43,490 research outputs found
Speech Recognition by Machine, A Review
This paper presents a brief survey on Automatic Speech Recognition and
discusses the major themes and advances made in the past 60 years of research,
so as to provide a technological perspective and an appreciation of the
fundamental progress that has been accomplished in this important area of
speech communication. After years of research and development the accuracy of
automatic speech recognition remains one of the important research challenges
(e.g., variations of the context, speakers, and environment).The design of
Speech Recognition system requires careful attentions to the following issues:
Definition of various types of speech classes, speech representation, feature
extraction techniques, speech classifiers, database and performance evaluation.
The problems that are existing in ASR and the various techniques to solve these
problems constructed by various research workers have been presented in a
chronological order. Hence authors hope that this work shall be a contribution
in the area of speech recognition. The objective of this review paper is to
summarize and compare some of the well known methods used in various stages of
speech recognition system and identify research topic and applications which
are at the forefront of this exciting and challenging field.Comment: 25 pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS December 2009, ISSN 1947 5500,
http://sites.google.com/site/ijcsis
Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks
A variety of real-world processes (over networks) produce sequences of data
whose complex temporal dynamics need to be studied. More especially, the event
timestamps can carry important information about the underlying network
dynamics, which otherwise are not available from the time-series evenly sampled
from continuous signals. Moreover, in most complex processes, event sequences
and evenly-sampled times series data can interact with each other, which
renders joint modeling of those two sources of data necessary. To tackle the
above problems, in this paper, we utilize the rich framework of (temporal)
point processes to model event data and timely update its intensity function by
the synergic twin Recurrent Neural Networks (RNNs). In the proposed
architecture, the intensity function is synergistically modulated by one RNN
with asynchronous events as input and another RNN with time series as input.
Furthermore, to enhance the interpretability of the model, the attention
mechanism for the neural point process is introduced. The whole model with
event type and timestamp prediction output layers can be trained end-to-end and
allows a black-box treatment for modeling the intensity. We substantiate the
superiority of our model in synthetic data and three real-world benchmark
datasets.Comment: 14 page
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks
Leveraging large historical data in electronic health record (EHR), we
developed Doctor AI, a generic predictive model that covers observed medical
conditions and medication uses. Doctor AI is a temporal model using recurrent
neural networks (RNN) and was developed and applied to longitudinal time
stamped EHR data from 260K patients over 8 years. Encounter records (e.g.
diagnosis codes, medication codes or procedure codes) were input to RNN to
predict (all) the diagnosis and medication categories for a subsequent visit.
Doctor AI assesses the history of patients to make multilabel predictions (one
label for each diagnosis or medication category). Based on separate blind test
set evaluation, Doctor AI can perform differential diagnosis with up to 79%
recall@30, significantly higher than several baselines. Moreover, we
demonstrate great generalizability of Doctor AI by adapting the resulting
models from one institution to another without losing substantial accuracy.Comment: Presented at 2016 Machine Learning and Healthcare Conference (MLHC
2016), Los Angeles, C
Forecasting Individualized Disease Trajectories using Interpretable Deep Learning
Disease progression models are instrumental in predicting individual-level
health trajectories and understanding disease dynamics. Existing models are
capable of providing either accurate predictions of patients prognoses or
clinically interpretable representations of disease pathophysiology, but not
both. In this paper, we develop the phased attentive state space (PASS) model
of disease progression, a deep probabilistic model that captures complex
representations for disease progression while maintaining clinical
interpretability. Unlike Markovian state space models which assume memoryless
dynamics, PASS uses an attention mechanism to induce "memoryful" state
transitions, whereby repeatedly updated attention weights are used to focus on
past state realizations that best predict future states. This gives rise to
complex, non-stationary state dynamics that remain interpretable through the
generated attention weights, which designate the relationships between the
realized state variables for individual patients. PASS uses phased LSTM units
(with time gates controlled by parametrized oscillations) to generate the
attention weights in continuous time, which enables handling
irregularly-sampled and potentially missing medical observations. Experiments
on data from a realworld cohort of patients show that PASS successfully
balances the tradeoff between accuracy and interpretability: it demonstrates
superior predictive accuracy and learns insightful individual-level
representations of disease progression
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing
This paper presents an empirical study of two widely-used sequence prediction
models, Conditional Random Fields (CRFs) and Long Short-Term Memory Networks
(LSTMs), on two fundamental tasks for Vietnamese text processing, including
part-of-speech tagging and named entity recognition. We show that a strong
lower bound for labeling accuracy can be obtained by relying only on simple
word-based features with minimal hand-crafted feature engineering, of 90.65\%
and 86.03\% performance scores on the standard test sets for the two tasks
respectively. In particular, we demonstrate empirically the surprising
efficiency of word embeddings in both of the two tasks, with both of the two
models. We point out that the state-of-the-art LSTMs model does not always
outperform significantly the traditional CRFs model, especially on
moderate-sized data sets. Finally, we give some suggestions and discussions for
efficient use of sequence labeling models in practical applications.Comment: To appear in the Proceedings of the 9th International Conference on
Knowledge and Systems Engineering (KSE) 201
Anomaly Detection and Modeling in 802.11 Wireless Networks
IEEE 802.11 Wireless Networks are getting more and more popular at university
campuses, enterprises, shopping centers, airports and in so many other public
places, providing Internet access to a large crowd openly and quickly. The
wireless users are also getting more dependent on WiFi technology and therefore
demanding more reliability and higher performance for this vital technology.
However, due to unstable radio conditions, faulty equipment, and dynamic user
behavior among other reasons, there are always unpredictable performance
problems in a wireless covered area. Detection and prediction of such problems
is of great significance to network managers if they are to alleviate the
connectivity issues of the mobile users and provide a higher quality wireless
service. This paper aims to improve the management of the 802.11 wireless
networks by characterizing and modeling wireless usage patterns in a set of
anomalous scenarios that can occur in such networks. We apply time-invariant
(Gaussian Mixture Models) and time-variant (Hidden Markov Models) modeling
approaches to a dataset generated from a large production network and describe
how we use these models for anomaly detection. We then generate several common
anomalies on a Testbed network and evaluate the proposed anomaly detection
methodologies in a controlled environment. The experimental results of the
Testbed show that HMM outperforms GMM and yields a higher anomaly detection
ratio and a lower false alarm rate
A Fast and Accurate Performance Analysis of Beaconless IEEE 802.15.4 Multi-Hop Networks
We develop an approximate analytical technique for evaluating the performance
of multi-hop networks based on beaconless IEEE 802.15.4, a popular standard for
wireless sensor networks. The network comprises sensor nodes, which generate
measurement packets, relay nodes which only forward packets, and a data sink
(base station). We consider a detailed stochastic process at each node, and
analyse this process taking into account the interaction with neighboring nodes
via certain time averaged unknown variables (e.g., channel sensing rates,
collision probabilities, etc.). By coupling the analyses at various nodes, we
obtain fixed point equations that can be solved numerically to obtain the
unknown variables, thereby yielding approximations of time average performance
measures, such as packet discard probabilities and average queueing delays. The
model incorporates packet generation at the sensor nodes and queues at the
sensor nodes and relay nodes. We demonstrate the accuracy of our model by an
extensive comparison with simulations. As an additional assessment of the
accuracy of the model, we utilize it in an algorithm for sensor network design
with quality-of-service (QoS) objectives, and show that designs obtained using
our model actually satisfy the QoS constraints (as validated by simulating the
networks), and the predictions are accurate to well within 10% as compared to
the simulation results.Comment: arXiv admin note: text overlap with arXiv:1201.300
An EM Algorithm for Continuous-time Bivariate Markov Chains
We study properties and parameter estimation of finite-state homogeneous
continuous-time bivariate Markov chains. Only one of the two processes of the
bivariate Markov chain is observable. The general form of the bivariate Markov
chain studied here makes no assumptions on the structure of the generator of
the chain, and hence, neither the underlying process nor the observable process
is necessarily Markov. The bivariate Markov chain allows for simultaneous jumps
of the underlying and observable processes. Furthermore, the inter-arrival time
of observed events is phase-type. The bivariate Markov chain generalizes the
batch Markovian arrival process as well as the Markov modulated Markov process.
We develop an expectation-maximization (EM) procedure for estimating the
generator of a bivariate Markov chain, and we demonstrate its performance. The
procedure does not rely on any numerical integration or sampling scheme of the
continuous-time bivariate Markov chain. The proposed EM algorithm is equally
applicable to multivariate Markov chains
Long-term Forecasting using Higher Order Tensor RNNs
We present Higher-Order Tensor RNN (HOT-RNN), a novel family of neural
sequence architectures for multivariate forecasting in environments with
nonlinear dynamics. Long-term forecasting in such systems is highly
challenging, since there exist long-term temporal dependencies, higher-order
correlations and sensitivity to error propagation. Our proposed recurrent
architecture addresses these issues by learning the nonlinear dynamics directly
using higher-order moments and higher-order state transition functions.
Furthermore, we decompose the higher-order structure using the tensor-train
decomposition to reduce the number of parameters while preserving the model
performance. We theoretically establish the approximation guarantees and the
variance bound for HOT-RNN for general sequence inputs. We also demonstrate 5%
~ 12% improvements for long-term prediction over general RNN and LSTM
architectures on a range of simulated environments with nonlinear dynamics, as
well on real-world time series data.Comment: 24 pages including appendix, updated JMLR versio
Non-Markovian Control with Gated End-to-End Memory Policy Networks
Partially observable environments present an important open challenge in the
domain of sequential control learning with delayed rewards. Despite numerous
attempts during the two last decades, the majority of reinforcement learning
algorithms and associated approximate models, applied to this context, still
assume Markovian state transitions. In this paper, we explore the use of a
recently proposed attention-based model, the Gated End-to-End Memory Network,
for sequential control. We call the resulting model the Gated End-to-End Memory
Policy Network. More precisely, we use a model-free value-based algorithm to
learn policies for partially observed domains using this memory-enhanced neural
network. This model is end-to-end learnable and it features unbounded memory.
Indeed, because of its attention mechanism and associated non-parametric
memory, the proposed model allows us to define an attention mechanism over the
observation stream unlike recurrent models. We show encouraging results that
illustrate the capability of our attention-based model in the context of the
continuous-state non-stationary control problem of stock trading. We also
present an OpenAI Gym environment for simulated stock exchange and explain its
relevance as a benchmark for the field of non-Markovian decision process
learning.Comment: 11 pages, 1 figure, 1 tabl
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