pwc-archive/papers-with-abstracts huggingface dataset:
An Adaptive Psychoacoustic Model for Automatic Speech Recognition
Authors: Peng Dai, Xue Teng, Frank Rudzicz, Ing Yann Soon
Date: 2016-09-14 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), model, speech-recognition, Speech Recognition

Compared with automatic speech recognition (ASR), the human auditory system
is more adept at handling noise-adverse situations, including environmental
noise and channel distortion. To mimic this adeptness, auditory models have
been widely incorporated in ASR systems to improve their robustness. This paper
proposes a novel auditory model which incorporates psychoacoustics and
otoacoustic emissions (OAEs) into ASR. In particular, we successfully implement
the frequency-dependent property of psychoacoustic models and effectively
improve resulting system performance. We also present a novel double-transform
spectrum-analysis technique, which can qualitatively predict ASR performance
for different noise types. Detailed theoretical analysis is provided to show
the effectiveness of the proposed algorithm. Experiments are carried out on the
AURORA2 database and show that the word recognition rate using our proposed
feature extraction method is significantly increased over the baseline. Given
models trained with clean speech, our proposed method achieves up to 85.39%
word recognition accuracy on noisy data.
Relativistic Monte Carlo
Authors: Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer
Date: 2016-09-14 00:00:00

Hamiltonian Monte Carlo (HMC) is a popular Markov chain Monte Carlo (MCMC)
algorithm that generates proposals for a Metropolis-Hastings algorithm by
simulating the dynamics of a Hamiltonian system. However, HMC is sensitive to
large time discretizations and performs poorly if there is a mismatch between
the spatial geometry of the target distribution and the scales of the momentum
distribution. In particular the mass matrix of HMC is hard to tune well. In
order to alleviate these problems we propose relativistic Hamiltonian Monte
Carlo, a version of HMC based on relativistic dynamics that introduce a maximum
velocity on particles. We also derive stochastic gradient versions of the
algorithm and show that the resulting algorithms bear interesting relationships
to gradient clipping, RMSprop, Adagrad and Adam, popular optimisation methods
in deep learning. Based on this, we develop relativistic stochastic gradient
descent by taking the zero-temperature limit of relativistic stochastic
gradient Hamiltonian Monte Carlo. In experiments we show that the relativistic
algorithms perform better than classical Newtonian variants and Adam.
Testing $k$-Monotonicity
Authors: Clément L. Canonne, Elena Grigorescu, Siyao Guo, Akash Kumar, Karl Wimmer
Date: 2016-09-01 00:00:00
Tasks: Learning Theory

A Boolean $k$-monotone function defined over a finite poset domain ${\cal D}$
alternates between the values $0$ and $1$ at most $k$ times on any ascending
chain in ${\cal D}$. Therefore, $k$-monotone functions are natural
generalizations of the classical monotone functions, which are the $1$-monotone
functions. Motivated by the recent interest in $k$-monotone functions in the
context of circuit complexity and learning theory, and by the central role that
monotonicity testing plays in the context of property testing, we initiate a
systematic study of $k$-monotone functions, in the property testing model. In
this model, the goal is to distinguish functions that are $k$-monotone (or are
close to being $k$-monotone) from functions that are far from being
$k$-monotone. Our results include the following:
  - We demonstrate a separation between testing $k$-monotonicity and testing
monotonicity, on the hypercube domain $\{0,1\}^d$, for $k\geq 3$;
  - We demonstrate a separation between testing and learning on $\{0,1\}^d$,
for $k=\omega(\log d)$: testing $k$-monotonicity can be performed with
$2^{O(\sqrt d \cdot \log d\cdot \log{1/\varepsilon})}$ queries, while learning
$k$-monotone functions requires $2^{\Omega(k\cdot \sqrt
d\cdot{1/\varepsilon})}$ queries (Blais et al. (RANDOM 2015)).
  - We present a tolerant test for functions $f\colon[n]^d\to \{0,1\}$ with
complexity independent of $n$, which makes progress on a problem left open by
Berman et al. (STOC 2014).
  Our techniques exploit the testing-by-learning paradigm, use novel
applications of Fourier analysis on the grid $[n]^d$, and draw connections to
distribution testing techniques.
Egocentric Meets Top-view
Authors: Shervin Ardeshir, Ali Borji
Date: 2016-08-30 00:00:00
Tasks: Graph Matching

Thanks to the availability and increasing popularity of Egocentric cameras
such as GoPro cameras, glasses, and etc. we have been provided with a plethora
of videos captured from the first person perspective. Surveillance cameras and
Unmanned Aerial Vehicles(also known as drones) also offer tremendous amount of
videos, mostly with top-down or oblique view-point. Egocentric vision and
top-view surveillance videos have been studied extensively in the past in the
computer vision community. However, the relationship between the two has yet to
be explored thoroughly. In this effort, we attempt to explore this relationship
by approaching two questions. First, having a set of egocentric videos and a
top-view video, can we verify if the top-view video contains all, or some of
the egocentric viewers present in the egocentric set? And second, can we
identify the egocentric viewers in the content of the top-view video? In other
words, can we find the cameramen in the surveillance videos? These problems can
become more challenging when the videos are not time-synchronous. Thus we
formalize the problem in a way which handles and also estimates the unknown
relative time-delays between the egocentric videos and the top-view video. We
formulate the problem as a spectral graph matching instance, and jointly seek
the optimal assignments and relative time-delays of the videos. As a result, we
spatiotemporally localize the egocentric observers in the top-view video. We
model each view (egocentric or top) using a graph, and compute the assignment
and time-delays in an iterative-alternative fashion.
Combining Texture and Shape Cues for Object Recognition With Minimal Supervision
Authors: Xingchao Peng, Kate Saenko
Date: 2016-09-14 00:00:00
Tasks: General Classification, Image Retrieval, Object Recognition

We present a novel approach to object classification and detection which
requires minimal supervision and which combines visual texture cues and shape
information learned from freely available unlabeled web search results. The
explosion of visual data on the web can potentially make visual examples of
almost any object easily accessible via web search. Previous unsupervised
methods have utilized either large scale sources of texture cues from the web,
or shape information from data such as crowdsourced CAD models. We propose a
two-stream deep learning framework that combines these cues, with one stream
learning visual texture cues from image search data, and the other stream
learning rich shape information from 3D CAD models. To perform classification
or detection for a novel image, the predictions of the two streams are combined
using a late fusion scheme. We present experiments and visualizations for both
tasks on the standard benchmark PASCAL VOC 2007 to demonstrate that texture and
shape provide complementary information in our model. Our method outperforms
previous web image based models, 3D CAD model based approaches, and weakly
supervised models.
Oracle performance for visual captioning
Authors: Li Yao, Nicolas Ballas, Kyunghyun Cho, John R. Smith, Yoshua Bengio
Date: 2015-11-14 00:00:00
Tasks: Image Captioning, Language Modeling, Language Modelling, Video Captioning

The task of associating images and videos with a natural language description
has attracted a great amount of attention recently. Rapid progress has been
made in terms of both developing novel algorithms and releasing new datasets.
Indeed, the state-of-the-art results on some of the standard datasets have been
pushed into the regime where it has become more and more difficult to make
significant improvements. Instead of proposing new models, this work
investigates the possibility of empirically establishing performance upper
bounds on various visual captioning datasets without extra data labelling
effort or human evaluation. In particular, it is assumed that visual captioning
is decomposed into two steps: from visual inputs to visual concepts, and from
visual concepts to natural language descriptions. One would be able to obtain
an upper bound when assuming the first step is perfect and only requiring
training a conditional language model for the second step. We demonstrate the
construction of such bounds on MS-COCO, YouTube2Text and LSMDC (a combination
of M-VAD and MPII-MD). Surprisingly, despite of the imperfect process we used
for visual concept extraction in the first step and the simplicity of the
language model for the second step, we show that current state-of-the-art
models fall short when being compared with the learned upper bounds.
Furthermore, with such a bound, we quantify several important factors
concerning image and video captioning: the number of visual concepts captured
by different models, the trade-off between the amount of visual elements
captured and their accuracy, and the intrinsic difficulty and blessing of
different datasets.
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
Authors: Vadim Kantorov, Maxime Oquab, Minsu Cho, Ivan Laptev
Date: 2016-09-14 00:00:00
Tasks: Object, Object Localization, Object Recognition, Weakly Supervised Object Detection, Weakly-Supervised Object Localization

We aim to localize objects in images using image-level supervision only.
Previous approaches to this problem mainly focus on discriminative object
regions and often fail to locate precise object boundaries. We address this
problem by introducing two types of context-aware guidance models, additive and
contrastive models, that leverage their surrounding context regions to improve
localization. The additive model encourages the predicted object region to be
supported by its surrounding context region. The contrastive model encourages
the predicted object region to be outstanding from its surrounding context
region. Our approach benefits from the recent success of convolutional neural
networks for object recognition and extends Fast R-CNN to weakly supervised
object localization. Extensive experimental evaluation on the PASCAL VOC 2007
and 2012 benchmarks shows hat our context-aware approach significantly improves
weakly supervised localization and detection.
Structured Sparse Principal Components Analysis with the TV-Elastic Net penalty
Authors: Amicie de Pierrefeu, Tommy Löfstedt, Fouad Hadj-Selem, Mathieu Dubois, Philippe Ciuciu, Vincent Frouin, Edouard Duchesnay
Date: 2016-09-06 00:00:00

Principal component analysis (PCA) is an exploratory tool widely used in data
analysis to uncover dominant patterns of variability within a population.
Despite its ability to represent a data set in a low-dimensional space, the
interpretability of PCA remains limited. However, in neuroimaging, it is
essential to uncover clinically interpretable phenotypic markers that would
account for the main variability in the brain images of a population. Recently,
some alternatives to the standard PCA approach, such as Sparse PCA, have been
proposed, their aim being to limit the density of the components. Nonetheless,
sparsity alone does not entirely solve the interpretability problem, since it
may yield scattered and unstable components. We hypothesized that the
incorporation of prior information regarding the structure of the data may lead
to improved relevance and interpretability of brain patterns. We therefore
present a simple extension of the popular PCA framework that adds structured
sparsity penalties on the loading vectors in order to identify the few stable
regions in the brain images accounting for most of the variability. Such
structured sparsity can be obtained by combining l1 and total variation (TV)
penalties, where the TV regularization encodes higher order information about
the structure of the data. This paper presents the structured sparse PCA
(denoted SPCA-TV) optimization framework and its resolution. We demonstrate the
efficiency and versatility of SPCA-TV on three different data sets. The gains
of SPCA-TV over unstructured approaches are significant,since SPCA-TV reveals
the variability within a data set in the form of intelligible brain patterns
that are easy to interpret, and are more stable across different samples.
Transliteration in Any Language with Surrogate Languages
Authors: Stephen Mayhew, Christos Christodoulopoulos, Dan Roth
Date: 2016-09-14 00:00:00
Tasks: Transliteration

We introduce a method for transliteration generation that can produce
transliterations in every language. Where previous results are only as
multilingual as Wikipedia, we show how to use training data from Wikipedia as
surrogate training for any language. Thus, the problem becomes one of ranking
Wikipedia languages in order of suitability with respect to a target language.
We introduce several task-specific methods for ranking languages, and show that
our approach is comparable to the oracle ceiling, and even outperforms it in
some cases.
Very Simple Classifier: a Concept Binary Classifier toInvestigate Features Based on Subsampling and Localility
Authors: Luca Masera, Enrico Blanzieri
Date: 2016-09-14 00:00:00
Tasks: Binary Classification

We propose Very Simple Classifier (VSC) a novel method designed to
incorporate the concepts of subsampling and locality in the definition of
features to be used as the input of a perceptron. The rationale is that
locality theoretically guarantees a bound on the generalization error. Each
feature in VSC is a max-margin classifier built on randomly-selected pairs of
samples. The locality in VSC is achieved by multiplying the value of the
feature by a confidence measure that can be characterized in terms of the
Chebichev inequality. The output of the layer is then fed in a output layer of
neurons. The weights of the output layer are then determined by a regularized
pseudoinverse. Extensive comparison of VSC against 9 competitors in the task of
binary classification is carried out. Results on 22 benchmark datasets with
fixed parameters show that VSC is competitive with the Multi Layer Perceptron
(MLP) and outperforms the other competitors. An exploration of the parameter
space shows VSC can outperform MLP.
Blending LSTMs into CNNs
Authors: Krzysztof J. Geras, Abdel-rahman Mohamed, Rich Caruana, Gregor Urban, Shengjie Wang, Ozlem Aslan, Matthai Philipose, Matthew Richardson, Charles Sutton
Date: 2015-11-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Inductive Bias, Model Compression, speech-recognition, Speech Recognition

We consider whether deep convolutional networks (CNNs) can represent decision
functions with similar accuracy as recurrent networks such as LSTMs. First, we
show that a deep CNN with an architecture inspired by the models recently
introduced in image recognition can yield better accuracy than previous
convolutional and LSTM networks on the standard 309h Switchboard automatic
speech recognition task. Then we show that even more accurate CNNs can be
trained under the guidance of LSTMs using a variant of model compression, which
we call model blending because the teacher and student models are similar in
complexity but different in inductive bias. Blending further improves the
accuracy of our CNN, yielding a computationally efficient model of accuracy
higher than any of the other individual models. Examining the effect of "dark
knowledge" in this model compression task, we find that less than 1% of the
highest probability labels are needed for accurate model compression.
Gray-box inference for structured Gaussian process models
Authors: Pietro Galliani, Amir Dezfouli, Edwin V. Bonilla, Novi Quadrianto
Date: 2016-09-14 00:00:00
Tasks: Stochastic Optimization, Structured Prediction, Variational Inference

We develop an automated variational inference method for Bayesian structured
prediction problems with Gaussian process (GP) priors and linear-chain
likelihoods. Our approach does not need to know the details of the structured
likelihood model and can scale up to a large number of observations.
Furthermore, we show that the required expected likelihood term and its
gradients in the variational objective (ELBO) can be estimated efficiently by
using expectations over very low-dimensional Gaussian distributions.
Optimization of the ELBO is fully parallelizable over sequences and amenable to
stochastic optimization, which we use along with control variate techniques and
state-of-the-art incremental optimization to make our framework useful in
practice. Results on a set of natural language processing tasks show that our
method can be as good as (and sometimes better than) hard-coded approaches
including SVM-struct and CRFs, and overcomes the scalability limitations of
previous inference algorithms based on sampling. Overall, this is a fundamental
step to developing automated inference methods for Bayesian structured
prediction.
The LICORS Cabinet: Nonparametric Algorithms for Spatio-temporal Prediction
Authors: George D. Montanez, Cosma Rohilla Shalizi
Date: 2015-06-08 00:00:00
Tasks: Prediction

Spatio-temporal data is intrinsically high dimensional, so unsupervised
modeling is only feasible if we can exploit structure in the process. When the
dynamics are local in both space and time, this structure can be exploited by
splitting the global field into many lower-dimensional "light cones". We review
light cone decompositions for predictive state reconstruction, introducing
three simple light cone algorithms. These methods allow for tractable inference
of spatio-temporal data, such as full-frame video. The algorithms make few
assumptions on the underlying process yet have good predictive performance and
can provide distributions over spatio-temporal data, enabling sophisticated
probabilistic inference.
Clustering Time Series and the Surprising Robustness of HMMs
Authors: Mark Kozdoba, Shie Mannor
Date: 2016-05-09 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

Suppose that we are given a time series where consecutive samples are
believed to come from a probabilistic source, that the source changes from time
to time and that the total number of sources is fixed. Our objective is to
estimate the distributions of the sources. A standard approach to this problem
is to model the data as a hidden Markov model (HMM). However, since the data
often lacks the Markov or the stationarity properties of an HMM, one can ask
whether this approach is still suitable or perhaps another approach is
required. In this paper we show that a maximum likelihood HMM estimator can be
used to approximate the source distributions in a much larger class of models
than HMMs. Specifically, we propose a natural and fairly general non-stationary
model of the data, where the only restriction is that the sources do not change
too often. Our main result shows that for this model, a maximum-likelihood HMM
estimator produces the correct second moment of the data, and the results can
be extended to higher moments.
Neural Machine Transliteration: Preliminary Results
Authors: Amir H. Jadidinejad
Date: 2016-09-14 00:00:00
Tasks: Decoder, Transliteration

Machine transliteration is the process of automatically transforming the
script of a word from a source language to a target language, while preserving
pronunciation. Sequence to sequence learning has recently emerged as a new
paradigm in supervised learning. In this paper a character-based
encoder-decoder model has been proposed that consists of two Recurrent Neural
Networks. The encoder is a Bidirectional recurrent neural network that encodes
a sequence of symbols into a fixed-length vector representation, and the
decoder generates the target sequence using an attention-based recurrent neural
network. The encoder, the decoder and the attention mechanism are jointly
trained to maximize the conditional probability of a target sequence given a
source sequence. Our experiments on different datasets show that the proposed
encoder-decoder model is able to achieve significantly higher transliteration
quality over traditional statistical models.
Criteria of efficiency for conformal prediction
Authors: Vladimir Vovk, Ilia Nouretdinov, Valentina Fedorova, Ivan Petej, Alex Gammerman
Date: 2016-03-14 00:00:00
Tasks: Classification, Conformal Prediction, General Classification, Prediction

We study optimal conformity measures for various criteria of efficiency of
classification in an idealised setting. This leads to an important class of
criteria of efficiency that we call probabilistic; it turns out that the most
standard criteria of efficiency used in literature on conformal prediction are
not probabilistic unless the problem of classification is binary. We consider
both unconditional and label-conditional conformal prediction.
A Novel Term_Class Relevance Measure for Text Categorization
Authors: D. S. Guru, Mahamad Suhil
Date: 2016-08-25 00:00:00
Tasks: Text Categorization

In this paper, we introduce a new measure called Term_Class relevance to
compute the relevancy of a term in classifying a document into a particular
class. The proposed measure estimates the degree of relevance of a given term,
in placing an unlabeled document to be a member of a known class, as a product
of Class_Term weight and Class_Term density; where the Class_Term weight is the
ratio of the number of documents of the class containing the term to the total
number of documents containing the term and the Class_Term density is the
relative density of occurrence of the term in the class to the total occurrence
of the term in the entire population. Unlike the other existing term weighting
schemes such as TF-IDF and its variants, the proposed relevance measure takes
into account the degree of relative participation of the term across all
documents of the class to the entire population. To demonstrate the
significance of the proposed measure experimentation has been conducted on the
20 Newsgroups dataset. Further, the superiority of the novel measure is brought
out through a comparative analysis.
Doubly stochastic large scale kernel learning with the empirical kernel map
Authors: Nikolaas Steenbergen, Sebastian Schelter, Felix Bießmann
Date: 2016-09-02 00:00:00
Tasks: Stochastic Optimization

With the rise of big data sets, the popularity of kernel methods declined and
neural networks took over again. The main problem with kernel methods is that
the kernel matrix grows quadratically with the number of data points. Most
attempts to scale up kernel methods solve this problem by discarding data
points or basis functions of some approximation of the kernel map. Here we
present a simple yet effective alternative for scaling up kernel methods that
takes into account the entire data set via doubly stochastic optimization of
the emprical kernel map. The algorithm is straightforward to implement, in
particular in parallel execution settings; it leverages the full power and
versatility of classical kernel functions without the need to explicitly
formulate a kernel map approximation. We provide empirical evidence that the
algorithm works on large data sets.