463 research outputs found
Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
We describe a statistical approach for modeling dialogue acts in
conversational speech, i.e., speech-act-like units such as Statement, Question,
Backchannel, Agreement, Disagreement, and Apology. Our model detects and
predicts dialogue acts based on lexical, collocational, and prosodic cues, as
well as on the discourse coherence of the dialogue act sequence. The dialogue
model is based on treating the discourse structure of a conversation as a
hidden Markov model and the individual dialogue acts as observations emanating
from the model states. Constraints on the likely sequence of dialogue acts are
modeled via a dialogue act n-gram. The statistical dialogue grammar is combined
with word n-grams, decision trees, and neural networks modeling the
idiosyncratic lexical and prosodic manifestations of each dialogue act. We
develop a probabilistic integration of speech recognition with dialogue
modeling, to improve both speech recognition and dialogue act classification
accuracy. Models are trained and evaluated using a large hand-labeled database
of 1,155 conversations from the Switchboard corpus of spontaneous
human-to-human telephone speech. We achieved good dialogue act labeling
accuracy (65% based on errorful, automatically recognized words and prosody,
and 71% based on word transcripts, compared to a chance baseline accuracy of
35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling
changed
ΠΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½Π°Ρ Π½Π°ΡΡΠ½Π°Ρ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΡ "Π ΠΎΠ±Π°ΡΡΠ½Π°Ρ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠ° ΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²Π°Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠ° - 2019" (04-06 ΠΈΡΠ»Ρ 2019 Π³.) : ΡΠ±ΠΎΡΠ½ΠΈΠΊ ΡΡΠ°ΡΠ΅ΠΉ
Π ΡΠ±ΠΎΡΠ½ΠΈΠΊΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΡΠ°ΡΡΠΈ, ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π½ΡΠ΅ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΠΌ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΠΌ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ ΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠΌ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π°ΠΌ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΡΡΠ½Π΄Π°ΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΡ
ΠΈ ΠΏΡΠΈΠΊΠ»Π°Π΄Π½ΡΡ
Π·Π°Π΄Π°Ρ. ΠΠ»Ρ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ², Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ², ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠΎΠ² Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ ΠΈ Π΅Π΅ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ.Π’Π΅ΠΊΡΡ ΠΊΠ½.: ΡΡΡ., Π°Π½Π³Π»
Bayesian system identification of a nonlinear dynamical system using a novel variant of Simulated Annealing
This work details the Bayesian identification of a nonlinear dynamical system using a novel MCMC algorithm: 'Data Annealing'. Data Annealing is similar to Simulated Annealing in that it allows the Markov chain to easily clear 'local traps' in the target distribution. To achieve this, training data is fed into the likelihood such that its influence over the posterior is introduced gradually - this allows the annealing procedure to be conducted with reduced computational expense. Additionally, Data Annealing uses a proposal distribution which allows it to conduct a local search accompanied by occasional long jumps, reducing the chance that it will become stuck in local traps. Here it is used to identify an experimental nonlinear system. The resulting Markov chains are used to approximate the covariance matrices of the parameters in a set of competing models before the issue of model selection is tackled using the Deviance Information Criterion. Β© 2014
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
Duality for nonlinear filtering
This thesis is concerned with the stochastic filtering problem for a hidden
Markov model (HMM) with the white noise observation model. For this filtering
problem, we make three types of original contributions: (1) dual
controllability characterization of stochastic observability, (2) dual minimum
variance optimal control formulation of the stochastic filtering problem, and
(3) filter stability analysis using the dual optimal control formulation.
For the first contribution of this thesis, a backward stochastic differential
equation (BSDE) is proposed as the dual control system. The observability
(detectability) of the HMM is shown to be equivalent to the controllability
(stabilizability) of the dual control system. For the linear-Gaussian model,
the dual relationship reduces to classical duality in linear systems theory.
The second contribution is to transform the minimum variance estimation
problem into an optimal control problem. The constraint is given by the dual
control system. The optimal solution is obtained via two approaches: (1) by an
application of maximum principle and (2) by the martingale characterization of
the optimal value. The optimal solution is used to derive the nonlinear filter.
The third contribution is to carry out filter stability analysis by studying
the dual optimal control problem. Two approaches are presented through Chapters
7 and 8. In Chapter 7, conditional Poincar\'e inequality (PI) is introduced.
Based on conditional PI, various convergence rates are obtained and related to
literature. In Chapter 8, the stabilizability of the dual control system is
shown to be a necessary and sufficient condition for filter stability on
certain finite state space model.Comment: Ph.D. Thesis of the autho
Implementing Bayesian Inference with Neural Networks
Embodied agents, be they animals or robots, acquire information about the world through their senses. Embodied agents, however, do not simply lose this information once it passes by, but rather process and store it for future use. The most general theory of how an agent can combine stored knowledge with new observations is Bayesian inference. In this dissertation I present a theory of how embodied agents can learn to implement Bayesian inference with neural networks.
By neural network I mean both artificial and biological neural networks, and in my dissertation I address both kinds. On one hand, I develop theory for implementing Bayesian inference in deep generative models, and I show how to train multilayer perceptrons to compute approximate predictions for Bayesian filtering. On the other hand, I show that several models in computational neuroscience are special cases of the general theory that I develop in this dissertation, and I use this theory to model and explain several phenomena in neuroscience. The key contributions of this dissertation can be summarized as follows:
- I develop a class of graphical model called nth-order harmoniums. An nth-order harmonium is an n-tuple of random variables, where the conditional distribution of each variable given all the others is always an element of the same exponential family. I show that harmoniums have a recursive structure which allows them to be analyzed at coarser and finer levels of detail.
- I define a class of harmoniums called rectified harmoniums, which are constrained to have priors which are conjugate to their posteriors. As a consequence of this, rectified harmoniums afford efficient sampling and learning.
- I develop deep harmoniums, which are harmoniums which can be represented by hierarchical, undirected graphs. I develop the theory of rectification for deep harmoniums, and develop a novel algorithm for training deep generative models.
- I show how to implement a variety of optimal and near-optimal Bayes filters by combining the solution to Bayes' rule provided by rectified harmoniums, with predictions computed by a recurrent neural network. I then show how to train a neural network to implement Bayesian filtering when the transition and emission distributions are unknown.
- I show how some well-established models of neural activity are special cases of the theory I present in this dissertation, and how these models can be generalized with the theory of rectification.
- I show how the theory that I present can model several neural phenomena including proprioception and gain-field modulation of tuning curves.
- I introduce a library for the programming language Haskell, within which I have implemented all the simulations presented in this dissertation. This library uses concepts from Riemannian geometry to provide a rigorous and efficient environment for implementing complex numerical simulations.
I also use the results presented in this dissertation to argue for the fundamental role of neural computation in embodied cognition. I argue, in other words, that before we will be able to build truly intelligent robots, we will need to truly understand biological brains
Predicting Flavonoid UGT Regioselectivity with Graphical Residue Models and Machine Learning.
Machine learning is applied to a challenging and biologically significant protein classification problem: the prediction of flavonoid UGT acceptor regioselectivity from primary protein sequence. Novel indices characterizing graphical models of protein residues are introduced. The indices are compared with existing amino acid indices and found to cluster residues appropriately. A variety of models employing the indices are then investigated by examining their performance when analyzed using nearest neighbor, support vector machine, and Bayesian neural network classifiers. Improvements over nearest neighbor classifications relying on standard alignment similarity scores are reported
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