10,438 research outputs found
Memory and long-range correlations in chess games
In this paper we report the existence of long-range memory in the opening
moves of a chronologically ordered set of chess games using an extensive chess
database. We used two mapping rules to build discrete time series and analyzed
them using two methods for detecting long-range correlations; rescaled range
analysis and detrented fluctuation analysis. We found that long-range memory is
related to the level of the players. When the database is filtered according to
player levels we found differences in the persistence of the different subsets.
For high level players, correlations are stronger at long time scales; whereas
in intermediate and low level players they reach the maximum value at shorter
time scales. This can be interpreted as a signature of the different strategies
used by players with different levels of expertise. These results are robust
against the assignation rules and the method employed in the analysis of the
time series.Comment: 12 pages, 5 figures. Published in Physica
-ARM: Network Sparsification via Stochastic Binary Optimization
We consider network sparsification as an -norm regularized binary
optimization problem, where each unit of a neural network (e.g., weight,
neuron, or channel, etc.) is attached with a stochastic binary gate, whose
parameters are jointly optimized with original network parameters. The
Augment-Reinforce-Merge (ARM), a recently proposed unbiased gradient estimator,
is investigated for this binary optimization problem. Compared to the hard
concrete gradient estimator from Louizos et al., ARM demonstrates superior
performance of pruning network architectures while retaining almost the same
accuracies of baseline methods. Similar to the hard concrete estimator, ARM
also enables conditional computation during model training but with improved
effectiveness due to the exact binary stochasticity. Thanks to the flexibility
of ARM, many smooth or non-smooth parametric functions, such as scaled sigmoid
or hard sigmoid, can be used to parameterize this binary optimization problem
and the unbiasness of the ARM estimator is retained, while the hard concrete
estimator has to rely on the hard sigmoid function to achieve conditional
computation and thus accelerated training. Extensive experiments on multiple
public datasets demonstrate state-of-the-art pruning rates with almost the same
accuracies of baseline methods. The resulting algorithm -ARM sparsifies
the Wide-ResNet models on CIFAR-10 and CIFAR-100 while the hard concrete
estimator cannot. The code is public available at
https://github.com/leo-yangli/l0-arm.Comment: Published as a conference paper at ECML 201
A Progressive Universal Noiseless Coder
The authors combine pruned tree-structured vector quantization (pruned TSVQ) with Itoh's (1987) universal noiseless coder. By combining pruned TSVQ with universal noiseless coding, they benefit from the “successive approximation” capabilities of TSVQ, thereby allowing progressive transmission of images, while retaining the ability to noiselessly encode images of unknown statistics in a provably asymptotically optimal fashion. Noiseless compression results are comparable to Ziv-Lempel and arithmetic coding for both images and finely quantized Gaussian sources
Pruning based Distance Sketches with Provable Guarantees on Random Graphs
Measuring the distances between vertices on graphs is one of the most
fundamental components in network analysis. Since finding shortest paths
requires traversing the graph, it is challenging to obtain distance information
on large graphs very quickly. In this work, we present a preprocessing
algorithm that is able to create landmark based distance sketches efficiently,
with strong theoretical guarantees. When evaluated on a diverse set of social
and information networks, our algorithm significantly improves over existing
approaches by reducing the number of landmarks stored, preprocessing time, or
stretch of the estimated distances.
On Erd\"{o}s-R\'{e}nyi graphs and random power law graphs with degree
distribution exponent , our algorithm outputs an exact distance
data structure with space between and
depending on the value of , where is the number of vertices. We
complement the algorithm with tight lower bounds for Erdos-Renyi graphs and the
case when is close to two.Comment: Full version for the conference paper to appear in The Web
Conference'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
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