205 research outputs found
Audio Event Detection using Weakly Labeled Data
Acoustic event detection is essential for content analysis and description of
multimedia recordings. The majority of current literature on the topic learns
the detectors through fully-supervised techniques employing strongly labeled
data. However, the labels available for majority of multimedia data are
generally weak and do not provide sufficient detail for such methods to be
employed. In this paper we propose a framework for learning acoustic event
detectors using only weakly labeled data. We first show that audio event
detection using weak labels can be formulated as an Multiple Instance Learning
problem. We then suggest two frameworks for solving multiple-instance learning,
one based on support vector machines, and the other on neural networks. The
proposed methods can help in removing the time consuming and expensive process
of manually annotating data to facilitate fully supervised learning. Moreover,
it can not only detect events in a recording but can also provide temporal
locations of events in the recording. This helps in obtaining a complete
description of the recording and is notable since temporal information was
never known in the first place in weakly labeled data.Comment: ACM Multimedia 201
A stochastic approximation algorithm with multiplicative step size modification
An algorithm of searching a zero of an unknown function \vphi : \,
\R \to \R is considered: ,\,
, where is the
value of \vphi measured at and is the
measurement error. The step sizes \gam_t > 0 are modified in the
course of the algorithm according to the rule: \, \gamma_t =
\min\{u\, \gamma_{t-1},\, \mstep\} if , and , otherwise, where . That is, at each iteration \gam_t is
multiplied either by or by , provided that the resulting
value does not exceed the predetermined value \mstep. The function
\vphi may have one or several zeros; the random values are
independent and identically distributed, with zero mean and finite
variance. Under some additional assumptions on \vphi, , and
\mstep, the conditions on and guaranteeing a.s.
convergence of the sequence , as well as a.s. divergence,
are determined. In particular, if and for any , one has
convergence for . Due to the
multiplicative updating rule for \gam_t, the sequence
converges rapidly: like a geometric progression (if convergence
takes place), but the limit value may not coincide with, but
instead, approximates one of the zeros of \vphi. By adjusting the
parameters and , one can reach arbitrarily high precision of
the approximation; higher precision is obtained at the expense of
lower convergence rate
Geometric deep learning
The goal of these course notes is to describe the main mathematical ideas behind geometric deep learning and to provide implementation details for several applications in shape analysis and synthesis, computer vision and computer graphics. The text in the course materials is primarily based on previously published work. With these notes we gather and provide a clear picture of the key concepts and techniques that fall under the umbrella of geometric deep learning, and illustrate the applications they enable. We also aim to provide practical implementation details for the methods presented in these works, as well as suggest further readings and extensions of these ideas
The time dimension of neural network models
This review attempts to provide an insightful perspective on the role of time within neural network models and the use of neural networks for problems involving time. The most commonly used neural network models are defined and explained giving mention to important technical issues but avoiding great detail. The relationship between recurrent and feedforward networks is emphasised, along with the distinctions in their practical and theoretical abilities. Some practical examples are discussed to illustrate the major issues concerning the application of neural networks to data with various types of temporal structure, and finally some highlights of current research on the more difficult types of problems are presented
Chiral perturbation theory in a magnetic background - finite-temperature effects
We consider chiral perturbation theory for SU(2) at finite temperature in
a constant magnetic background . We compute the thermal mass of the pions
and the pion decay constant to leading order in chiral perturbation theory in
the presence of the magnetic field. The magnetic field gives rise to a
splitting between and as well as between
and . We also calculate the free energy and the
quark condensate to next-to-leading order in chiral perturbation theory. Both
the pion decay constants and the quark condensate are decreasing slower as a
function of temperature as compared to the case with vanishing magnetic field.
The latter result suggests that the critical temperature for the chiral
transition is larger in the presence of a constant magnetic field. The increase
of as a function of is in agreement with most model calculations but
in disagreement with recent lattice calculations.Comment: 24 pages and 9 fig
The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry
This paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic damage, not with the goal of replacing existing approaches, but as a mean to improve the precision of empirical methods. For such, damage data collected in the aftermath of the 1998 Azores earthquake (Portugal) is used to develop a comparative analysis between damage grades obtained resorting to a classic damage formulation and an innovative approach based on Artificial Neural Networks (ANNs). The analysis is carried out on the basis of a vulnerability index computed with a hybrid seismic vulnerability assessment methodology, which is subsequently used as input to both approaches. The results obtained are then compared with real post-earthquake damage observation and critically discussed taking into account the level of adjustment achieved by each approach. Finally, a computer routine that uses the ANN as an approximation function is developed and applied to derive a new vulnerability curve expression. In general terms, the ANN developed in this study allowed to obtain much better approximations than those achieved with the original vulnerability approach, which has revealed to be quite non-conservative. Similarly, the proposed vulnerability curve expression was found to provide a more accurate damage prediction than the traditional analytical expressions.SFRH/BPD/122598/2016info:eu-repo/semantics/publishedVersio
Approximate policy iteration: A survey and some new methods
We consider the classical policy iteration method of dynamic programming (DP), where approximations and simulation are used to deal with the curse of dimensionality. We survey a number of issues: convergence and rate of convergence of approximate policy evaluation methods, singularity and susceptibility to simulation noise of policy evaluation, exploration issues, constrained and enhanced policy iteration, policy oscillation and chattering, and optimistic and distributed policy iteration. Our discussion of policy evaluation is couched in general terms and aims to unify the available methods in the light of recent research developments and to compare the two main policy evaluation approaches: projected equations and temporal differences (TD), and aggregation. In the context of these approaches, we survey two different types of simulation-based algorithms: matrix inversion methods, such as least-squares temporal difference (LSTD), and iterative methods, such as least-squares policy evaluation (LSPE) and TD (λ), and their scaled variants. We discuss a recent method, based on regression and regularization, which rectifies the unreliability of LSTD for nearly singular projected Bellman equations. An iterative version of this method belongs to the LSPE class of methods and provides the connecting link between LSTD and LSPE. Our discussion of policy improvement focuses on the role of policy oscillation and its effect on performance guarantees. We illustrate that policy evaluation when done by the projected equation/TD approach may lead to policy oscillation, but when done by aggregation it does not. This implies better error bounds and more regular performance for aggregation, at the expense of some loss of generality in cost function representation capability. Hard aggregation provides the connecting link between projected equation/TD-based and aggregation-based policy evaluation, and is characterized by favorable error bounds.National Science Foundation (U.S.) (No.ECCS-0801549)Los Alamos National Laboratory. Information Science and Technology InstituteUnited States. Air Force (No.FA9550-10-1-0412
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