5,663 research outputs found
A Model for Prejudiced Learning in Noisy Environments
Based on the heuristics that maintaining presumptions can be beneficial in
uncertain environments, we propose a set of basic axioms for learning systems
to incorporate the concept of prejudice. The simplest, memoryless model of a
deterministic learning rule obeying the axioms is constructed, and shown to be
equivalent to the logistic map. The system's performance is analysed in an
environment in which it is subject to external randomness, weighing learning
defectiveness against stability gained. The corresponding random dynamical
system with inhomogeneous, additive noise is studied, and shown to exhibit the
phenomena of noise induced stability and stochastic bifurcations. The overall
results allow for the interpretation that prejudice in uncertain environments
entails a considerable portion of stubbornness as a secondary phenomenon.Comment: 21 pages, 11 figures; reduced graphics to slash size, full version on
Author's homepage. Minor revisions in text and references, identical to
version to be published in Applied Mathematics and Computatio
Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning
Obstacle avoidance is a fundamental requirement for autonomous robots which
operate in, and interact with, the real world. When perception is limited to
monocular vision avoiding collision becomes significantly more challenging due
to the lack of 3D information. Conventional path planners for obstacle
avoidance require tuning a number of parameters and do not have the ability to
directly benefit from large datasets and continuous use. In this paper, a
dueling architecture based deep double-Q network (D3QN) is proposed for
obstacle avoidance, using only monocular RGB vision. Based on the dueling and
double-Q mechanisms, D3QN can efficiently learn how to avoid obstacles in a
simulator even with very noisy depth information predicted from RGB image.
Extensive experiments show that D3QN enables twofold acceleration on learning
compared with a normal deep Q network and the models trained solely in virtual
environments can be directly transferred to real robots, generalizing well to
various new environments with previously unseen dynamic objects.Comment: Accepted by RSS 2017 workshop New Frontiers for Deep Learning in
Robotic
Robust Sound Event Classification using Deep Neural Networks
The automatic recognition of sound events by computers is an important aspect of emerging applications such as automated surveillance, machine hearing and auditory scene understanding. Recent advances in machine learning, as well as in computational models of the human auditory system, have contributed to advances in this increasingly popular research field. Robust sound event classification, the ability to recognise sounds under real-world noisy conditions, is an especially challenging task. Classification methods translated from the speech recognition domain, using features such as mel-frequency cepstral coefficients, have been shown to perform reasonably well for the sound event classification task, although spectrogram-based or auditory image analysis techniques reportedly achieve superior performance in noise.
This paper outlines a sound event classification framework that compares auditory image front end features with spectrogram image-based front end features, using support vector machine and deep neural network classifiers. Performance is evaluated on a standard robust classification task in different levels of corrupting noise, and with several system enhancements, and shown to compare very well with current state-of-the-art classification techniques
Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System
A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175
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