the high memory usage is often a major problem for topic
modeling of massive corpora containing a large number of topics. To reduce the
space complexity, we propose a novel algorithm without storing previous
messages for training LDA: tiny belief propagation (TBP). The basic idea of TBP
relates the message passing algorithms with the non-negative matrix
factorization (NMF) algorithms, which absorb the message updating into the
message passing process, and thus avoid storing previous messages. Experimental
results on four large data sets confirm that TBP performs comparably well or
even better than current state-of-the-art training algorithms for LDA but with
a much less memory consumption. TBP can do topic modeling when massive corpora
cannot fit in the computer memory, for example, extracting thematic topics from
7 GB PUBMED corpora on a common desktop computer with 2GB memory.Comment: 20 pages, 7 figure
Reset-free Trial-and-Error Learning for Robot Damage Recovery
The high probability of hardware failures prevents many advanced robots
(e.g., legged robots) from being confidently deployed in real-world situations
(e.g., post-disaster rescue). Instead of attempting to diagnose the failures,
robots could adapt by trial-and-error in order to be able to complete their
tasks. In this situation, damage recovery can be seen as a Reinforcement
Learning (RL) problem. However, the best RL algorithms for robotics require the
robot and the environment to be reset to an initial state after each episode,
that is, the robot is not learning autonomously. In addition, most of the RL
methods for robotics do not scale well with complex robots (e.g., walking
robots) and either cannot be used at all or take too long to converge to a
solution (e.g., hours of learning). In this paper, we introduce a novel
learning algorithm called "Reset-free Trial-and-Error" (RTE) that (1) breaks
the complexity by pre-generating hundreds of possible behaviors with a dynamics
simulator of the intact robot, and (2) allows complex robots to quickly recover
from damage while completing their tasks and taking the environment into
account. We evaluate our algorithm on a simulated wheeled robot, a simulated
six-legged robot, and a real six-legged walking robot that are damaged in
several ways (e.g., a missing leg, a shortened leg, faulty motor, etc.) and
whose objective is to reach a sequence of targets in an arena. Our experiments
show that the robots can recover most of their locomotion abilities in an
environment with obstacles, and without any human intervention.Comment: 18 pages, 16 figures, 3 tables, 6 pseudocodes/algorithms, video at
https://youtu.be/IqtyHFrb3BU, code at
https://github.com/resibots/chatzilygeroudis_2018_rt
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