pwc-archive/papers-with-abstracts huggingface dataset:
Non-smooth Variable Projection
Authors: Tristan van Leeuwen, Aleksandr Aravkin
Date: 2016-01-19 00:00:00

Variable projection solves structured optimization problems by completely minimizing over a subset of the variables while iterating over the remaining variables. Over the last 30 years, the technique has been widely used, with empirical and theoretical results demonstrating both greater efficacy and greater stability compared to competing approaches. Classic examples have exploited closed-form projections and smoothness of the objective function. We extend the approach to problems that include non-smooth terms, and where the projection subproblems can only be solved inexactly by iterative methods. We propose an inexact adaptive algonrithm for solving such problems and analyze its computational complexity. Finally, we show how the theory can be used to design methods for selected problems occurring frequently in machine-learning and inverse problems.
Learning 3D Object Categories by Looking Around Them
Authors: David Novotny, Diane Larlus, Andrea Vedaldi
Date: 2017-05-10 00:00:00
Proceeding: ICCV 2017 10
Tasks: Data Augmentation, Object

Traditional approaches for learning 3D object categories use either synthetic data or manual supervision. In this paper, we propose a method which does not require manual annotations and is instead cued by observing objects from a moving vantage point. Our system builds on two innovations: a Siamese viewpoint factorization network that robustly aligns different videos together without explicitly comparing 3D shapes; and a 3D shape completion network that can extract the full shape of an object from partial observations. We also demonstrate the benefits of configuring networks to perform probabilistic predictions as well as of geometry-aware data augmentation schemes. We obtain state-of-the-art results on publicly-available benchmarks.
Logistic Regression as Soft Perceptron Learning
Authors: Raul Rojas
Date: 2017-08-24 00:00:00
Tasks: regression

We comment on the fact that gradient ascent for logistic regression has a
connection with the perceptron learning algorithm. Logistic learning is the
"soft" variant of perceptron learning.
Multi-View Stereo with Single-View Semantic Mesh Refinement
Authors: Andrea Romanoni, Marco Ciccone, Francesco Visin, Matteo Matteucci
Date: 2017-08-16 00:00:00
Tasks: 3D Reconstruction

While 3D reconstruction is a well-established and widely explored research
topic, semantic 3D reconstruction has only recently witnessed an increasing
share of attention from the Computer Vision community. Semantic annotations
allow in fact to enforce strong class-dependent priors, as planarity for ground
and walls, which can be exploited to refine the reconstruction often resulting
in non-trivial performance improvements. State-of-the art methods propose
volumetric approaches to fuse RGB image data with semantic labels; even if
successful, they do not scale well and fail to output high resolution meshes.
In this paper we propose a novel method to refine both the geometry and the
semantic labeling of a given mesh. We refine the mesh geometry by applying a
variational method that optimizes a composite energy made of a state-of-the-art
pairwise photo-metric term and a single-view term that models the semantic
consistency between the labels of the 3D mesh and those of the segmented
images. We also update the semantic labeling through a novel Markov Random
Field (MRF) formulation that, together with the classical data and smoothness
terms, takes into account class-specific priors estimated directly from the
annotated mesh. This is in contrast to state-of-the-art methods that are
typically based on handcrafted or learned priors. We are the first, jointly
with the very recent and seminal work of [M. Blaha et al arXiv:1706.08336,
2017], to propose the use of semantics inside a mesh refinement framework.
Differently from [M. Blaha et al arXiv:1706.08336, 2017], which adopts a more
classical pairwise comparison to estimate the flow of the mesh, we apply a
single-view comparison between the semantically annotated image and the current
3D mesh labels; this improves the robustness in case of noisy segmentations.
Exploring the structure of a real-time, arbitrary neural artistic stylization network
Authors: Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin, Jonathon Shlens
Date: 2017-05-18 00:00:00
Tasks: Style Transfer

In this paper, we present a method which combines the flexibility of the
neural algorithm of artistic style with the speed of fast style transfer
networks to allow real-time stylization using any content/style image pair. We
build upon recent work leveraging conditional instance normalization for
multi-style transfer networks by learning to predict the conditional instance
normalization parameters directly from a style image. The model is successfully
trained on a corpus of roughly 80,000 paintings and is able to generalize to
paintings previously unobserved. We demonstrate that the learned embedding
space is smooth and contains a rich structure and organizes semantic
information associated with paintings in an entirely unsupervised manner.
SPARCNN: SPAtially Related Convolutional Neural Networks
Authors: JT Turner, Kalyan Moy Gupta, David Aha
Date: 2017-08-24 00:00:00
Tasks: General Classification, object-detection, Object Detection, Region Proposal

The ability to accurately detect and classify objects at varying pixel sizes
in cluttered scenes is crucial to many Navy applications. However, detection
performance of existing state-of the-art approaches such as convolutional
neural networks (CNNs) degrade and suffer when applied to such cluttered and
multi-object detection tasks. We conjecture that spatial relationships between
objects in an image could be exploited to significantly improve detection
accuracy, an approach that had not yet been considered by any existing
techniques (to the best of our knowledge) at the time the research was
conducted. We introduce a detection and classification technique called
Spatially Related Detection with Convolutional Neural Networks (SPARCNN) that
learns and exploits a probabilistic representation of inter-object spatial
configurations within images from training sets for more effective region
proposals to use with state-of-the-art CNNs. Our empirical evaluation of
SPARCNN on the VOC 2007 dataset shows that it increases classification accuracy
by 8% when compared to a region proposal technique that does not exploit
spatial relations. More importantly, we obtained a higher performance boost of
18.8% when task difficulty in the test set is increased by including highly
obscured objects and increased image clutter.
Correlating Satellite Cloud Cover with Sky Cameras
Authors: Shilpa Manandhar, Soumyabrata Dev, Yee Hui Lee, Yu Song Meng
Date: 2017-08-24 00:00:00

The role of clouds is manifold in understanding the various events in the
atmosphere, and also in studying the radiative balance of the earth. The
conventional manner of such cloud analysis is performed mainly via satellite
images. However, because of its low temporal- and spatial- resolutions,
ground-based sky cameras are now getting popular. In this paper, we study the
relation between the cloud cover obtained from MODIS images, with the coverage
obtained from ground-based sky cameras. This will help us to better understand
cloud formation in the atmosphere - both from satellite images and ground-based
observations.
Study of Clear Sky Models for Singapore
Authors: Soumyabrata Dev, Shilpa Manandhar, Yee Hui Lee, Stefan Winkler
Date: 2017-08-24 00:00:00

The estimation of total solar irradiance falling on the earth's surface is
important in the field of solar energy generation and forecasting. Several
clear-sky solar radiation models have been developed over the last few decades.
Most of these models are based on empirical distribution of various
geographical parameters; while a few models consider various atmospheric
effects in the solar energy estimation. In this paper, we perform a comparative
analysis of several popular clear-sky models, in the tropical region of
Singapore. This is important in countries like Singapore, where we are
primarily focused on reliable and efficient solar energy generation. We analyze
and compare three popular clear-sky models that are widely used in the
literature. We validate our solar estimation results using actual solar
irradiance measurements obtained from collocated weather stations. We finally
conclude the most reliable clear sky model for Singapore, based on all clear
sky days in a year.
Analyzing Cloud Optical Properties Using Sky Cameras
Authors: Shilpa Manandhar, Soumyabrata Dev, Yee Hui Lee, Yu Song Meng
Date: 2017-08-24 00:00:00

Clouds play a significant role in the fluctuation of solar radiation received
by the earth's surface. It is important to study the various cloud properties,
as it impacts the total solar irradiance falling on the earth's surface. One of
such important optical properties of the cloud is the Cloud Optical Thickness
(COT). It is defined with the amount of light that can pass through the clouds.
The COT values are generally obtained from satellite images. However, satellite
images have a low temporal- and spatial- resolutions; and are not suitable for
study in applications as solar energy generation and forecasting. Therefore,
ground-based sky cameras are now getting popular in such fields. In this paper,
we analyze the cloud optical thickness value, from the ground-based sky
cameras, and provide future research directions.
Depth Super-Resolution Meets Uncalibrated Photometric Stereo
Authors: Songyou Peng, Bjoern Haefner, Yvain Quéau, Daniel Cremers
Date: 2017-08-01 00:00:00
Tasks: Super-Resolution

A novel depth super-resolution approach for RGB-D sensors is presented. It
disambiguates depth super-resolution through high-resolution photometric clues
and, symmetrically, it disambiguates uncalibrated photometric stereo through
low-resolution depth cues. To this end, an RGB-D sequence is acquired from the
same viewing angle, while illuminating the scene from various uncalibrated
directions. This sequence is handled by a variational framework which fits
high-resolution shape and reflectance, as well as lighting, to both the
low-resolution depth measurements and the high-resolution RGB ones. The key
novelty consists in a new PDE-based photometric stereo regularizer which
implicitly ensures surface regularity. This allows to carry out depth
super-resolution in a purely data-driven manner, without the need for any
ad-hoc prior or material calibration. Real-world experiments are carried out
using an out-of-the-box RGB-D sensor and a hand-held LED light source.
An Ensemble Classifier for Predicting the Onset of Type II Diabetes
Authors: John Semerdjian, Spencer Frank
Date: 2017-08-24 00:00:00
Tasks: General Classification, Nutrition, Survey, Vocal Bursts Type Prediction

Prediction of disease onset from patient survey and lifestyle data is quickly
becoming an important tool for diagnosing a disease before it progresses. In
this study, data from the National Health and Nutrition Examination Survey
(NHANES) questionnaire is used to predict the onset of type II diabetes. An
ensemble model using the output of five classification algorithms was developed
to predict the onset on diabetes based on 16 features. The ensemble model had
an AUC of 0.834 indicating high performance.
M2D: Monolog to Dialog Generation for Conversational Story Telling
Authors: Kevin K. Bowden, Grace I. Lin, Lena I. Reed, Marilyn A. Walker
Date: 2017-08-24 00:00:00

Storytelling serves many different social functions, e.g. stories are used to
persuade, share troubles, establish shared values, learn social behaviors, and
entertain. Moreover, stories are often told conversationally through dialog,
and previous work suggests that information provided dialogically is more
engaging than when provided in monolog. In this paper, we present algorithms
for converting a deep representation of a story into a dialogic storytelling,
that can vary aspects of the telling, including the personality of the
storytellers. We conduct several experiments to test whether dialogic
storytellings are more engaging, and whether automatically generated variants
in linguistic form that correspond to personality differences can be recognized
in an extended storytelling dialog.
Automatic Myocardial Segmentation by Using A Deep Learning Network in Cardiac MRI
Authors: Ariel H. Curiale, Flavio D. Colavecchia, Pablo Kaluza, Roberto A. Isoardi, German Mato
Date: 2017-08-24 00:00:00
Tasks: Diagnostic, Prognosis, Segmentation

Cardiac function is of paramount importance for both prognosis and treatment
of different pathologies such as mitral regurgitation, ischemia, dyssynchrony
and myocarditis. Cardiac behavior is determined by structural and functional
features. In both cases, the analysis of medical imaging studies requires to
detect and segment the myocardium. Nowadays, magnetic resonance imaging (MRI)
is one of the most relevant and accurate non-invasive diagnostic tools for
cardiac structure and function.
  In this work we propose to use a deep learning technique to assist the
automatization of myocardial segmentation in cardiac MRI. We present several
improvements to previous works in this paper: we propose to use the Jaccard
distance as optimization objective function, we integrate a residual learning
strategy into the code, and we introduce a batch normalization layer to train
the fully convolutional neural network. Our results demonstrate that this
architecture outperforms previous approaches based on a similar network
architecture, and that provides a suitable approach for myocardial
segmentation. Our benchmark shows that the automatic myocardial segmentation
takes less than 22 seg. for a volume of 128~x~128~x~13 pixels in a 3.1 GHz
intel core i7.
Bayesian Compressive Sensing Using Normal Product Priors
Authors: Zhou Zhou, Kaihui Liu, Jun Fang
Date: 2017-08-24 00:00:00
Tasks: compressed sensing, Compressive Sensing

In this paper, we introduce a new sparsity-promoting prior, namely, the
"normal product" prior, and develop an efficient algorithm for sparse signal
recovery under the Bayesian framework. The normal product distribution is the
distribution of a product of two normally distributed variables with zero means
and possibly different variances. Like other sparsity-encouraging distributions
such as the Student's $t$-distribution, the normal product distribution has a
sharp peak at origin, which makes it a suitable prior to encourage sparse
solutions. A two-stage normal product-based hierarchical model is proposed. We
resort to the variational Bayesian (VB) method to perform the inference.
Simulations are conducted to illustrate the effectiveness of our proposed
algorithm as compared with other state-of-the-art compressed sensing
algorithms.
Inverting Variational Autoencoders for Improved Generative Accuracy
Authors: Ian Gemp, Ishan Durugkar, Mario Parente, M. Darby Dyar, Sridhar Mahadevan
Date: 2016-08-21 00:00:00

Recent advances in semi-supervised learning with deep generative models have
shown promise in generalizing from small labeled datasets
($\mathbf{x},\mathbf{y}$) to large unlabeled ones ($\mathbf{x}$). In the case
where the codomain has known structure, a large unfeatured dataset
($\mathbf{y}$) is potentially available. We develop a parameter-efficient, deep
semi-supervised generative model for the purpose of exploiting this untapped
data source. Empirical results show improved performance in disentangling
latent variable semantics as well as improved discriminative prediction on
Martian spectroscopic and handwritten digit domains.
Variational autoencoders for tissue heterogeneity exploration from (almost) no preprocessed mass spectrometry imaging data
Authors: Paolo Inglese, James L. Alexander, Anna Mroz, Zoltan Takats, Robert Glen
Date: 2017-08-23 00:00:00
Tasks: Dimensionality Reduction

The paper presents the application of Variational Autoencoders (VAE) for data
dimensionality reduction and explorative analysis of mass spectrometry imaging
data (MSI). The results confirm that VAEs are capable of detecting the patterns
associated with the different tissue sub-types with performance than standard
approaches.
CloudScan - A configuration-free invoice analysis system using recurrent neural networks
Authors: Rasmus Berg Palm, Ole Winther, Florian Laws
Date: 2017-08-24 00:00:00

We present CloudScan; an invoice analysis system that requires zero
configuration or upfront annotation. In contrast to previous work, CloudScan
does not rely on templates of invoice layout, instead it learns a single global
model of invoices that naturally generalizes to unseen invoice layouts. The
model is trained using data automatically extracted from end-user provided
feedback. This automatic training data extraction removes the requirement for
users to annotate the data precisely. We describe a recurrent neural network
model that can capture long range context and compare it to a baseline logistic
regression model corresponding to the current CloudScan production system. We
train and evaluate the system on 8 important fields using a dataset of 326,471
invoices. The recurrent neural network and baseline model achieve 0.891 and
0.887 average F1 scores respectively on seen invoice layouts. For the harder
task of unseen invoice layouts, the recurrent neural network model outperforms
the baseline with 0.840 average F1 compared to 0.788.
Mixing time estimation in reversible Markov chains from a single sample path
Authors: Daniel Hsu, Aryeh Kontorovich, David A. Levin, Yuval Peres, Csaba Szepesvári
Date: 2017-08-24 00:00:00
Proceeding: NeurIPS 2015

The spectral gap $\gamma$ of a finite, ergodic, and reversible Markov chain
is an important parameter measuring the asymptotic rate of convergence. In
applications, the transition matrix $P$ may be unknown, yet one sample of the
chain up to a fixed time $n$ may be observed. We consider here the problem of
estimating $\gamma$ from this data. Let $\pi$ be the stationary distribution of
$P$, and $\pi_\star = \min_x \pi(x)$. We show that if $n =
\tilde{O}\bigl(\frac{1}{\gamma \pi_\star}\bigr)$, then $\gamma$ can be
estimated to within multiplicative constants with high probability. When $\pi$
is uniform on $d$ states, this matches (up to logarithmic correction) a lower
bound of $\tilde{\Omega}\bigl(\frac{d}{\gamma}\bigr)$ steps required for
precise estimation of $\gamma$. Moreover, we provide the first procedure for
computing a fully data-dependent interval, from a single finite-length
trajectory of the chain, that traps the mixing time $t_{\text{mix}}$ of the
chain at a prescribed confidence level. The interval does not require the
knowledge of any parameters of the chain. This stands in contrast to previous
approaches, which either only provide point estimates, or require a reset
mechanism, or additional prior knowledge. The interval is constructed around
the relaxation time $t_{\text{relax}} = 1/\gamma$, which is strongly related to
the mixing time, and the width of the interval converges to zero roughly at a
$1/\sqrt{n}$ rate, where $n$ is the length of the sample path.
Gait Recognition from Motion Capture Data
Authors: Michal Balazia, Petr Sojka
Date: 2017-08-24 00:00:00
Tasks: Gait Recognition, General Classification

Gait recognition from motion capture data, as a pattern classification discipline, can be improved by the use of machine learning. This paper contributes to the state-of-the-art with a statistical approach for extracting robust gait features directly from raw data by a modification of Linear Discriminant Analysis with Maximum Margin Criterion. Experiments on the CMU MoCap database show that the suggested method outperforms thirteen relevant methods based on geometric features and a method to learn the features by a combination of Principal Component Analysis and Linear Discriminant Analysis. The methods are evaluated in terms of the distribution of biometric templates in respective feature spaces expressed in a number of class separability coefficients and classification metrics. Results also indicate a high portability of learned features, that means, we can learn what aspects of walk people generally differ in and extract those as general gait features. Recognizing people without needing group-specific features is convenient as particular people might not always provide annotated learning data. As a contribution to reproducible research, our evaluation framework and database have been made publicly available. This research makes motion capture technology directly applicable for human recognition.
An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods
Authors: Michal Balazia, Petr Sojka
Date: 2017-01-04 00:00:00
Tasks: Gait Recognition

As a contribution to reproducible research, this paper presents a framework and a database to improve the development, evaluation and comparison of methods for gait recognition from motion capture (MoCap) data. The evaluation framework provides implementation details and source codes of state-of-the-art human-interpretable geometric features as well as our own approaches where gait features are learned by a modification of Fisher's Linear Discriminant Analysis with the Maximum Margin Criterion, and by a combination of Principal Component Analysis and Linear Discriminant Analysis. It includes a description and source codes of a mechanism for evaluating four class separability coefficients of feature space and four rank-based classifier performance metrics. This framework also contains a tool for learning a custom classifier and for classifying a custom query on a custom gallery. We provide an experimental database along with source codes for its extraction from the general CMU MoCap database.
Walker-Independent Features for Gait Recognition from Motion Capture Data
Authors: Michal Balazia, Petr Sojka
Date: 2016-09-22 00:00:00
Tasks: Gait Recognition

MoCap-based human identification, as a pattern recognition discipline, can be optimized using a machine learning approach. Yet in some applications such as video surveillance new identities can appear on the fly and labeled data for all encountered people may not always be available. This work introduces the concept of learning walker-independent gait features directly from raw joint coordinates by a modification of the Fisher Linear Discriminant Analysis with Maximum Margin Criterion. Our new approach shows not only that these features can discriminate different people than who they are learned on, but also that the number of learning identities can be much smaller than the number of walkers encountered in the real operation.
Learning Robust Features for Gait Recognition by Maximum Margin Criterion
Authors: Michal Balazia, Petr Sojka
Date: 2016-09-14 00:00:00
Tasks: Gait Recognition

In the field of gait recognition from motion capture data, designing human-interpretable gait features is a common practice of many fellow researchers. To refrain from ad-hoc schemes and to find maximally discriminative features we may need to explore beyond the limits of human interpretability. This paper contributes to the state-of-the-art with a machine learning approach for extracting robust gait features directly from raw joint coordinates. The features are learned by a modification of Linear Discriminant Analysis with Maximum Margin Criterion so that the identities are maximally separated and, in combination with an appropriate classifier, used for gait recognition. Experiments on the CMU MoCap database show that this method outperforms eight other relevant methods in terms of the distribution of biometric templates in respective feature spaces expressed in four class separability coefficients. Additional experiments indicate that this method is a leading concept for rank-based classifier systems.