134 research outputs found
Comparing Probabilistic Models for Melodic Sequences
Modelling the real world complexity of music is a challenge for machine
learning. We address the task of modeling melodic sequences from the same music
genre. We perform a comparative analysis of two probabilistic models; a
Dirichlet Variable Length Markov Model (Dirichlet-VMM) and a Time Convolutional
Restricted Boltzmann Machine (TC-RBM). We show that the TC-RBM learns
descriptive music features, such as underlying chords and typical melody
transitions and dynamics. We assess the models for future prediction and
compare their performance to a VMM, which is the current state of the art in
melody generation. We show that both models perform significantly better than
the VMM, with the Dirichlet-VMM marginally outperforming the TC-RBM. Finally,
we evaluate the short order statistics of the models, using the
Kullback-Leibler divergence between test sequences and model samples, and show
that our proposed methods match the statistics of the music genre significantly
better than the VMM.Comment: in Proceedings of the ECML-PKDD 2011. Lecture Notes in Computer
Science, vol. 6913, pp. 289-304. Springer (2011
Inference of kinetic Ising model on sparse graphs
Based on dynamical cavity method, we propose an approach to the inference of
kinetic Ising model, which asks to reconstruct couplings and external fields
from given time-dependent output of original system. Our approach gives an
exact result on tree graphs and a good approximation on sparse graphs, it can
be seen as an extension of Belief Propagation inference of static Ising model
to kinetic Ising model. While existing mean field methods to the kinetic Ising
inference e.g., na\" ive mean-field, TAP equation and simply mean-field, use
approximations which calculate magnetizations and correlations at time from
statistics of data at time , dynamical cavity method can use statistics of
data at times earlier than to capture more correlations at different time
steps. Extensive numerical experiments show that our inference method is
superior to existing mean-field approaches on diluted networks.Comment: 9 pages, 3 figures, comments are welcom
Solving the Sports League Scheduling Problem with Tabu Search
In this paper we present a tabu approach for a version of the Sports League Scheduling Problem. The approach adopted is based on a formulation of the problem as a Constraint Satisfaction Problem (CSP). Tests were carried out on problem instances of up to 40 teams representing 780 integer variables with 780 values per variable. Experimental results show that this approach outperforms some existing methods and is one of the most promising methods for solving problems of this type
Adaptive cluster expansion for the inverse Ising problem: convergence, algorithm and tests
We present a procedure to solve the inverse Ising problem, that is to find
the interactions between a set of binary variables from the measure of their
equilibrium correlations. The method consists in constructing and selecting
specific clusters of variables, based on their contributions to the
cross-entropy of the Ising model. Small contributions are discarded to avoid
overfitting and to make the computation tractable. The properties of the
cluster expansion and its performances on synthetic data are studied. To make
the implementation easier we give the pseudo-code of the algorithm.Comment: Paper submitted to Journal of Statistical Physic
Game Theoretical Interactions of Moving Agents
Game theory has been one of the most successful quantitative concepts to
describe social interactions, their strategical aspects, and outcomes. Among
the payoff matrix quantifying the result of a social interaction, the
interaction conditions have been varied, such as the number of repeated
interactions, the number of interaction partners, the possibility to punish
defective behavior etc. While an extension to spatial interactions has been
considered early on such as in the "game of life", recent studies have focussed
on effects of the structure of social interaction networks.
However, the possibility of individuals to move and, thereby, evade areas
with a high level of defection, and to seek areas with a high level of
cooperation, has not been fully explored so far. This contribution presents a
model combining game theoretical interactions with success-driven motion in
space, and studies the consequences that this may have for the degree of
cooperation and the spatio-temporal dynamics in the population. It is
demonstrated that the combination of game theoretical interactions with motion
gives rise to many self-organized behavioral patterns on an aggregate level,
which can explain a variety of empirically observed social behaviors
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