pwc-archive/papers-with-abstracts huggingface dataset:
Improved Regularization Techniques for End-to-End Speech Recognition
Authors: Yingbo Zhou, Caiming Xiong, Richard Socher
Date: 2017-12-19 00:00:00
Tasks: Data Augmentation, speech-recognition, Speech Recognition

Regularization is important for end-to-end speech models, since the models
are highly flexible and easy to overfit. Data augmentation and dropout has been
important for improving end-to-end models in other domains. However, they are
relatively under explored for end-to-end speech models. Therefore, we
investigate the effectiveness of both methods for end-to-end trainable, deep
speech recognition models. We augment audio data through random perturbations
of tempo, pitch, volume, temporal alignment, and adding random noise.We further
investigate the effect of dropout when applied to the inputs of all layers of
the network. We show that the combination of data augmentation and dropout give
a relative performance improvement on both Wall Street Journal (WSJ) and
LibriSpeech dataset of over 20%. Our model performance is also competitive with
other end-to-end speech models on both datasets.
On Data-Dependent Random Features for Improved Generalization in Supervised Learning
Authors: Shahin Shahrampour, Ahmad Beirami, Vahid Tarokh
Date: 2017-12-19 00:00:00

The randomized-feature approach has been successfully employed in large-scale
kernel approximation and supervised learning. The distribution from which the
random features are drawn impacts the number of features required to
efficiently perform a learning task. Recently, it has been shown that employing
data-dependent randomization improves the performance in terms of the required
number of random features. In this paper, we are concerned with the
randomized-feature approach in supervised learning for good generalizability.
We propose the Energy-based Exploration of Random Features (EERF) algorithm
based on a data-dependent score function that explores the set of possible
features and exploits the promising regions. We prove that the proposed score
function with high probability recovers the spectrum of the best fit within the
model class. Our empirical results on several benchmark datasets further verify
that our method requires smaller number of random features to achieve a certain
generalization error compared to the state-of-the-art while introducing
negligible pre-processing overhead. EERF can be implemented in a few lines of
code and requires no additional tuning parameters.
Improving End-to-End Speech Recognition with Policy Learning
Authors: Yingbo Zhou, Caiming Xiong, Richard Socher
Date: 2017-12-19 00:00:00
Tasks: speech-recognition, Speech Recognition

Connectionist temporal classification (CTC) is widely used for maximum
likelihood learning in end-to-end speech recognition models. However, there is
usually a disparity between the negative maximum likelihood and the performance
metric used in speech recognition, e.g., word error rate (WER). This results in
a mismatch between the objective function and metric during training. We show
that the above problem can be mitigated by jointly training with maximum
likelihood and policy gradient. In particular, with policy learning we are able
to directly optimize on the (otherwise non-differentiable) performance metric.
We show that joint training improves relative performance by 4% to 13% for our
end-to-end model as compared to the same model learned through maximum
likelihood. The model achieves 5.53% WER on Wall Street Journal dataset, and
5.42% and 14.70% on Librispeech test-clean and test-other set, respectively.
Column Generation for Interaction Coverage in Combinatorial Software Testing
Authors: Serdar Kadioglu
Date: 2017-12-19 00:00:00
Tasks: software testing

This paper proposes a novel column generation framework for combinatorial
software testing. In particular, it combines Mathematical Programming and
Constraint Programming in a hybrid decomposition to generate covering arrays.
The approach allows generating parameterized test cases with coverage
guarantees between parameter interactions of a given application. Compared to
exhaustive testing, combinatorial test case generation reduces the number of
tests to run significantly. Our column generation algorithm is generic and can
accommodate mixed coverage arrays over heterogeneous alphabets. The algorithm
is realized in practice as a cloud service and recognized as one of the five
winners of the company-wide cloud application challenge at Oracle. The service
is currently helping software developers from a range of different product
teams in their testing efforts while exposing declarative constraint models and
hybrid optimization techniques to a broader audience.
Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation
Authors: Jose Dolz, Christian Desrosiers, Li Wang, Jing Yuan, Dinggang Shen, Ismail Ben Ayed
Date: 2017-12-14 00:00:00
Tasks: Image Segmentation, Infant Brain Mri Segmentation, Medical Image Segmentation, MRI segmentation, Segmentation, Semantic Segmentation

Precise 3D segmentation of infant brain tissues is an essential step towards
comprehensive volumetric studies and quantitative analysis of early brain
developement. However, computing such segmentations is very challenging,
especially for 6-month infant brain, due to the poor image quality, among other
difficulties inherent to infant brain MRI, e.g., the isointense contrast
between white and gray matter and the severe partial volume effect due to small
brain sizes. This study investigates the problem with an ensemble of semi-dense
fully convolutional neural networks (CNNs), which employs T1-weighted and
T2-weighted MR images as input. We demonstrate that the ensemble agreement is
highly correlated with the segmentation errors. Therefore, our method provides
measures that can guide local user corrections. To the best of our knowledge,
this work is the first ensemble of 3D CNNs for suggesting annotations within
images. Furthermore, inspired by the very recent success of dense networks, we
propose a novel architecture, SemiDenseNet, which connects all convolutional
layers directly to the end of the network. Our architecture allows the
efficient propagation of gradients during training, while limiting the number
of parameters, requiring one order of magnitude less parameters than popular
medical image segmentation networks such as 3D U-Net. Another contribution of
our work is the study of the impact that early or late fusions of multiple
image modalities might have on the performances of deep architectures. We
report evaluations of our method on the public data of the MICCAI iSEG-2017
Challenge on 6-month infant brain MRI segmentation, and show very competitive
results among 21 teams, ranking first or second in most metrics.
The NarrativeQA Reading Comprehension Challenge
Authors: Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, Edward Grefenstette
Date: 2017-12-19 00:00:00
Proceeding: TACL 2018 1
Tasks: Information Retrieval, Question Answering, Reading Comprehension, Retrieval

Reading comprehension (RC)---in contrast to information retrieval---requires
integrating information and reasoning about events, entities, and their
relations across a full document. Question answering is conventionally used to
assess RC ability, in both artificial agents and children learning to read.
However, existing RC datasets and tasks are dominated by questions that can be
solved by selecting answers using superficial information (e.g., local context
similarity or global term frequency); they thus fail to test for the essential
integrative aspect of RC. To encourage progress on deeper comprehension of
language, we present a new dataset and set of tasks in which the reader must
answer questions about stories by reading entire books or movie scripts. These
tasks are designed so that successfully answering their questions requires
understanding the underlying narrative rather than relying on shallow pattern
matching or salience. We show that although humans solve the tasks easily,
standard RC models struggle on the tasks presented here. We provide an analysis
of the dataset and the challenges it presents.
Snake: a Stochastic Proximal Gradient Algorithm for Regularized Problems over Large Graphs
Authors: Adil Salim, Pascal Bianchi, Walid Hachem
Date: 2017-12-19 00:00:00

A regularized optimization problem over a large unstructured graph is
studied, where the regularization term is tied to the graph geometry. Typical
regularization examples include the total variation and the Laplacian
regularizations over the graph. When applying the proximal gradient algorithm
to solve this problem, there exist quite affordable methods to implement the
proximity operator (backward step) in the special case where the graph is a
simple path without loops. In this paper, an algorithm, referred to as "Snake",
is proposed to solve such regularized problems over general graphs, by taking
benefit of these fast methods. The algorithm consists in properly selecting
random simple paths in the graph and performing the proximal gradient algorithm
over these simple paths. This algorithm is an instance of a new general
stochastic proximal gradient algorithm, whose convergence is proven.
Applications to trend filtering and graph inpainting are provided among others.
Numerical experiments are conducted over large graphs.
Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks
Authors: Marzieh Haghighi, Simon K. Warfield, Sila Kurugol
Date: 2017-12-19 00:00:00
Tasks: Kidney Function, Segmentation

Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI)
images could help in diagnosis and treatment of kidney diseases of children.
Automatic segmentation of renal parenchyma is an important step in this
process. In this paper, we propose a time and memory efficient fully automated
segmentation method which achieves high segmentation accuracy with running time
in the order of seconds in both normal kidneys and kidneys with hydronephrosis.
The proposed method is based on a cascaded application of two 3D convolutional
neural networks that employs spatial and temporal information at the same time
in order to learn the tasks of localization and segmentation of kidneys,
respectively. Segmentation performance is evaluated on both normal and abnormal
kidneys with varying levels of hydronephrosis. We achieved a mean dice
coefficient of 91.4 and 83.6 for normal and abnormal kidneys of pediatric
patients, respectively.
The Merits of Sharing a Ride
Authors: Pooyan Ehsani, Jia Yuan Yu
Date: 2017-12-19 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

The culture of sharing instead of ownership is sharply increasing in
individuals behaviors. Particularly in transportation, concepts of sharing a
ride in either carpooling or ridesharing have been recently adopted. An
efficient optimization approach to match passengers in real-time is the core of
any ridesharing system. In this paper, we model ridesharing as an online
matching problem on general graphs such that passengers do not drive private
cars and use shared taxis. We propose an optimization algorithm to solve it.
The outlined algorithm calculates the optimal waiting time when a passenger
arrives. This leads to a matching with minimal overall overheads while
maximizing the number of partnerships. To evaluate the behavior of our
algorithm, we used NYC taxi real-life data set. Results represent a substantial
reduction in overall overheads.
Privacy-Preserving Adversarial Networks
Authors: Ardhendu Tripathy, Ye Wang, Prakash Ishwar
Date: 2017-12-19 00:00:00
Tasks: Privacy Preserving

We propose a data-driven framework for optimizing privacy-preserving data release mechanisms to attain the information-theoretically optimal tradeoff between minimizing distortion of useful data and concealing specific sensitive information. Our approach employs adversarially-trained neural networks to implement randomized mechanisms and to perform a variational approximation of mutual information privacy. We validate our Privacy-Preserving Adversarial Networks (PPAN) framework via proof-of-concept experiments on discrete and continuous synthetic data, as well as the MNIST handwritten digits dataset. For synthetic data, our model-agnostic PPAN approach achieves tradeoff points very close to the optimal tradeoffs that are analytically-derived from model knowledge. In experiments with the MNIST data, we visually demonstrate a learned tradeoff between minimizing the pixel-level distortion versus concealing the written digit.
Any-gram Kernels for Sentence Classification: A Sentiment Analysis Case Study
Authors: Rasoul Kaljahi, Jennifer Foster
Date: 2017-12-19 00:00:00
Tasks: General Classification, Sentence, Sentence Classification, Sentiment Analysis, Sentiment Classification, Word Embeddings

Any-gram kernels are a flexible and efficient way to employ bag-of-n-gram
features when learning from textual data. They are also compatible with the use
of word embeddings so that word similarities can be accounted for. While the
original any-gram kernels are implemented on top of tree kernels, we propose a
new approach which is independent of tree kernels and is more efficient. We
also propose a more effective way to make use of word embeddings than the
original any-gram formulation. When applied to the task of sentiment
classification, our new formulation achieves significantly better performance.
Bilinear residual Neural Network for the identification and forecasting of dynamical systems
Authors: Ronan Fablet, Said Ouala, Cedric Herzet
Date: 2017-12-19 00:00:00

Due to the increasing availability of large-scale observation and simulation
datasets, data-driven representations arise as efficient and relevant
computation representations of dynamical systems for a wide range of
applications, where model-driven models based on ordinary differential equation
remain the state-of-the-art approaches. In this work, we investigate neural
networks (NN) as physically-sound data-driven representations of such systems.
Reinterpreting Runge-Kutta methods as graphical models, we consider a residual
NN architecture and introduce bilinear layers to embed non-linearities which
are intrinsic features of dynamical systems. From numerical experiments for
classic dynamical systems, we demonstrate the relevance of the proposed
NN-based architecture both in terms of forecasting performance and model
identification.
GoDP: Globally optimized dual pathway system for facial landmark localization in-the-wild
Authors: Yuhang Wu, Shishir K. Shah, Ioannis A. Kakadiaris
Date: 2017-04-07 00:00:00
Tasks: Face Alignment, Face Identification, Face Recognition, Facial Landmark Detection, regression, Robust Face Recognition

Facial landmark localization is a fundamental module for pose-invariant face
recognition. The most common approach for facial landmark detection is cascaded
regression, which is composed of two steps: feature extraction and facial shape
regression. Recent methods employ deep convolutional networks to extract robust
features for each step, while the whole system could be regarded as a deep
cascaded regression architecture. In this work, instead of employing a deep
regression network, a Globally Optimized Dual-Pathway (GoDP) deep architecture
is proposed to identify the target pixels through solving a cascaded pixel
labeling problem without resorting to high-level inference models or complex
stacked architecture. The proposed end-to-end system relies on distance-aware
softmax functions and dual-pathway proposal-refinement architecture. Results
show that it outperforms the state-of-the-art cascaded regression-based methods
on multiple in-the-wild face alignment databases. The model achieves 1.84
normalized mean error (NME) on the AFLW database, which outperforms 3DDFA by
61.8%. Experiments on face identification demonstrate that GoDP, coupled with
DPM-headhunter, is able to improve rank-1 identification rate by 44.2% compared
to Dlib toolbox on a challenging database.
Mining Smart Card Data for Travelers' Mini Activities
Authors: Boris Chidlovskii
Date: 2017-12-19 00:00:00

In the context of public transport modeling and simulation, we address the
problem of mismatch between simulated transit trips and observed ones. We point
to the weakness of the current travel demand modeling process; the trips it
generates are over-optimistic and do not reflect the real passenger choices. We
introduce the notion of mini activities the travelers do during the trips; they
can explain the deviation of simulated trips from the observed trips. We
propose to mine the smart card data to extract the mini activities. We develop
a technique to integrate them in the generated trips and learn such an
integration from two available sources, the trip history and trip planner
recommendations. For an input travel demand, we build a Markov chain over the
trip collection and apply the Monte Carlo Markov Chain algorithm to integrate
mini activities in such a way that the selected characteristics converge to the
desired distributions. We test our method in different settings on the
passenger trip collection of Nancy, France. We report experimental results
demonstrating a very important mismatch reduction.
Bipartite Graph Matching for Keyframe Summary Evaluation
Authors: Iain A. D. Gunn, Ludmila I. Kuncheva, Paria Yousefi
Date: 2017-12-19 00:00:00
Tasks: Graph Matching

A keyframe summary, or "static storyboard", is a collection of frames from a
video designed to summarise its semantic content. Many algorithms have been
proposed to extract such summaries automatically. How best to evaluate these
outputs is an important but little-discussed question. We review the current
methods for matching frames between two summaries in the formalism of graph
theory. Our analysis revealed different behaviours of these methods, which we
illustrate with a number of case studies. Based on the results, we recommend a
greedy matching algorithm due to Kannappan et al.
ComboGAN: Unrestrained Scalability for Image Domain Translation
Authors: Asha Anoosheh, Eirikur Agustsson, Radu Timofte, Luc Van Gool
Date: 2017-12-19 00:00:00
Tasks: Image-to-Image Translation, Translation

This year alone has seen unprecedented leaps in the area of learning-based
image translation, namely CycleGAN, by Zhu et al. But experiments so far have
been tailored to merely two domains at a time, and scaling them to more would
require an quadratic number of models to be trained. And with two-domain models
taking days to train on current hardware, the number of domains quickly becomes
limited by the time and resources required to process them. In this paper, we
propose a multi-component image translation model and training scheme which
scales linearly - both in resource consumption and time required - with the
number of domains. We demonstrate its capabilities on a dataset of paintings by
14 different artists and on images of the four different seasons in the Alps.
Note that 14 data groups would need (14 choose 2) = 91 different CycleGAN
models: a total of 182 generator/discriminator pairs; whereas our model
requires only 14 generator/discriminator pairs.
On the Evaluation of Video Keyframe Summaries using User Ground Truth
Authors: Ludmila I. Kuncheva, Paria Yousefi, Iain A. D. Gunn
Date: 2017-12-19 00:00:00

Given the great interest in creating keyframe summaries from video, it is
surprising how little has been done to formalise their evaluation and
comparison. User studies are often carried out to demonstrate that a proposed
method generates a more appealing summary than one or two rival methods. But
larger comparison studies cannot feasibly use such user surveys. Here we
propose a discrimination capacity measure as a formal way to quantify the
improvement over the uniform baseline, assuming that one or more ground truth
summaries are available. Using the VSUMM video collection, we examine 10 video
feature types, including CNN and SURF, and 6 methods for matching frames from
two summaries. Our results indicate that a simple frame representation through
hue histograms suffices for the purposes of comparing keyframe summaries. We
subsequently propose a formal protocol for comparing summaries when ground
truth is available.
Learning Fixation Point Strategy for Object Detection and Classification
Authors: Jie Lyu, Zejian yuan, Dapeng Chen
Date: 2017-12-19 00:00:00
Tasks: Classification, General Classification, Object, object-detection, Object Detection

We propose a novel recurrent attentional structure to localize and recognize
objects jointly. The network can learn to extract a sequence of local
observations with detailed appearance and rough context, instead of sliding
windows or convolutions on the entire image. Meanwhile, those observations are
fused to complete detection and classification tasks. On training, we present a
hybrid loss function to learn the parameters of the multi-task network
end-to-end. Particularly, the combination of stochastic and object-awareness
strategy, named SA, can select more abundant context and ensure the last
fixation close to the object. In addition, we build a real-world dataset to
verify the capacity of our method in detecting the object of interest including
those small ones. Our method can predict a precise bounding box on an image,
and achieve high speed on large images without pooling operations. Experimental
results indicate that the proposed method can mine effective context by several
local observations. Moreover, the precision and speed are easily improved by
changing the number of recurrent steps. Finally, we will open the source code
of our proposed approach.
Comparison of fingerprint authentication algorithms for small imaging sensors
Authors: Mathilde Bourjot, Regis Perrier, Jean François Mainguet
Date: 2017-12-19 00:00:00

The demand for biometric systems has been increasing with the growth of the
smartphone market. Biometric devices allow the user to authenticate easily
while securing its private data without the need to remember any access code.
Amongst them, fingerprint sensors are the most widespread because they seem to
provide a good balance between reliability, cost and ease of use. According to
smartphone manufacturers, the security level is guaranteed to be high. However,
the size of those sensors, which is only a few millimeters squared, prevents
the use of minutiae algorithms. To the best of our knowledge, very few studies
shed light onto this problem, though many pattern recognition algorithms
already exist as well as commercial solutions which are supposedly robust. In
this article we try to provide insights on how to tackle this problem by
analyzing the performance of three algorithms dedicated to pattern recognition.
Analogy Mining for Specific Design Needs
Authors: Karni Gilon, Felicia Y Ng, Joel Chan, Hila Lifshitz Assaf, Aniket Kittur, Dafna Shahaf
Date: 2017-12-19 00:00:00

Finding analogical inspirations in distant domains is a powerful way of
solving problems. However, as the number of inspirations that could be matched
and the dimensions on which that matching could occur grow, it becomes
challenging for designers to find inspirations relevant to their needs.
Furthermore, designers are often interested in exploring specific aspects of a
product-- for example, one designer might be interested in improving the
brewing capability of an outdoor coffee maker, while another might wish to
optimize for portability. In this paper we introduce a novel system for
targeting analogical search for specific needs. Specifically, we contribute a
novel analogical search engine for expressing and abstracting specific design
needs that returns more distant yet relevant inspirations than alternate
approaches.