pwc-archive/papers-with-abstracts huggingface dataset:
Median-Based Generation of Synthetic Speech Durations using a Non-Parametric Approach
Authors: Srikanth Ronanki, Oliver Watts, Simon King, Gustav Eje Henter
Date: 2016-08-22 00:00:00
Tasks: Speech Synthesis

This paper proposes a new approach to duration modelling for statistical
parametric speech synthesis in which a recurrent statistical model is trained
to output a phone transition probability at each timestep (acoustic frame).
Unlike conventional approaches to duration modelling -- which assume that
duration distributions have a particular form (e.g., a Gaussian) and use the
mean of that distribution for synthesis -- our approach can in principle model
any distribution supported on the non-negative integers. Generation from this
model can be performed in many ways; here we consider output generation based
on the median predicted duration. The median is more typical (more probable)
than the conventional mean duration, is robust to training-data irregularities,
and enables incremental generation. Furthermore, a frame-level approach to
duration prediction is consistent with a longer-term goal of modelling
durations and acoustic features together. Results indicate that the proposed
method is competitive with baseline approaches in approximating the median
duration of held-out natural speech.
Modeling Missing Data in Clinical Time Series with RNNs
Authors: Zachary C. Lipton, David C. Kale, Randall Wetzel
Date: 2016-06-13 00:00:00
Tasks: General Classification, Imputation, Time Series, Time Series Analysis

We demonstrate a simple strategy to cope with missing data in sequential
inputs, addressing the task of multilabel classification of diagnoses given
clinical time series. Collected from the pediatric intensive care unit (PICU)
at Children's Hospital Los Angeles, our data consists of multivariate time
series of observations. The measurements are irregularly spaced, leading to
missingness patterns in temporally discretized sequences. While these artifacts
are typically handled by imputation, we achieve superior predictive performance
by treating the artifacts as features. Unlike linear models, recurrent neural
networks can realize this improvement using only simple binary indicators of
missingness. For linear models, we show an alternative strategy to capture this
signal. Training models on missingness patterns only, we show that for some
diseases, what tests are run can be as predictive as the results themselves.
Deep Convolutional Neural Network for Inverse Problems in Imaging
Authors: Kyong Hwan Jin, Michael T. McCann, Emmanuel Froustey, Michael Unser
Date: 2016-11-11 00:00:00
Tasks: GPU

In this paper, we propose a novel deep convolutional neural network
(CNN)-based algorithm for solving ill-posed inverse problems. Regularized
iterative algorithms have emerged as the standard approach to ill-posed inverse
problems in the past few decades. These methods produce excellent results, but
can be challenging to deploy in practice due to factors including the high
computational cost of the forward and adjoint operators and the difficulty of
hyper parameter selection. The starting point of our work is the observation
that unrolled iterative methods have the form of a CNN (filtering followed by
point-wise non-linearity) when the normal operator (H*H, the adjoint of H times
H) of the forward model is a convolution. Based on this observation, we propose
using direct inversion followed by a CNN to solve normal-convolutional inverse
problems. The direct inversion encapsulates the physical model of the system,
but leads to artifacts when the problem is ill-posed; the CNN combines
multiresolution decomposition and residual learning in order to learn to remove
these artifacts while preserving image structure. We demonstrate the
performance of the proposed network in sparse-view reconstruction (down to 50
views) on parallel beam X-ray computed tomography in synthetic phantoms as well
as in real experimental sinograms. The proposed network outperforms total
variation-regularized iterative reconstruction for the more realistic phantoms
and requires less than a second to reconstruct a 512 x 512 image on GPU.
Oriented bounding boxes using multiresolution contours for fast interference detection of arbitrary geometry objects
Authors: L. A. Rivera, Vania V. Estrela, P. C. P. Carvalho
Date: 2016-11-11 00:00:00

Interference detection of arbitrary geometric objects is not a trivial task
due to the heavy computational load imposed by implementation issues. The
hierarchically structured bounding boxes help us to quickly isolate the contour
of segments in interference. In this paper, a new approach is introduced to
treat the interference detection problem involving the representation of
arbitrary shaped objects. Our proposed method relies upon searching for the
best possible way to represent contours by means of hierarchically structured
rectangular oriented bounding boxes. This technique handles 2D objects
boundaries defined by closed B-spline curves with roughness details. Each
oriented box is adapted and fitted to the segments of the contour using second
order statistical indicators from some elements of the segments of the object
contour in a multiresolution framework. Our method is efficient and robust when
it comes to 2D animations in real time. It can deal with smooth curves and
polygonal approximations as well results are present to illustrate the
performance of the new method.
A Bayesian Network Model for Interesting Itemsets
Authors: Jaroslav Fowkes, Charles Sutton
Date: 2015-10-14 00:00:00
Tasks: model

Mining itemsets that are the most interesting under a statistical model of
the underlying data is a commonly used and well-studied technique for
exploratory data analysis, with the most recent interestingness models
exhibiting state of the art performance. Continuing this highly promising line
of work, we propose the first, to the best of our knowledge, generative model
over itemsets, in the form of a Bayesian network, and an associated novel
measure of interestingness. Our model is able to efficiently infer interesting
itemsets directly from the transaction database using structural EM, in which
the E-step employs the greedy approximation to weighted set cover. Our approach
is theoretically simple, straightforward to implement, trivially parallelizable
and retrieves itemsets whose quality is comparable to, if not better than,
existing state of the art algorithms as we demonstrate on several real-world
datasets.
The optimality of attaching unlinked labels to unlinked meanings
Authors: Ramon Ferrer-i-Cancho
Date: 2013-10-22 00:00:00

Vocabulary learning by children can be characterized by many biases. When
encountering a new word, children as well as adults, are biased towards
assuming that it means something totally different from the words that they
already know. To the best of our knowledge, the 1st mathematical proof of the
optimality of this bias is presented here. First, it is shown that this bias is
a particular case of the maximization of mutual information between words and
meanings. Second, the optimality is proven within a more general information
theoretic framework where mutual information maximization competes with other
information theoretic principles. The bias is a prediction from modern
information theory. The relationship between information theoretic principles
and the principles of contrast and mutual exclusivity is also shown.
Contradiction Detection for Rumorous Claims
Authors: Piroska Lendvai, Uwe D. Reichel
Date: 2016-11-08 00:00:00
Proceeding: WS 2016 12
Tasks: Natural Language Inference

The utilization of social media material in journalistic workflows is
increasing, demanding automated methods for the identification of mis- and
disinformation. Since textual contradiction across social media posts can be a
signal of rumorousness, we seek to model how claims in Twitter posts are being
textually contradicted. We identify two different contexts in which
contradiction emerges: its broader form can be observed across independently
posted tweets and its more specific form in threaded conversations. We define
how the two scenarios differ in terms of central elements of argumentation:
claims and conversation structure. We design and evaluate models for the two
scenarios uniformly as 3-way Recognizing Textual Entailment tasks in order to
represent claims and conversation structure implicitly in a generic inference
model, while previous studies used explicit or no representation of these
properties. To address noisy text, our classifiers use simple similarity
features derived from the string and part-of-speech level. Corpus statistics
reveal distribution differences for these features in contradictory as opposed
to non-contradictory tweet relations, and the classifiers yield state of the
art performance.
Show me the material evidence: Initial experiments on evaluating hypotheses from user-generated multimedia data
Authors: Bernardo Gonçalves
Date: 2016-11-11 00:00:00

Subjective questions such as `does neymar dive', or `is clinton lying', or
`is trump a fascist', are popular queries to web search engines, as can be seen
by autocompletion suggestions on Google, Yahoo and Bing. In the era of
cognitive computing, beyond search, they could be handled as hypotheses issued
for evaluation. Our vision is to leverage on unstructured data and metadata of
the rich user-generated multimedia that is often shared as material evidence in
favor or against hypotheses in social media platforms. In this paper we present
two preliminary experiments along those lines and discuss challenges for a
cognitive computing system that collects material evidence from user-generated
multimedia towards aggregating it into some form of collective decision on the
hypothesis.
A Subsequence Interleaving Model for Sequential Pattern Mining
Authors: Jaroslav Fowkes, Charles Sutton
Date: 2016-02-16 00:00:00
Tasks: model, Sequential Pattern Mining

Recent sequential pattern mining methods have used the minimum description
length (MDL) principle to define an encoding scheme which describes an
algorithm for mining the most compressing patterns in a database. We present a
novel subsequence interleaving model based on a probabilistic model of the
sequence database, which allows us to search for the most compressing set of
patterns without designing a specific encoding scheme. Our proposed algorithm
is able to efficiently mine the most relevant sequential patterns and rank them
using an associated measure of interestingness. The efficient inference in our
model is a direct result of our use of a structural expectation-maximization
framework, in which the expectation-step takes the form of a submodular
optimization problem subject to a coverage constraint. We show on both
synthetic and real world datasets that our model mines a set of sequential
patterns with low spuriousness and redundancy, high interpretability and
usefulness in real-world applications. Furthermore, we demonstrate that the
quality of the patterns from our approach is comparable to, if not better than,
existing state of the art sequential pattern mining algorithms.
Deeply-Recursive Convolutional Network for Image Super-Resolution
Authors: Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee
Date: 2015-11-14 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Super-Resolution, Super-Resolution

We propose an image super-resolution method (SR) using a deeply-recursive
convolutional network (DRCN). Our network has a very deep recursive layer (up
to 16 recursions). Increasing recursion depth can improve performance without
introducing new parameters for additional convolutions. Albeit advantages,
learning a DRCN is very hard with a standard gradient descent method due to
exploding/vanishing gradients. To ease the difficulty of training, we propose
two extensions: recursive-supervision and skip-connection. Our method
outperforms previous methods by a large margin.
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Authors: Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee
Date: 2015-11-14 00:00:00
Proceeding: CVPR 2016 6
Tasks: General Classification, Image Super-Resolution, Super-Resolution, Video Super-Resolution

We present a highly accurate single-image super-resolution (SR) method. Our
method uses a very deep convolutional network inspired by VGG-net used for
ImageNet classification \cite{simonyan2015very}. We find increasing our network
depth shows a significant improvement in accuracy. Our final model uses 20
weight layers. By cascading small filters many times in a deep network
structure, contextual information over large image regions is exploited in an
efficient way. With very deep networks, however, convergence speed becomes a
critical issue during training. We propose a simple yet effective training
procedure. We learn residuals only and use extremely high learning rates
($10^4$ times higher than SRCNN \cite{dong2015image}) enabled by adjustable
gradient clipping. Our proposed method performs better than existing methods in
accuracy and visual improvements in our results are easily noticeable.
Greedy Step Averaging: A parameter-free stochastic optimization method
Authors: Xiatian Zhang, Fan Yao, Yongjun Tian
Date: 2016-11-11 00:00:00
Tasks: Stochastic Optimization

In this paper we present the greedy step averaging(GSA) method, a
parameter-free stochastic optimization algorithm for a variety of machine
learning problems. As a gradient-based optimization method, GSA makes use of
the information from the minimizer of a single sample's loss function, and
takes average strategy to calculate reasonable learning rate sequence. While
most existing gradient-based algorithms introduce an increasing number of hyper
parameters or try to make a trade-off between computational cost and
convergence rate, GSA avoids the manual tuning of learning rate and brings in
no more hyper parameters or extra cost. We perform exhaustive numerical
experiments for logistic and softmax regression to compare our method with the
other state of the art ones on 16 datasets. Results show that GSA is robust on
various scenarios.
Deep Recurrent Neural Network for Mobile Human Activity Recognition with High Throughput
Authors: Masaya Inoue, Sozo Inoue, Takeshi Nishida
Date: 2016-11-11 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

In this paper, we propose a method of human activity recognition with high
throughput from raw accelerometer data applying a deep recurrent neural network
(DRNN), and investigate various architectures and its combination to find the
best parameter values. The "high throughput" refers to short time at a time of
recognition. We investigated various parameters and architectures of the DRNN
by using the training dataset of 432 trials with 6 activity classes from 7
people. The maximum recognition rate was 95.42% and 83.43% against the test
data of 108 segmented trials each of which has single activity class and 18
multiple sequential trials, respectively. Here, the maximum recognition rates
by traditional methods were 71.65% and 54.97% for each. In addition, the
efficiency of the found parameters was evaluated by using additional dataset.
Further, as for throughput of the recognition per unit time, the constructed
DRNN was requiring only 1.347 [ms], while the best traditional method required
11.031 [ms] which includes 11.027 [ms] for feature calculation. These
advantages are caused by the compact and small architecture of the constructed
real time oriented DRNN.
UTCNN: a Deep Learning Model of Stance Classificationon on Social Media Text
Authors: Wei-Fan Chen, Lun-Wei Ku
Date: 2016-11-11 00:00:00
Tasks: Document Classification

Most neural network models for document classification on social media focus
on text infor-mation to the neglect of other information on these platforms. In
this paper, we classify post stance on social media channels and develop UTCNN,
a neural network model that incorporates user tastes, topic tastes, and user
comments on posts. UTCNN not only works on social media texts, but also
analyzes texts in forums and message boards. Experiments performed on Chinese
Facebook data and English online debate forum data show that UTCNN achieves a
0.755 macro-average f-score for supportive, neutral, and unsupportive stance
classes on Facebook data, which is significantly better than models in which
either user, topic, or comment information is withheld. This model design
greatly mitigates the lack of data for the minor class without the use of
oversampling. In addition, UTCNN yields a 0.842 accuracy on English online
debate forum data, which also significantly outperforms results from previous
work as well as other deep learning models, showing that UTCNN performs well
regardless of language or platform.
An Information Criterion for Inferring Coupling in Distributed Dynamical Systems
Authors: Oliver M. Cliff, Mikhail Prokopenko, Robert Fitch
Date: 2016-05-23 00:00:00

The behaviour of many real-world phenomena can be modelled by nonlinear
dynamical systems whereby a latent system state is observed through a filter.
We are interested in interacting subsystems of this form, which we model by a
set of coupled maps as a synchronous update graph dynamical systems.
Specifically, we study the structure learning problem for spatially distributed
dynamical systems coupled via a directed acyclic graph. Unlike established
structure learning procedures that find locally maximum posterior probabilities
of a network structure containing latent variables, our work exploits the
properties of dynamical systems to compute globally optimal approximations of
these distributions. We arrive at this result by the use of time delay
embedding theorems. Taking an information-theoretic perspective, we show that
the log-likelihood has an intuitive interpretation in terms of information
transfer.
Continuous occurrence theory
Authors: Abdorrahman Haeri
Date: 2016-08-06 00:00:00

Usually gradual and continuous changes in entities will lead to appear
events. But usually it is supposed that an event is occurred at once. In this
research an integrated framework called continuous occurrence theory (COT) is
presented to investigate respective path leading to occurrence of the events in
the real world. For this purpose initially fundamental concepts are defined.
Afterwards, the appropriate tools such as occurrence variables computations,
occurrence dependency function and occurrence model are introduced and
explained in a systematic manner. Indeed, COT provides the possibility to: (a)
monitor occurrence of events during time; (b) study background of the events;
(c) recognize the relevant issues of each event; and (d) understand how these
issues affect on the considered event. The developed framework (COT) provides
the necessary context to analyze accurately continual changes of the issues and
the relevant events in the various branches of science and business. Finally,
typical applications of COT and an applied modeling example of it have been
explained and a mathematical programming example is modeled in the occurrence
based environment.
Generalized Entropies and the Similarity of Texts
Authors: Eduardo G. Altmann, Laercio Dias, Martin Gerlach
Date: 2016-11-11 00:00:00

We show how generalized Gibbs-Shannon entropies can provide new insights on
the statistical properties of texts. The universal distribution of word
frequencies (Zipf's law) implies that the generalized entropies, computed at
the word level, are dominated by words in a specific range of frequencies. Here
we show that this is the case not only for the generalized entropies but also
for the generalized (Jensen-Shannon) divergences, used to compute the
similarity between different texts. This finding allows us to identify the
contribution of specific words (and word frequencies) for the different
generalized entropies and also to estimate the size of the databases needed to
obtain a reliable estimation of the divergences. We test our results in large
databases of books (from the Google n-gram database) and scientific papers
(indexed by Web of Science).
Generation of Near-Optimal Solutions Using ILP-Guided Sampling
Authors: Ashwin Srinivasan, Gautam Shroff, Lovekesh Vig, Sarmimala Saikia, Puneet Agarwal
Date: 2016-08-03 00:00:00
Tasks: Heuristic Search, Inductive logic programming, Job Shop Scheduling, Scheduling

Our interest in this paper is in optimisation problems that are intractable
to solve by direct numerical optimisation, but nevertheless have significant
amounts of relevant domain-specific knowledge. The category of heuristic search
techniques known as estimation of distribution algorithms (EDAs) seek to
incrementally sample from probability distributions in which optimal (or
near-optimal) solutions have increasingly higher probabilities. Can we use
domain knowledge to assist the estimation of these distributions? To answer
this in the affirmative, we need: (a)a general-purpose technique for the
incorporation of domain knowledge when constructing models for optimal values;
and (b)a way of using these models to generate new data samples. Here we
investigate a combination of the use of Inductive Logic Programming (ILP) for
(a), and standard logic-programming machinery to generate new samples for (b).
Specifically, on each iteration of distribution estimation, an ILP engine is
used to construct a model for good solutions. The resulting theory is then used
to guide the generation of new data instances, which are now restricted to
those derivable using the ILP model in conjunction with the background
knowledge). We demonstrate the approach on two optimisation problems
(predicting optimal depth-of-win for the KRK endgame, and job-shop scheduling).
Our results are promising: (a)On each iteration of distribution estimation,
samples obtained with an ILP theory have a substantially greater proportion of
good solutions than samples without a theory; and (b)On termination of
distribution estimation, samples obtained with an ILP theory contain more
near-optimal samples than samples without a theory. Taken together, these
results suggest that the use of ILP-constructed theories could be a useful
technique for incorporating complex domain-knowledge into estimation
distribution procedures.
Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification
Authors: Qingshan Liu, Renlong Hang, Huihui Song, Zhi Li
Date: 2016-11-11 00:00:00
Tasks: General Classification, image-classification, Image Classification, Satellite Image Classification, Vocal Bursts Intensity Prediction

In this paper, we propose a multi-scale deep feature learning method for
high-resolution satellite image classification. Specifically, we firstly warp
the original satellite image into multiple different scales. The images in each
scale are employed to train a deep convolutional neural network (DCNN).
However, simultaneously training multiple DCNNs is time-consuming. To address
this issue, we explore DCNN with spatial pyramid pooling (SPP-net). Since
different SPP-nets have the same number of parameters, which share the
identical initial values, and only fine-tuning the parameters in
fully-connected layers ensures the effectiveness of each network, thereby
greatly accelerating the training process. Then, the multi-scale satellite
images are fed into their corresponding SPP-nets respectively to extract
multi-scale deep features. Finally, a multiple kernel learning method is
developed to automatically learn the optimal combination of such features.
Experiments on two difficult datasets show that the proposed method achieves
favorable performance compared to other state-of-the-art methods.