1,337 research outputs found
An integrated neuro-mechanical model of C. elegans forward locomotion
One of the most tractable organisms for the study of nervous
systems is the nematode Caenorhabditis elegans, whose locomotion in
particular has been the subject of a number of models. In this paper we
present a first integrated neuro-mechanical model of forward locomotion.
We find that a previous neural model is robust to the addition of a
body with mechanical properties, and that the integrated model produces
oscillations with a more realistic frequency and waveform than the neural
model alone. We conclude that the body and environment are likely to
be important components of the worm’s locomotion subsystem
Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot
We address the problem of autonomously learning controllers for
vision-capable mobile robots. We extend McCallum's (1995) Nearest-Sequence
Memory algorithm to allow for general metrics over state-action trajectories.
We demonstrate the feasibility of our approach by successfully running our
algorithm on a real mobile robot. The algorithm is novel and unique in that it
(a) explores the environment and learns directly on a mobile robot without
using a hand-made computer model as an intermediate step, (b) does not require
manual discretization of the sensor input space, (c) works in piecewise
continuous perceptual spaces, and (d) copes with partial observability.
Together this allows learning from much less experience compared to previous
methods.Comment: 14 pages, 8 figure
SVMs for Automatic Speech Recognition: a Survey
Hidden Markov Models (HMMs) are, undoubtedly, the most employed core technique for Automatic Speech Recognition (ASR). Nevertheless, we are still far from achieving high-performance ASR systems. Some alternative approaches, most of them based on Artificial Neural Networks (ANNs), were proposed during the late eighties and early nineties. Some of them tackled the ASR problem using predictive ANNs, while others proposed hybrid HMM/ANN systems. However, despite some achievements, nowadays, the preponderance of Markov Models is a fact.
During the last decade, however, a new tool appeared in the field of machine learning that has proved to be able to cope with hard classification problems in several fields of application: the Support Vector Machines (SVMs). The SVMs are effective discriminative classifiers with several outstanding characteristics, namely: their solution is that with maximum margin; they are capable to deal with samples of a very higher dimensionality; and their convergence to the minimum of the associated cost function is guaranteed.
These characteristics have made SVMs very popular and successful. In this chapter we discuss their strengths and weakness in the ASR context and make a review of the current state-of-the-art techniques. We organize the contributions in two parts: isolated-word recognition and continuous speech recognition. Within the first part we review several techniques to produce the fixed-dimension vectors needed for original SVMs. Afterwards we explore more sophisticated techniques based on the use of kernels capable to deal with sequences of different length. Among them is the DTAK kernel, simple and effective, which rescues an old technique of speech recognition: Dynamic Time Warping (DTW). Within the second part, we describe some recent approaches to tackle more complex tasks like connected digit recognition or continuous speech recognition using SVMs. Finally we draw some conclusions and outline several ongoing lines of research
Deep Learning and Statistical Models for Time-Critical Pedestrian Behaviour Prediction
The time it takes for a classifier to make an accurate prediction can be
crucial in many behaviour recognition problems. For example, an autonomous
vehicle should detect hazardous pedestrian behaviour early enough for it to
take appropriate measures. In this context, we compare the switching linear
dynamical system (SLDS) and a three-layered bi-directional long short-term
memory (LSTM) neural network, which are applied to infer pedestrian behaviour
from motion tracks. We show that, though the neural network model achieves an
accuracy of 80%, it requires long sequences to achieve this (100 samples or
more). The SLDS, has a lower accuracy of 74%, but it achieves this result with
short sequences (10 samples). To our knowledge, such a comparison on sequence
length has not been considered in the literature before. The results provide a
key intuition of the suitability of the models in time-critical problems
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