pwc-archive/papers-with-abstracts huggingface dataset:
Optimized Spatial Partitioning via Minimal Swarm Intelligence
Authors: Casey Kneale, Dominic Poerio, Karl S. Booksh
Date: 2017-01-19 00:00:00

Optimized spatial partitioning algorithms are the corner stone of many
successful experimental designs and statistical methods. Of these algorithms,
the Centroidal Voronoi Tessellation (CVT) is the most widely utilized. CVT
based methods require global knowledge of spatial boundaries, do not readily
allow for weighted regions, have challenging implementations, and are
inefficiently extended to high dimensional spaces. We describe two simple
partitioning schemes based on nearest and next nearest neighbor locations which
easily incorporate these features at the slight expense of optimal placement.
Several novel qualitative techniques which assess these partitioning schemes
are also included. The feasibility of autonomous uninformed sensor networks
utilizing these algorithms are considered. Some improvements in particle swarm
optimizer results on multimodal test functions from partitioned initial
positions in two space are also illustrated. Pseudo code for all of the novel
algorithms depicted here-in is available in the supplementary information of
this manuscript.
Deep Neural Networks - A Brief History
Authors: Krzysztof J. Cios
Date: 2017-01-19 00:00:00

Introduction to deep neural networks and their history.
A new look at reweighted message passing
Authors: Vladimir Kolmogorov
Date: 2013-09-22 00:00:00

We propose a new family of message passing techniques for MAP estimation in
graphical models which we call {\em Sequential Reweighted Message Passing}
(SRMP). Special cases include well-known techniques such as {\em Min-Sum
Diffusion} (MSD) and a faster {\em Sequential Tree-Reweighted Message Passing}
(TRW-S). Importantly, our derivation is simpler than the original derivation of
TRW-S, and does not involve a decomposition into trees. This allows easy
generalizations. We present such a generalization for the case of higher-order
graphical models, and test it on several real-world problems with promising
results.
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications
Authors: Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma
Date: 2017-01-19 00:00:00
Tasks: Density Estimation, Image Generation

PixelCNNs are a recently proposed class of powerful generative models with
tractable likelihood. Here we discuss our implementation of PixelCNNs which we
make available at https://github.com/openai/pixel-cnn. Our implementation
contains a number of modifications to the original model that both simplify its
structure and improve its performance. 1) We use a discretized logistic mixture
likelihood on the pixels, rather than a 256-way softmax, which we find to speed
up training. 2) We condition on whole pixels, rather than R/G/B sub-pixels,
simplifying the model structure. 3) We use downsampling to efficiently capture
structure at multiple resolutions. 4) We introduce additional short-cut
connections to further speed up optimization. 5) We regularize the model using
dropout. Finally, we present state-of-the-art log likelihood results on
CIFAR-10 to demonstrate the usefulness of these modifications.
A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery
Authors: Lingxiao Wang, Xiao Zhang, Quanquan Gu
Date: 2017-01-09 00:00:00

We propose a generic framework based on a new stochastic variance-reduced
gradient descent algorithm for accelerating nonconvex low-rank matrix recovery.
Starting from an appropriate initial estimator, our proposed algorithm performs
projected gradient descent based on a novel semi-stochastic gradient
specifically designed for low-rank matrix recovery. Based upon the mild
restricted strong convexity and smoothness conditions, we derive a projected
notion of the restricted Lipschitz continuous gradient property, and prove that
our algorithm enjoys linear convergence rate to the unknown low-rank matrix
with an improved computational complexity. Moreover, our algorithm can be
employed to both noiseless and noisy observations, where the optimal sample
complexity and the minimax optimal statistical rate can be attained
respectively. We further illustrate the superiority of our generic framework
through several specific examples, both theoretically and experimentally.
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects
Authors: Tomas Hodan, Pavel Haluza, Stepan Obdrzalek, Jiri Matas, Manolis Lourakis, Xenophon Zabulis
Date: 2017-01-19 00:00:00
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, Pose Estimation

We introduce T-LESS, a new public dataset for estimating the 6D pose, i.e.
translation and rotation, of texture-less rigid objects. The dataset features
thirty industry-relevant objects with no significant texture and no
discriminative color or reflectance properties. The objects exhibit symmetries
and mutual similarities in shape and/or size. Compared to other datasets, a
unique property is that some of the objects are parts of others. The dataset
includes training and test images that were captured with three synchronized
sensors, specifically a structured-light and a time-of-flight RGB-D sensor and
a high-resolution RGB camera. There are approximately 39K training and 10K test
images from each sensor. Additionally, two types of 3D models are provided for
each object, i.e. a manually created CAD model and a semi-automatically
reconstructed one. Training images depict individual objects against a black
background. Test images originate from twenty test scenes having varying
complexity, which increases from simple scenes with several isolated objects to
very challenging ones with multiple instances of several objects and with a
high amount of clutter and occlusion. The images were captured from a
systematically sampled view sphere around the object/scene, and are annotated
with accurate ground truth 6D poses of all modeled objects. Initial evaluation
results indicate that the state of the art in 6D object pose estimation has
ample room for improvement, especially in difficult cases with significant
occlusion. The T-LESS dataset is available online at cmp.felk.cvut.cz/t-less.
Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional Neural Networks
Authors: James Garland, David Gregg
Date: 2016-08-30 00:00:00

Convolutional Neural Networks (CNNs) are one of the most successful deep
machine learning technologies for processing image, voice and video data. CNNs
require large amounts of processing capacity and memory, which can exceed the
resources of low power mobile and embedded systems. Several designs for
hardware accelerators have been proposed for CNNs which typically contain large
numbers of Multiply Accumulate (MAC) units. One approach to reducing data sizes
and memory traffic in CNN accelerators is "weight sharing", where the full
range of values in a trained CNN are put in bins and the bin index is stored
instead of the original weight value. In this paper we propose a novel MAC
circuit that exploits binning in weight-sharing CNNs. Rather than computing the
MAC directly we instead count the frequency of each weight and place it in a
bin. We then compute the accumulated value in a subsequent multiply phase. This
allows hardware multipliers in the MAC circuit to be replaced with adders and
selection logic. Experiments show that for the same clock speed our approach
results in fewer gates, smaller logic, and reduced power.
Liquid Democracy: An Analysis in Binary Aggregation and Diffusion
Authors: Zoé Christoff, Davide Grossi
Date: 2016-12-23 00:00:00

The paper proposes an analysis of liquid democracy (or, delegable proxy
voting) from the perspective of binary aggregation and of binary diffusion
models. We show how liquid democracy on binary issues can be embedded into the
framework of binary aggregation with abstentions, enabling the transfer of
known results about the latter---such as impossibility theorems---to the
former. This embedding also sheds light on the relation between delegation
cycles in liquid democracy and the probability of collective abstentions, as
well as the issue of individual rationality in a delegable proxy voting
setting. We then show how liquid democracy on binary issues can be modeled and
analyzed also as a specific process of dynamics of binary opinions on networks.
These processes---called Boolean DeGroot processes---are a special case of the
DeGroot stochastic model of opinion diffusion. We establish the convergence
conditions of such processes and show they provide some novel insights on how
the effects of delegation cycles and individual rationality could be mitigated
within liquid democracy.
  The study is a first attempt to provide theoretical foundations to the
delgable proxy features of the liquid democracy voting system. Our analysis
suggests recommendations on how the system may be modified to make it more
resilient with respect to the handling of delegation cycles and of inconsistent
majorities.
Learning first-order definable concepts over structures of small degree
Authors: Martin Grohe, Martin Ritzert
Date: 2017-01-19 00:00:00
Tasks: BIG-bench Machine Learning

We consider a declarative framework for machine learning where concepts and
hypotheses are defined by formulas of a logic over some background structure.
We show that within this framework, concepts defined by first-order formulas
over a background structure of at most polylogarithmic degree can be learned in
polylogarithmic time in the "probably approximately correct" learning sense.
Joint Bayesian Gaussian discriminant analysis for speaker verification
Authors: Yiyan Wang, Haotian Xu, Zhijian Ou
Date: 2016-12-13 00:00:00
Tasks: Face Verification, Speaker Verification

State-of-the-art i-vector based speaker verification relies on variants of
Probabilistic Linear Discriminant Analysis (PLDA) for discriminant analysis. We
are mainly motivated by the recent work of the joint Bayesian (JB) method,
which is originally proposed for discriminant analysis in face verification. We
apply JB to speaker verification and make three contributions beyond the
original JB. 1) In contrast to the EM iterations with approximated statistics
in the original JB, the EM iterations with exact statistics are employed and
give better performance. 2) We propose to do simultaneous diagonalization (SD)
of the within-class and between-class covariance matrices to achieve efficient
testing, which has broader application scope than the SVD-based efficient
testing method in the original JB. 3) We scrutinize similarities and
differences between various Gaussian PLDAs and JB, complementing the previous
analysis of comparing JB only with Prince-Elder PLDA. Extensive experiments are
conducted on NIST SRE10 core condition 5, empirically validating the
superiority of JB with faster convergence rate and 9-13% EER reduction compared
with state-of-the-art PLDA.
Extreme value statistics for censored data with heavy tails under competing risks
Authors: Julien Worms, Rym Worms
Date: 2017-01-19 00:00:00

This paper addresses the problem of estimating, in the presence of random
censoring as well as competing risks, the extreme value index of the
(sub)-distribution function associated to one particular cause, in the
heavy-tail case. Asymptotic normality of the proposed estimator (which has the
form of an Aalen-Johansen integral, and is the first estimator proposed in this
context) is established. A small simulation study exhibits its performances for
finite samples. Estimation of extreme quantiles of the cumulative incidence
function is also addressed.
Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition
Authors: Anoop Cherian, Piotr Koniusz, Stephen Gould
Date: 2017-01-19 00:00:00
Tasks: Action Recognition, Fine-grained Action Recognition, Object Recognition, Temporal Action Localization

Most successful deep learning algorithms for action recognition extend models
designed for image-based tasks such as object recognition to video. Such
extensions are typically trained for actions on single video frames or very
short clips, and then their predictions from sliding-windows over the video
sequence are pooled for recognizing the action at the sequence level. Usually
this pooling step uses the first-order statistics of frame-level action
predictions. In this paper, we explore the advantages of using higher-order
correlations; specifically, we introduce Higher-order Kernel (HOK) descriptors
generated from the late fusion of CNN classifier scores from all the frames in
a sequence. To generate these descriptors, we use the idea of kernel
linearization. Specifically, a similarity kernel matrix, which captures the
temporal evolution of deep classifier scores, is first linearized into kernel
feature maps. The HOK descriptors are then generated from the higher-order
co-occurrences of these feature maps, and are then used as input to a
video-level classifier. We provide experiments on two fine-grained action
recognition datasets and show that our scheme leads to state-of-the-art
results.
Moving to VideoKifu: the last steps toward a fully automatic record-keeping of a Go game
Authors: Mario Corsolini, Andrea Carta
Date: 2017-01-19 00:00:00

In a previous paper [ arXiv:1508.03269 ] we described the techniques we
successfully employed for automatically reconstructing the whole move sequence
of a Go game by means of a set of pictures. Now we describe how it is possible
to reconstruct the move sequence by means of a video stream (which may be
provided by an unattended webcam), possibly in real-time. Although the basic
algorithms remain the same, we will discuss the new problems that arise when
dealing with videos, with special care for the ones that could block a
real-time analysis and require an improvement of our previous techniques or
even a completely brand new approach. Eventually we present a number of
preliminary but positive experimental results supporting the effectiveness of
the software we are developing, built on the ideas here outlined.
Block-wise Lensless Compressive Camera
Authors: Xin Yuan, Gang Huang, Hong Jiang, Paul Wilford
Date: 2017-01-19 00:00:00
Tasks: Compressive Sensing, Image Reconstruction

The existing lensless compressive camera
($\text{L}^2\text{C}^2$)~\cite{Huang13ICIP} suffers from low capture rates,
resulting in low resolution images when acquired over a short time. In this
work, we propose a new regime to mitigate these drawbacks. We replace the
global-based compressive sensing used in the existing $\text{L}^2\text{C}^2$ by
the local block (patch) based compressive sensing. We use a single sensor for
each block, rather than for the entire image, thus forming a multiple but
spatially parallel sensor $\text{L}^2\text{C}^2$. This new camera retains the
advantages of existing $\text{L}^2\text{C}^2$ while leading to the following
additional benefits: 1) Since each block can be very small, {\em e.g.}$~8\times
8$ pixels, we only need to capture $\sim 10$ measurements to achieve reasonable
reconstruction. Therefore the capture time can be reduced significantly. 2) The
coding patterns used in each block can be the same, therefore the sensing
matrix is only of the block size compared to the entire image size in existing
$\text{L}^2\text{C}^2$. This saves the memory requirement of the sensing matrix
as well as speeds up the reconstruction. 3) Patch based image reconstruction is
fast and since real time stitching algorithms exist, we can perform real time
reconstruction. 4) These small blocks can be integrated to any desirable
number, leading to ultra high resolution images while retaining fast capture
rate and fast reconstruction. We develop multiple geometries of this block-wise
$\text{L}^2\text{C}^2$ in this paper. We have built prototypes of the proposed
block-wise $\text{L}^2\text{C}^2$ and demonstrated excellent results of real
data.
Dictionary Learning from Incomplete Data
Authors: Valeriya Naumova, Karin Schnass
Date: 2017-01-13 00:00:00
Tasks: Dictionary Learning, Image Inpainting

This paper extends the recently proposed and theoretically justified
iterative thresholding and $K$ residual means algorithm ITKrM to learning
dicionaries from incomplete/masked training data (ITKrMM). It further adapts
the algorithm to the presence of a low rank component in the data and provides
a strategy for recovering this low rank component again from incomplete data.
Several synthetic experiments show the advantages of incorporating information
about the corruption into the algorithm. Finally, image inpainting is
considered as application example, which demonstrates the superior performance
of ITKrMM in terms of speed at similar or better reconstruction quality
compared to its closest dictionary learning counterpart.
Simultaneous Food Localization and Recognition
Authors: Marc Bolaños, Petia Radeva
Date: 2016-04-27 00:00:00
Tasks: Nutrition, Object Localization

The development of automatic nutrition diaries, which would allow to keep
track objectively of everything we eat, could enable a whole new world of
possibilities for people concerned about their nutrition patterns. With this
purpose, in this paper we propose the first method for simultaneous food
localization and recognition. Our method is based on two main steps, which
consist in, first, produce a food activation map on the input image (i.e. heat
map of probabilities) for generating bounding boxes proposals and, second,
recognize each of the food types or food-related objects present in each
bounding box. We demonstrate that our proposal, compared to the most similar
problem nowadays - object localization, is able to obtain high precision and
reasonable recall levels with only a few bounding boxes. Furthermore, we show
that it is applicable to both conventional and egocentric images.
Profiling of OCR'ed Historical Texts Revisited
Authors: Florian Fink, Klaus-U. Schulz, Uwe Springmann
Date: 2017-01-19 00:00:00
Tasks: Optical Character Recognition (OCR)

In the absence of ground truth it is not possible to automatically determine
the exact spectrum and occurrences of OCR errors in an OCR'ed text. Yet, for
interactive postcorrection of OCR'ed historical printings it is extremely
useful to have a statistical profile available that provides an estimate of
error classes with associated frequencies, and that points to conjectured
errors and suspicious tokens. The method introduced in Reffle (2013) computes
such a profile, combining lexica, pattern sets and advanced matching techniques
in a specialized Expectation Maximization (EM) procedure. Here we improve this
method in three respects: First, the method in Reffle (2013) is not adaptive:
user feedback obtained by actual postcorrection steps cannot be used to compute
refined profiles. We introduce a variant of the method that is open for
adaptivity, taking correction steps of the user into account. This leads to
higher precision with respect to recognition of erroneous OCR tokens. Second,
during postcorrection often new historical patterns are found. We show that
adding new historical patterns to the linguistic background resources leads to
a second kind of improvement, enabling even higher precision by telling
historical spellings apart from OCR errors. Third, the method in Reffle (2013)
does not make any active use of tokens that cannot be interpreted in the
underlying channel model. We show that adding these uninterpretable tokens to
the set of conjectured errors leads to a significant improvement of the recall
for error detection, at the same time improving precision.
3D Face Morphable Models "In-the-Wild"
Authors: James Booth, Epameinondas Antonakos, Stylianos Ploumpis, George Trigeorgis, Yannis Panagakis, Stefanos Zafeiriou
Date: 2017-01-19 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Face Reconstruction

3D Morphable Models (3DMMs) are powerful statistical models of 3D facial
shape and texture, and among the state-of-the-art methods for reconstructing
facial shape from single images. With the advent of new 3D sensors, many 3D
facial datasets have been collected containing both neutral as well as
expressive faces. However, all datasets are captured under controlled
conditions. Thus, even though powerful 3D facial shape models can be learnt
from such data, it is difficult to build statistical texture models that are
sufficient to reconstruct faces captured in unconstrained conditions
("in-the-wild"). In this paper, we propose the first, to the best of our
knowledge, "in-the-wild" 3DMM by combining a powerful statistical model of
facial shape, which describes both identity and expression, with an
"in-the-wild" texture model. We show that the employment of such an
"in-the-wild" texture model greatly simplifies the fitting procedure, because
there is no need to optimize with regards to the illumination parameters.
Furthermore, we propose a new fast algorithm for fitting the 3DMM in arbitrary
images. Finally, we have captured the first 3D facial database with relatively
unconstrained conditions and report quantitative evaluations with
state-of-the-art performance. Complementary qualitative reconstruction results
are demonstrated on standard "in-the-wild" facial databases. An open source
implementation of our technique is released as part of the Menpo Project.
A Joint Framework for Argumentative Text Analysis Incorporating Domain Knowledge
Authors: Zhongyu Wei, Chen Li, Yang Liu
Date: 2017-01-19 00:00:00
Tasks: General Classification, Relation, Relation Classification

For argumentation mining, there are several sub-tasks such as argumentation
component type classification, relation classification. Existing research tends
to solve such sub-tasks separately, but ignore the close relation between them.
In this paper, we present a joint framework incorporating logical relation
between sub-tasks to improve the performance of argumentation structure
generation. We design an objective function to combine the predictions from
individual models for each sub-task and solve the problem with some constraints
constructed from background knowledge. We evaluate our proposed model on two
public corpora and the experiment results show that our model can outperform
the baseline that uses a separate model significantly for each sub-task. Our
model also shows advantages on component-related sub-tasks compared to a
state-of-the-art joint model based on the evidence graph.