pwc-archive/papers-with-abstracts huggingface dataset:
Lyrics-to-Audio Alignment by Unsupervised Discovery of Repetitive Patterns in Vowel Acoustics
Authors: Sungkyun Chang, Kyogu Lee
Date: 2017-01-21 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Most of the previous approaches to lyrics-to-audio alignment used a
pre-developed automatic speech recognition (ASR) system that innately suffered
from several difficulties to adapt the speech model to individual singers. A
significant aspect missing in previous works is the self-learnability of
repetitive vowel patterns in the singing voice, where the vowel part used is
more consistent than the consonant part. Based on this, our system first learns
a discriminative subspace of vowel sequences, based on weighted symmetric
non-negative matrix factorization (WS-NMF), by taking the self-similarity of a
standard acoustic feature as an input. Then, we make use of canonical time
warping (CTW), derived from a recent computer vision technique, to find an
optimal spatiotemporal transformation between the text and the acoustic
sequences. Experiments with Korean and English data sets showed that deploying
this method after a pre-developed, unsupervised, singing source separation
achieved more promising results than other state-of-the-art unsupervised
approaches and an existing ASR-based system.
Motion Segmentation via Global and Local Sparse Subspace Optimization
Authors: Michael Ying Yang, Hanno Ackermann, Weiyao Lin, Sitong Feng, Bodo Rosenhahn
Date: 2017-01-24 00:00:00
Tasks: Clustering, Motion Segmentation, Segmentation

In this paper, we propose a new framework for segmenting feature-based moving
objects under affine subspace model. Since the feature trajectories in practice
are high-dimensional and contain a lot of noise, we firstly apply the sparse
PCA to represent the original trajectories with a low-dimensional global
subspace, which consists of the orthogonal sparse principal vectors.
Subsequently, the local subspace separation will be achieved via automatically
searching the sparse representation of the nearest neighbors for each projected
data. In order to refine the local subspace estimation result and deal with the
missing data problem, we propose an error estimation to encourage the projected
data that span a same local subspace to be clustered together. In the end, the
segmentation of different motions is achieved through the spectral clustering
on an affinity matrix, which is constructed with both the error estimation and
sparse neighbors optimization. We test our method extensively and compare it
with state-of-the-art methods on the Hopkins 155 dataset and Freiburg-Berkeley
Motion Segmentation dataset. The results show that our method is comparable
with the other motion segmentation methods, and in many cases exceed them in
terms of precision and computation time.
Randomized Primal-Dual Proximal Block Coordinate Updates
Authors: Xiang Gao, Yangyang Xu, Shuzhong Zhang
Date: 2016-05-19 00:00:00

In this paper we propose a randomized primal-dual proximal block coordinate
updating framework for a general multi-block convex optimization model with
coupled objective function and linear constraints. Assuming mere convexity, we
establish its $O(1/t)$ convergence rate in terms of the objective value and
feasibility measure. The framework includes several existing algorithms as
special cases such as a primal-dual method for bilinear saddle-point problems
(PD-S), the proximal Jacobian ADMM (Prox-JADMM) and a randomized variant of the
ADMM method for multi-block convex optimization. Our analysis recovers and/or
strengthens the convergence properties of several existing algorithms. For
example, for PD-S our result leads to the same order of convergence rate
without the previously assumed boundedness condition on the constraint sets,
and for Prox-JADMM the new result provides convergence rate in terms of the
objective value and the feasibility violation. It is well known that the
original ADMM may fail to converge when the number of blocks exceeds two. Our
result shows that if an appropriate randomization procedure is invoked to
select the updating blocks, then a sublinear rate of convergence in expectation
can be guaranteed for multi-block ADMM, without assuming any strong convexity.
The new approach is also extended to solve problems where only a stochastic
approximation of the (sub-)gradient of the objective is available, and we
establish an $O(1/\sqrt{t})$ convergence rate of the extended approach for
solving stochastic programming.
Sparse models for Computer Vision
Authors: Laurent Perrinet
Date: 2017-01-24 00:00:00

The representation of images in the brain is known to be sparse. That is, as
neural activity is recorded in a visual area ---for instance the primary visual
cortex of primates--- only a few neurons are active at a given time with
respect to the whole population. It is believed that such a property reflects
the efficient match of the representation with the statistics of natural
scenes. Applying such a paradigm to computer vision therefore seems a promising
approach towards more biomimetic algorithms. Herein, we will describe a
biologically-inspired approach to this problem. First, we will describe an
unsupervised learning paradigm which is particularly adapted to the efficient
coding of image patches. Then, we will outline a complete multi-scale framework
---SparseLets--- implementing a biologically inspired sparse representation of
natural images. Finally, we will propose novel methods for integrating prior
information into these algorithms and provide some preliminary experimental
results. We will conclude by giving some perspective on applying such
algorithms to computer vision. More specifically, we will propose that
bio-inspired approaches may be applied to computer vision using predictive
coding schemes, sparse models being one simple and efficient instance of such
schemes.
Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial "Bottleneck" Structure
Authors: Min Wang, Baoyuan Liu, Hassan Foroosh
Date: 2016-08-15 00:00:00

Deep convolutional neural networks achieve remarkable visual recognition
performance, at the cost of high computational complexity. In this paper, we
have a new design of efficient convolutional layers based on three schemes. The
3D convolution operation in a convolutional layer can be considered as
performing spatial convolution in each channel and linear projection across
channels simultaneously. By unravelling them and arranging the spatial
convolution sequentially, the proposed layer is composed of a single
intra-channel convolution, of which the computation is negligible, and a linear
channel projection. A topological subdivisioning is adopted to reduce the
connection between the input channels and output channels. Additionally, we
also introduce a spatial "bottleneck" structure that utilizes a
convolution-projection-deconvolution pipeline to take advantage of the
correlation between adjacent pixels in the input. Our experiments demonstrate
that the proposed layers remarkably outperform the standard convolutional
layers with regard to accuracy/complexity ratio. Our models achieve similar
accuracy to VGG, ResNet-50, ResNet-101 while requiring 42, 4.5, 6.5 times less
computation respectively.
Learning an attention model in an artificial visual system
Authors: Alon Hazan, Yuval Harel, Ron Meir
Date: 2017-01-24 00:00:00
Tasks: Reinforcement Learning

The Human visual perception of the world is of a large fixed image that is
highly detailed and sharp. However, receptor density in the retina is not
uniform: a small central region called the fovea is very dense and exhibits
high resolution, whereas a peripheral region around it has much lower spatial
resolution. Thus, contrary to our perception, we are only able to observe a
very small region around the line of sight with high resolution. The perception
of a complete and stable view is aided by an attention mechanism that directs
the eyes to the numerous points of interest within the scene. The eyes move
between these targets in quick, unconscious movements, known as "saccades".
Once a target is centered at the fovea, the eyes fixate for a fraction of a
second while the visual system extracts the necessary information. An
artificial visual system was built based on a fully recurrent neural network
set within a reinforcement learning protocol, and learned to attend to regions
of interest while solving a classification task. The model is consistent with
several experimentally observed phenomena, and suggests novel predictions.
Collective Vertex Classification Using Recursive Neural Network
Authors: Qiongkai Xu, Qing Wang, Chenchen Xu, Lizhen Qu
Date: 2017-01-24 00:00:00
Tasks: Classification, General Classification

Collective classification of vertices is a task of assigning categories to
each vertex in a graph based on both vertex attributes and link structure.
Nevertheless, some existing approaches do not use the features of neighbouring
vertices properly, due to the noise introduced by these features. In this
paper, we propose a graph-based recursive neural network framework for
collective vertex classification. In this framework, we generate hidden
representations from both attributes of vertices and representations of
neighbouring vertices via recursive neural networks. Under this framework, we
explore two types of recursive neural units, naive recursive neural unit and
long short-term memory unit. We have conducted experiments on four real-world
network datasets. The experimental results show that our frame- work with long
short-term memory model achieves better results and outperforms several
competitive baseline methods.
Robust mixture modelling using sub-Gaussian stable distribution
Authors: Mahdi Teimouri, Saeid Rezakhah, Adel Mohammdpour
Date: 2017-01-24 00:00:00

Heavy-tailed distributions are widely used in robust mixture modelling due to
possessing thick tails. As a computationally tractable subclass of the stable
distributions, sub-Gaussian $\alpha$-stable distribution received much interest
in the literature. Here, we introduce a type of expectation maximization
algorithm that estimates parameters of a mixture of sub-Gaussian stable
distributions. A comparative study, in the presence of some well-known mixture
models, is performed to show the robustness and performance of the mixture of
sub-Gaussian $\alpha$-stable distributions for modelling, simulated, synthetic,
and real data.
Proceedings of the 12th Workshop on User Interfaces for Theorem Provers
Authors: Serge Autexier, Pedro Quaresma
Date: 2017-01-24 00:00:00

The UITP workshop series brings together researchers interested in designing,
developing and evaluating user interfaces for automated reasoning tools, such
as interactive proof assistants, automated theorem provers, model finders,
tools for formal methods, and tools for visualising and manipulating logical
formulas and proofs. The twelth edition of UITP took place in Coimbra,
Portugal, and was part of the International Joint Conference on Automated
Reasoning (IJCAR'16). The workshop consisted of an invited talk, six
presentations of submitted papers and lively hands-on session for reasoning
tools and their user-interface. These post-proceedings contain four contributed
papers accepted for publication after a second round of reviewing after the
workshop as well as the invited paper.
A Contextual Bandit Approach for Stream-Based Active Learning
Authors: Linqi Song, Jie Xu
Date: 2017-01-24 00:00:00
Tasks: Active Learning, Decision Making, Sequential Decision Making

Contextual bandit algorithms -- a class of multi-armed bandit algorithms that
exploit the contextual information -- have been shown to be effective in
solving sequential decision making problems under uncertainty. A common
assumption adopted in the literature is that the realized (ground truth) reward
by taking the selected action is observed by the learner at no cost, which,
however, is not realistic in many practical scenarios. When observing the
ground truth reward is costly, a key challenge for the learner is how to
judiciously acquire the ground truth by assessing the benefits and costs in
order to balance learning efficiency and learning cost. From the information
theoretic perspective, a perhaps even more interesting question is how much
efficiency might be lost due to this cost. In this paper, we design a novel
contextual bandit-based learning algorithm and endow it with the active
learning capability. The key feature of our algorithm is that in addition to
sending a query to an annotator for the ground truth, prior information about
the ground truth learned by the learner is sent together, thereby reducing the
query cost. We prove that by carefully choosing the algorithm parameters, the
learning regret of the proposed algorithm achieves the same order as that of
conventional contextual bandit algorithms in cost-free scenarios, implying
that, surprisingly, cost due to acquiring the ground truth does not increase
the learning regret in the long-run. Our analysis shows that prior information
about the ground truth plays a critical role in improving the system
performance in scenarios where active learning is necessary.
Deep learning trends for focal brain pathology segmentation in MRI
Authors: Mohammad Havaei, Nicolas Guizard, Hugo Larochelle, Pierre-Marc Jodoin
Date: 2016-07-18 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Medical Diagnosis

Segmentation of focal (localized) brain pathologies such as brain tumors and
brain lesions caused by multiple sclerosis and ischemic strokes are necessary
for medical diagnosis, surgical planning and disease development as well as
other applications such as tractography. Over the years, attempts have been
made to automate this process for both clinical and research reasons. In this
regard, machine learning methods have long been a focus of attention. Over the
past two years, the medical imaging field has seen a rise in the use of a
particular branch of machine learning commonly known as deep learning. In the
non-medical computer vision world, deep learning based methods have obtained
state-of-the-art results on many datasets. Recent studies in computer aided
diagnostics have shown deep learning methods (and especially convolutional
neural networks - CNN) to yield promising results. In this chapter, we provide
a survey of CNN methods applied to medical imaging with a focus on brain
pathology segmentation. In particular, we discuss their characteristic
peculiarities and their specific configuration and adjustments that are best
suited to segment medical images. We also underline the intrinsic differences
deep learning methods have with other machine learning methods.
A graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery
Authors: Juheon Lee, David Coomes, Carola-Bibiane Schonlieb, Xiaohao Cai, Jan Lellmann, Michele Dalponte, Yadvinder Malhi, Nathalie Butt, Mike Morecroft
Date: 2017-01-24 00:00:00
Tasks: Computational Efficiency, Management

Recognising individual trees within remotely sensed imagery has important
applications in forest ecology and management. Several algorithms for tree
delineation have been suggested, mostly based on locating local maxima or
inverted basins in raster canopy height models (CHMs) derived from Light
Detection And Ranging (LiDAR) data or photographs. However, these algorithms
often lead to inaccurate estimates of forest stand characteristics due to the
limited information content of raster CHMs. Here we develop a 3D tree
delineation method which uses graph cut to delineate trees from the full 3D
LiDAR point cloud, and also makes use of any optical imagery available
(hyperspectral imagery in our case). First, conventional methods are used to
locate local maxima in the CHM and generate an initial map of trees. Second, a
graph is built from the LiDAR point cloud, fused with the hyperspectral data.
For computational efficiency, the feature space of hyperspectral imagery is
reduced using robust PCA. Third, a multi-class normalised cut is applied to the
graph, using the initial map of trees to constrain the number of clusters and
their locations. Finally, recursive normalised cut is used to subdivide, if
necessary, each of the clusters identified by the initial analysis. We call
this approach Multiclass Cut followed by Recursive Cut (MCRC). The
effectiveness of MCRC was tested using three datasets: i) NewFor, ii) a
coniferous forest in the Italian Alps, and iii) a deciduous woodland in the UK.
The performance of MCRC was usually superior to that of other delineation
methods, and was further improved by including high-resolution optical imagery.
Since MCRC delineates the entire LiDAR point cloud in 3D, it allows individual
crown characteristics to be measured. By making full use of the data available,
graph cut has the potential to considerably improve the accuracy of tree
delineation.
Speech Map: A Statistical Multimodal Atlas of 4D Tongue Motion During Speech from Tagged and Cine MR Images
Authors: Jonghye Woo, Fangxu Xing, Maureen Stone, Jordan Green, Timothy G. Reese, Thomas J. Brady, Van J. Wedeen, Jerry L. Prince, Georges El Fakhri
Date: 2017-01-24 00:00:00
Tasks: Motion Estimation

Quantitative measurement of functional and anatomical traits of 4D tongue
motion in the course of speech or other lingual behaviors remains a major
challenge in scientific research and clinical applications. Here, we introduce
a statistical multimodal atlas of 4D tongue motion using healthy subjects,
which enables a combined quantitative characterization of tongue motion in a
reference anatomical configuration. This atlas framework, termed Speech Map,
combines cine- and tagged-MRI in order to provide both the anatomic reference
and motion information during speech. Our approach involves a series of steps
including (1) construction of a common reference anatomical configuration from
cine-MRI, (2) motion estimation from tagged-MRI, (3) transformation of the
motion estimations to the reference anatomical configuration, and (4)
computation of motion quantities such as Lagrangian strain. Using this
framework, the anatomic configuration of the tongue appears motionless, while
the motion fields and associated strain measurements change over the time
course of speech. In addition, to form a succinct representation of the
high-dimensional and complex motion fields, principal component analysis is
carried out to characterize the central tendencies and variations of motion
fields of our speech tasks. Our proposed method provides a platform to
quantitatively and objectively explain the differences and variability of
tongue motion by illuminating internal motion and strain that have so far been
intractable. The findings are used to understand how tongue function for speech
is limited by abnormal internal motion and strain in glossectomy patients.
Learning to Generate Images with Perceptual Similarity Metrics
Authors: Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel
Date: 2015-11-19 00:00:00
Tasks: image-classification, Image Classification, Image Generation, MS-SSIM, SSIM, Super-Resolution

Deep networks are increasingly being applied to problems involving image
synthesis, e.g., generating images from textual descriptions and reconstructing
an input image from a compact representation. Supervised training of
image-synthesis networks typically uses a pixel-wise loss (PL) to indicate the
mismatch between a generated image and its corresponding target image. We
propose instead to use a loss function that is better calibrated to human
perceptual judgments of image quality: the multiscale structural-similarity
score (MS-SSIM). Because MS-SSIM is differentiable, it is easily incorporated
into gradient-descent learning. We compare the consequences of using MS-SSIM
versus PL loss on training deterministic and stochastic autoencoders. For three
different architectures, we collected human judgments of the quality of image
reconstructions. Observers reliably prefer images synthesized by
MS-SSIM-optimized models over those synthesized by PL-optimized models, for two
distinct PL measures ($\ell_1$ and $\ell_2$ distances). We also explore the
effect of training objective on image encoding and analyze conditions under
which perceptually-optimized representations yield better performance on image
classification. Finally, we demonstrate the superiority of
perceptually-optimized networks for super-resolution imaging. Just as computer
vision has advanced through the use of convolutional architectures that mimic
the structure of the mammalian visual system, we argue that significant
additional advances can be made in modeling images through the use of training
objectives that are well aligned to characteristics of human perception.
Information Extraction with Character-level Neural Networks and Free Noisy Supervision
Authors: Philipp Meerkamp, Zhengyi Zhou
Date: 2016-12-13 00:00:00

We present an architecture for information extraction from text that augments
an existing parser with a character-level neural network. The network is
trained using a measure of consistency of extracted data with existing
databases as a form of noisy supervision. Our architecture combines the ability
of constraint-based information extraction systems to easily incorporate domain
knowledge and constraints with the ability of deep neural networks to leverage
large amounts of data to learn complex features. Boosting the existing parser's
precision, the system led to large improvements over a mature and highly tuned
constraint-based production information extraction system used at Bloomberg for
financial language text.
Imitating Driver Behavior with Generative Adversarial Networks
Authors: Alex Kuefler, Jeremy Morton, Tim Wheeler, Mykel Kochenderfer
Date: 2017-01-24 00:00:00
Tasks: Imitation Learning

The ability to accurately predict and simulate human driving behavior is
critical for the development of intelligent transportation systems. Traditional
modeling methods have employed simple parametric models and behavioral cloning.
This paper adopts a method for overcoming the problem of cascading errors
inherent in prior approaches, resulting in realistic behavior that is robust to
trajectory perturbations. We extend Generative Adversarial Imitation Learning
to the training of recurrent policies, and we demonstrate that our model
outperforms rule-based controllers and maximum likelihood models in realistic
highway simulations. Our model both reproduces emergent behavior of human
drivers, such as lane change rate, while maintaining realistic control over
long time horizons.
Similarity Mapping with Enhanced Siamese Network for Multi-Object Tracking
Authors: Minyoung Kim, Stefano Alletto, Luca Rigazio
Date: 2016-09-28 00:00:00
Tasks: Multi-Object Tracking, Object Tracking

Multi-object tracking has recently become an important area of computer
vision, especially for Advanced Driver Assistance Systems (ADAS). Despite
growing attention, achieving high performance tracking is still challenging,
with state-of-the- art systems resulting in high complexity with a large number
of hyper parameters. In this paper, we focus on reducing overall system
complexity and the number hyper parameters that need to be tuned to a specific
environment. We introduce a novel tracking system based on similarity mapping
by Enhanced Siamese Neural Network (ESNN), which accounts for both appearance
and geometric information, and is trainable end-to-end. Our system achieves
competitive performance in both speed and accuracy on MOT16 challenge, compared
to known state-of-the-art methods.