pwc-archive/papers-with-abstracts huggingface dataset:
Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series
Authors: Ruben Loaiza-Maya, Michael Stanley Smith
Date: 2017-12-26 00:00:00
Tasks: Time Series, Time Series Analysis

We propose a new variational Bayes estimator for high-dimensional copulas
with discrete, or a combination of discrete and continuous, margins. The method
is based on a variational approximation to a tractable augmented posterior, and
is faster than previous likelihood-based approaches. We use it to estimate
drawable vine copulas for univariate and multivariate Markov ordinal and mixed
time series. These have dimension $rT$, where $T$ is the number of observations
and $r$ is the number of series, and are difficult to estimate using previous
methods. The vine pair-copulas are carefully selected to allow for
heteroskedasticity, which is a feature of most ordinal time series data. When
combined with flexible margins, the resulting time series models also allow for
other common features of ordinal data, such as zero inflation, multiple modes
and under- or over-dispersion. Using six example series, we illustrate both the
flexibility of the time series copula models, and the efficacy of the
variational Bayes estimator for copulas of up to 792 dimensions and 60
parameters. This far exceeds the size and complexity of copula models for
discrete data that can be estimated using previous methods.
Improved Training of Wasserstein GANs
Authors: Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville
Date: 2017-03-31 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Conditional Image Generation, Image Generation, Synthetic Data Generation

Generative Adversarial Networks (GANs) are powerful generative models, but
suffer from training instability. The recently proposed Wasserstein GAN (WGAN)
makes progress toward stable training of GANs, but sometimes can still generate
only low-quality samples or fail to converge. We find that these problems are
often due to the use of weight clipping in WGAN to enforce a Lipschitz
constraint on the critic, which can lead to undesired behavior. We propose an
alternative to clipping weights: penalize the norm of gradient of the critic
with respect to its input. Our proposed method performs better than standard
WGAN and enables stable training of a wide variety of GAN architectures with
almost no hyperparameter tuning, including 101-layer ResNets and language
models over discrete data. We also achieve high quality generations on CIFAR-10
and LSUN bedrooms.
A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression
Authors: Luis M. Briceno-Arias, Giovanni Chierchia, Emilie Chouzenoux, Jean-Christophe Pesquet
Date: 2017-12-25 00:00:00
Tasks: regression

In this paper, we propose a new optimization algorithm for sparse logistic
regression based on a stochastic version of the Douglas-Rachford splitting
method. Our algorithm sweeps the training set by randomly selecting a
mini-batch of data at each iteration, and it allows us to update the variables
in a block coordinate manner. Our approach leverages the proximity operator of
the logistic loss, which is expressed with the generalized Lambert W function.
Experiments carried out on standard datasets demonstrate the efficiency of our
approach w.r.t. stochastic gradient-like methods.
Generative Adversarial Nets for Multiple Text Corpora
Authors: Baiyang Wang, Diego Klabjan
Date: 2017-12-25 00:00:00
Tasks: Cross-corpus, Word Embeddings

Generative adversarial nets (GANs) have been successfully applied to the
artificial generation of image data. In terms of text data, much has been done
on the artificial generation of natural language from a single corpus. We
consider multiple text corpora as the input data, for which there can be two
applications of GANs: (1) the creation of consistent cross-corpus word
embeddings given different word embeddings per corpus; (2) the generation of
robust bag-of-words document embeddings for each corpora. We demonstrate our
GAN models on real-world text data sets from different corpora, and show that
embeddings from both models lead to improvements in supervised learning
problems.
SAGA: A Submodular Greedy Algorithm For Group Recommendation
Authors: Shameem A Puthiya Parambath, Nishant Vijayakumar, Sanjay Chawla
Date: 2017-12-25 00:00:00

In this paper, we propose a unified framework and an algorithm for the
problem of group recommendation where a fixed number of items or alternatives
can be recommended to a group of users. The problem of group recommendation
arises naturally in many real world contexts, and is closely related to the
budgeted social choice problem studied in economics. We frame the group
recommendation problem as choosing a subgraph with the largest group consensus
score in a completely connected graph defined over the item affinity matrix. We
propose a fast greedy algorithm with strong theoretical guarantees, and show
that the proposed algorithm compares favorably to the state-of-the-art group
recommendation algorithms according to commonly used relevance and coverage
performance measures on benchmark dataset.
Overcomplete Frame Thresholding for Acoustic Scene Analysis
Authors: Romain Cosentino, Randall Balestriero, Richard Baraniuk, Ankit Patel
Date: 2017-12-25 00:00:00
Tasks: Action Detection, Activity Detection, Anomaly Detection, General Classification, regression

In this work, we derive a generic overcomplete frame thresholding scheme
based on risk minimization. Overcomplete frames being favored for analysis
tasks such as classification, regression or anomaly detection, we provide a way
to leverage those optimal representations in real-world applications through
the use of thresholding. We validate the method on a large scale bird activity
detection task via the scattering network architecture performed by means of
continuous wavelets, known for being an adequate dictionary in audio
environments.
On Connecting Stochastic Gradient MCMC and Differential Privacy
Authors: Bai Li, Changyou Chen, Hao liu, Lawrence Carin
Date: 2017-12-25 00:00:00
Tasks: BIG-bench Machine Learning

Significant success has been realized recently on applying machine learning
to real-world applications. There have also been corresponding concerns on the
privacy of training data, which relates to data security and confidentiality
issues. Differential privacy provides a principled and rigorous privacy
guarantee on machine learning models. While it is common to design a model
satisfying a required differential-privacy property by injecting noise, it is
generally hard to balance the trade-off between privacy and utility. We show
that stochastic gradient Markov chain Monte Carlo (SG-MCMC) -- a class of
scalable Bayesian posterior sampling algorithms proposed recently -- satisfies
strong differential privacy with carefully chosen step sizes. We develop theory
on the performance of the proposed differentially-private SG-MCMC method. We
conduct experiments to support our analysis and show that a standard SG-MCMC
sampler without any modification (under a default setting) can reach
state-of-the-art performance in terms of both privacy and utility on Bayesian
learning.
Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methods
Authors: Xin-Yao Qian
Date: 2017-06-03 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

Precise financial series predicting has long been a difficult problem because
of unstableness and many noises within the series. Although Traditional time
series models like ARIMA and GARCH have been researched and proved to be
effective in predicting, their performances are still far from satisfying.
Machine Learning, as an emerging research field in recent years, has brought
about many incredible improvements in tasks such as regressing and classifying,
and it's also promising to exploit the methodology in financial time series
predicting. In this paper, the predicting precision of financial time series
between traditional time series models and mainstream machine learning models
including some state-of-the-art ones of deep learning are compared through
experiment using real stock index data from history. The result shows that
machine learning as a modern method far surpasses traditional models in
precision.
Deep Blind Image Inpainting
Authors: Yang Liu, Jinshan Pan, Zhixun Su
Date: 2017-12-25 00:00:00
Tasks: Decoder, Image Inpainting, Image Restoration

Image inpainting is a challenging problem as it needs to fill the information
of the corrupted regions. Most of the existing inpainting algorithms assume
that the positions of the corrupted regions are known. Different from the
existing methods that usually make some assumptions on the corrupted regions,
we present an efficient blind image inpainting algorithm to directly restore a
clear image from a corrupted input. Our algorithm is motivated by the residual
learning algorithm which aims to learn the missing infor- mation in corrupted
regions. However, directly using exist- ing residual learning algorithms in
image restoration does not well solve this problem as little information is
available in the corrupted regions. To solve this problem, we introduce an
encoder and decoder architecture to capture more useful information and develop
a robust loss function to deal with outliers. Our algorithm can predict the
missing information in the corrupted regions, thus facilitating the clear image
restoration. Both qualitative and quantitative experimental demonstrate that
our algorithm can deal with the corrupted regions of arbitrary shapes and
performs favorably against state-of-the-art methods.
STDP-based spiking deep convolutional neural networks for object recognition
Authors: Saeed Reza Kheradpisheh, Mohammad Ganjtabesh, Simon J. Thorpe, Timothée Masquelier
Date: 2016-11-04 00:00:00
Tasks: Object Recognition

Previous studies have shown that spike-timing-dependent plasticity (STDP) can
be used in spiking neural networks (SNN) to extract visual features of low or
intermediate complexity in an unsupervised manner. These studies, however, used
relatively shallow architectures, and only one layer was trainable. Another
line of research has demonstrated - using rate-based neural networks trained
with back-propagation - that having many layers increases the recognition
robustness, an approach known as deep learning. We thus designed a deep SNN,
comprising several convolutional (trainable with STDP) and pooling layers. We
used a temporal coding scheme where the most strongly activated neurons fire
first, and less activated neurons fire later or not at all. The network was
exposed to natural images. Thanks to STDP, neurons progressively learned
features corresponding to prototypical patterns that were both salient and
frequent. Only a few tens of examples per category were required and no label
was needed. After learning, the complexity of the extracted features increased
along the hierarchy, from edge detectors in the first layer to object
prototypes in the last layer. Coding was very sparse, with only a few thousands
spikes per image, and in some cases the object category could be reasonably
well inferred from the activity of a single higher-order neuron. More
generally, the activity of a few hundreds of such neurons contained robust
category information, as demonstrated using a classifier on Caltech 101,
ETH-80, and MNIST databases. We also demonstrate the superiority of STDP over
other unsupervised techniques such as random crops (HMAX) or auto-encoders.
Taken together, our results suggest that the combination of STDP with latency
coding may be a key to understanding the way that the primate visual system
learns, its remarkable processing speed and its low energy consumption.
A total uncertainty measure for D numbers based on belief intervals
Authors: Xinyang Deng, Wen Jiang
Date: 2017-12-25 00:00:00
Tasks: Specificity

As a generalization of Dempster-Shafer theory, the theory of D numbers is a
new theoretical framework for uncertainty reasoning. Measuring the uncertainty
of knowledge or information represented by D numbers is an unsolved issue in
that theory. In this paper, inspired by distance based uncertainty measures for
Dempster-Shafer theory, a total uncertainty measure for a D number is proposed
based on its belief intervals. The proposed total uncertainty measure can
simultaneously capture the discord, and non-specificity, and non-exclusiveness
involved in D numbers. And some basic properties of this total uncertainty
measure, including range, monotonicity, generalized set consistency, are also
presented.
Deep learning from crowds
Authors: Filipe Rodrigues, Francisco Pereira
Date: 2017-09-06 00:00:00
Tasks: Deep Learning

Over the last few years, deep learning has revolutionized the field of
machine learning by dramatically improving the state-of-the-art in various
domains. However, as the size of supervised artificial neural networks grows,
typically so does the need for larger labeled datasets. Recently, crowdsourcing
has established itself as an efficient and cost-effective solution for labeling
large sets of data in a scalable manner, but it often requires aggregating
labels from multiple noisy contributors with different levels of expertise. In
this paper, we address the problem of learning deep neural networks from
crowds. We begin by describing an EM algorithm for jointly learning the
parameters of the network and the reliabilities of the annotators. Then, a
novel general-purpose crowd layer is proposed, which allows us to train deep
neural networks end-to-end, directly from the noisy labels of multiple
annotators, using only backpropagation. We empirically show that the proposed
approach is able to internally capture the reliability and biases of different
annotators and achieve new state-of-the-art results for various crowdsourced
datasets across different settings, namely classification, regression and
sequence labeling.
Translation of "Zur Ermittlung eines Objektes aus zwei Perspektiven mit innerer Orientierung" by Erwin Kruppa (1913)
Authors: Guillermo Gallego, Elias Mueggler, Peter Sturm
Date: 2017-12-25 00:00:00
Tasks: Pose Estimation, Translation

Erwin Kruppa's 1913 paper, Erwin Kruppa, "Zur Ermittlung eines Objektes aus
zwei Perspektiven mit innerer Orientierung", Sitzungsberichte der
Mathematisch-Naturwissenschaftlichen Kaiserlichen Akademie der Wissenschaften,
Vol. 122 (1913), pp. 1939-1948, which may be translated as "To determine a 3D
object from two perspective views with known inner orientation", is a landmark
paper in Computer Vision because it provides the first five-point algorithm for
relative pose estimation. Kruppa showed that (a finite number of solutions for)
the relative pose between two calibrated images of a rigid object can be
computed from five point matches between the images. Kruppa's work also gained
attention in the topic of camera self-calibration, as presented in (Maybank and
Faugeras, 1992). Since the paper is still relevant today (more than a hundred
citations within the last ten years) and the paper is not available online, we
ordered a copy from the German National Library in Frankfurt and provide an
English translation along with the German original. We also adapt the
terminology to a modern jargon and provide some clarifications (highlighted in
sans-serif font). For a historical review of geometric computer vision, the
reader is referred to the recent survey paper (Sturm, 2011).
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
Authors: Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, Matthew P. Lungren, Andrew Y. Ng
Date: 2017-11-14 00:00:00
Tasks: Pneumonia Detection

We develop an algorithm that can detect pneumonia from chest X-rays at a
level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer
convolutional neural network trained on ChestX-ray14, currently the largest
publicly available chest X-ray dataset, containing over 100,000 frontal-view
X-ray images with 14 diseases. Four practicing academic radiologists annotate a
test set, on which we compare the performance of CheXNet to that of
radiologists. We find that CheXNet exceeds average radiologist performance on
the F1 metric. We extend CheXNet to detect all 14 diseases in ChestX-ray14 and
achieve state of the art results on all 14 diseases.
The Power of Arc Consistency for CSPs Defined by Partially-Ordered Forbidden Patterns
Authors: Martin C. Cooper, Stanislav Živný
Date: 2016-04-27 00:00:00
Tasks: ARC

Characterising tractable fragments of the constraint satisfaction problem
(CSP) is an important challenge in theoretical computer science and artificial
intelligence. Forbidding patterns (generic sub-instances) provides a means of
defining CSP fragments which are neither exclusively language-based nor
exclusively structure-based. It is known that the class of binary CSP instances
in which the broken-triangle pattern (BTP) does not occur, a class which
includes all tree-structured instances, are decided by arc consistency (AC), a
ubiquitous reduction operation in constraint solvers. We provide a
characterisation of simple partially-ordered forbidden patterns which have this
AC-solvability property. It turns out that BTP is just one of five such
AC-solvable patterns. The four other patterns allow us to exhibit new tractable
classes.
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
Authors: Wentao Zhu, Xiang Xiang, Trac. D. Tran, Gregory D. Hager, Xiaohui Xie
Date: 2017-10-24 00:00:00
Tasks: Mass Segmentation From Mammograms, Position, Segmentation

Mass segmentation provides effective morphological features which are
important for mass diagnosis. In this work, we propose a novel end-to-end
network for mammographic mass segmentation which employs a fully convolutional
network (FCN) to model a potential function, followed by a CRF to perform
structured learning. Because the mass distribution varies greatly with pixel
position, the FCN is combined with a position priori. Further, we employ
adversarial training to eliminate over-fitting due to the small sizes of
mammogram datasets. Multi-scale FCN is employed to improve the segmentation
performance. Experimental results on two public datasets, INbreast and
DDSM-BCRP, demonstrate that our end-to-end network achieves better performance
than state-of-the-art approaches.
\footnote{https://github.com/wentaozhu/adversarial-deep-structural-networks.git}
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
Authors: Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, Dimitris Metaxas
Date: 2017-10-19 00:00:00
Tasks: Generative Adversarial Network, Image Generation, Text-to-Image Generation

Although Generative Adversarial Networks (GANs) have shown remarkable success
in various tasks, they still face challenges in generating high quality images.
In this paper, we propose Stacked Generative Adversarial Networks (StackGAN)
aiming at generating high-resolution photo-realistic images. First, we propose
a two-stage generative adversarial network architecture, StackGAN-v1, for
text-to-image synthesis. The Stage-I GAN sketches the primitive shape and
colors of the object based on given text description, yielding low-resolution
images. The Stage-II GAN takes Stage-I results and text descriptions as inputs,
and generates high-resolution images with photo-realistic details. Second, an
advanced multi-stage generative adversarial network architecture, StackGAN-v2,
is proposed for both conditional and unconditional generative tasks. Our
StackGAN-v2 consists of multiple generators and discriminators in a tree-like
structure; images at multiple scales corresponding to the same scene are
generated from different branches of the tree. StackGAN-v2 shows more stable
training behavior than StackGAN-v1 by jointly approximating multiple
distributions. Extensive experiments demonstrate that the proposed stacked
generative adversarial networks significantly outperform other state-of-the-art
methods in generating photo-realistic images.
Robust Frequent Directions with Application in Online Learning
Authors: Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang
Date: 2017-05-15 00:00:00

The frequent directions (FD) technique is a deterministic approach for online
sketching that has many applications in machine learning. The conventional FD
is a heuristic procedure that often outputs rank deficient matrices. To
overcome the rank deficiency problem, we propose a new sketching strategy
called robust frequent directions (RFD) by introducing a regularization term.
RFD can be derived from an optimization problem. It updates the sketch matrix
and the regularization term adaptively and jointly. RFD reduces the
approximation error of FD without increasing the computational cost. We also
apply RFD to online learning and propose an effective hyperparameter-free
online Newton algorithm. We derive a regret bound for our online Newton
algorithm based on RFD, which guarantees the robustness of the algorithm. The
experimental studies demonstrate that the proposed method outperforms
state-of-the-art second order online learning algorithms.
Stratified Transfer Learning for Cross-domain Activity Recognition
Authors: Jindong Wang, Yiqiang Chen, Lisha Hu, Xiaohui Peng, Philip S. Yu
Date: 2017-12-25 00:00:00
Tasks: Activity Recognition, Cross-Domain Activity Recognition, General Classification, Transfer Learning

In activity recognition, it is often expensive and time-consuming to acquire
sufficient activity labels. To solve this problem, transfer learning leverages
the labeled samples from the source domain to annotate the target domain which
has few or none labels. Existing approaches typically consider learning a
global domain shift while ignoring the intra-affinity between classes, which
will hinder the performance of the algorithms. In this paper, we propose a
novel and general cross-domain learning framework that can exploit the
intra-affinity of classes to perform intra-class knowledge transfer. The
proposed framework, referred to as Stratified Transfer Learning (STL), can
dramatically improve the classification accuracy for cross-domain activity
recognition. Specifically, STL first obtains pseudo labels for the target
domain via majority voting technique. Then, it performs intra-class knowledge
transfer iteratively to transform both domains into the same subspaces.
Finally, the labels of target domain are obtained via the second annotation. To
evaluate the performance of STL, we conduct comprehensive experiments on three
large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI
DSADS), which demonstrates that STL significantly outperforms other
state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%).
Furthermore, we extensively investigate the performance of STL across different
degrees of similarities and activity levels between domains. And we also
discuss the potential of STL in other pervasive computing applications to
provide empirical experience for future research.
CycleGAN Face-off
Authors: Xiaohan Jin, Ye Qi, Shangxuan Wu
Date: 2017-12-09 00:00:00
Tasks: Style Transfer

Face-off is an interesting case of style transfer where the facial
expressions and attributes of one person could be fully transformed to another
face. We are interested in the unsupervised training process which only
requires two sequences of unaligned video frames from each person and learns
what shared attributes to extract automatically. In this project, we explored
various improvements for adversarial training (i.e. CycleGAN[Zhu et al., 2017])
to capture details in facial expressions and head poses and thus generate
transformation videos of higher consistency and stability.