4,866 research outputs found
Online Ensemble Learning of Sensorimotor Contingencies
Forward models play a key role in cognitive agents by providing predictions of the sensory consequences of motor commands, also known as sensorimotor contingencies (SMCs). In continuously evolving environments, the ability to anticipate is fundamental in distinguishing cognitive from reactive agents, and it is particularly relevant for autonomous robots, that must be able to adapt their models in an online manner. Online learning skills, high accuracy of the forward models and multiple-step-ahead predictions are needed to enhance the robots’ anticipation capabilities. We propose an online heterogeneous ensemble learning method for building accurate forward models of SMCs relating motor commands to effects in robots’ sensorimotor system, in particular considering proprioception and vision. Our method achieves up to 98% higher accuracy both in short and long term predictions, compared to single predictors and other online and offline homogeneous ensembles. This method is validated on two different humanoid robots, namely the iCub and the Baxter
Scalable Similarity Search for Molecular Descriptors
Similarity search over chemical compound databases is a fundamental task in
the discovery and design of novel drug-like molecules. Such databases often
encode molecules as non-negative integer vectors, called molecular descriptors,
which represent rich information on various molecular properties. While there
exist efficient indexing structures for searching databases of binary vectors,
solutions for more general integer vectors are in their infancy. In this paper
we present a time- and space- efficient index for the problem that we call the
succinct intervals-splitting tree algorithm for molecular descriptors (SITAd).
Our approach extends efficient methods for binary-vector databases, and uses
ideas from succinct data structures. Our experiments, on a large database of
over 40 million compounds, show SITAd significantly outperforms alternative
approaches in practice.Comment: To be appeared in the Proceedings of SISAP'1
Rule-based Machine Learning Methods for Functional Prediction
We describe a machine learning method for predicting the value of a
real-valued function, given the values of multiple input variables. The method
induces solutions from samples in the form of ordered disjunctive normal form
(DNF) decision rules. A central objective of the method and representation is
the induction of compact, easily interpretable solutions. This rule-based
decision model can be extended to search efficiently for similar cases prior to
approximating function values. Experimental results on real-world data
demonstrate that the new techniques are competitive with existing machine
learning and statistical methods and can sometimes yield superior regression
performance.Comment: See http://www.jair.org/ for any accompanying file
Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments
The feasibility of deep neural networks (DNNs) to address data stream
problems still requires intensive study because of the static and offline
nature of conventional deep learning approaches. A deep continual learning
algorithm, namely autonomous deep learning (ADL), is proposed in this paper.
Unlike traditional deep learning methods, ADL features a flexible structure
where its network structure can be constructed from scratch with the absence of
an initial network structure via the self-constructing network structure. ADL
specifically addresses catastrophic forgetting by having a different-depth
structure which is capable of achieving a trade-off between plasticity and
stability. Network significance (NS) formula is proposed to drive the hidden
nodes growing and pruning mechanism. Drift detection scenario (DDS) is put
forward to signal distributional changes in data streams which induce the
creation of a new hidden layer. The maximum information compression index
(MICI) method plays an important role as a complexity reduction module
eliminating redundant layers. The efficacy of ADL is numerically validated
under the prequential test-then-train procedure in lifelong environments using
nine popular data stream problems. The numerical results demonstrate that ADL
consistently outperforms recent continual learning methods while characterizing
the automatic construction of network structures
Pruned Bit-Reversal Permutations: Mathematical Characterization, Fast Algorithms and Architectures
A mathematical characterization of serially-pruned permutations (SPPs)
employed in variable-length permuters and their associated fast pruning
algorithms and architectures are proposed. Permuters are used in many signal
processing systems for shuffling data and in communication systems as an
adjunct to coding for error correction. Typically only a small set of discrete
permuter lengths are supported. Serial pruning is a simple technique to alter
the length of a permutation to support a wider range of lengths, but results in
a serial processing bottleneck. In this paper, parallelizing SPPs is formulated
in terms of recursively computing sums involving integer floor and related
functions using integer operations, in a fashion analogous to evaluating
Dedekind sums. A mathematical treatment for bit-reversal permutations (BRPs) is
presented, and closed-form expressions for BRP statistics are derived. It is
shown that BRP sequences have weak correlation properties. A new statistic
called permutation inliers that characterizes the pruning gap of pruned
interleavers is proposed. Using this statistic, a recursive algorithm that
computes the minimum inliers count of a pruned BR interleaver (PBRI) in
logarithmic time complexity is presented. This algorithm enables parallelizing
a serial PBRI algorithm by any desired parallelism factor by computing the
pruning gap in lookahead rather than a serial fashion, resulting in significant
reduction in interleaving latency and memory overhead. Extensions to 2-D block
and stream interleavers, as well as applications to pruned fast Fourier
transforms and LTE turbo interleavers, are also presented. Moreover,
hardware-efficient architectures for the proposed algorithms are developed.
Simulation results demonstrate 3 to 4 orders of magnitude improvement in
interleaving time compared to existing approaches.Comment: 31 page
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