pwc-archive/papers-with-abstracts huggingface dataset:
What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification
Authors: Lin Wu, Yang Wang, Xue Li, Junbin Gao
Date: 2017-07-21 00:00:00
Tasks: Person Re-Identification

Matching pedestrians across disjoint camera views, known as person
re-identification (re-id), is a challenging problem that is of importance to
visual recognition and surveillance. Most existing methods exploit local
regions within spatial manipulation to perform matching in local
correspondence. However, they essentially extract \emph{fixed} representations
from pre-divided regions for each image and perform matching based on the
extracted representation subsequently. For models in this pipeline, local finer
patterns that are crucial to distinguish positive pairs from negative ones
cannot be captured, and thus making them underperformed. In this paper, we
propose a novel deep multiplicative integration gating function, which answers
the question of \emph{what-and-where to match} for effective person re-id. To
address \emph{what} to match, our deep network emphasizes common local patterns
by learning joint representations in a multiplicative way. The network
comprises two Convolutional Neural Networks (CNNs) to extract convolutional
activations, and generates relevant descriptors for pedestrian matching. This
thus, leads to flexible representations for pair-wise images. To address
\emph{where} to match, we combat the spatial misalignment by performing
spatially recurrent pooling via a four-directional recurrent neural network to
impose spatial dependency over all positions with respect to the entire image.
The proposed network is designed to be end-to-end trainable to characterize
local pairwise feature interactions in a spatially aligned manner. To
demonstrate the superiority of our method, extensive experiments are conducted
over three benchmark data sets: VIPeR, CUHK03 and Market-1501.
An adaptive thresholding approach for automatic optic disk segmentation
Authors: Farnoosh Ghadiri, Robert Bergevin, Masoud Shafiee
Date: 2017-10-14 00:00:00
Tasks: Segmentation

Optic disk segmentation is a prerequisite step in automatic retinal screening
systems. In this paper, we propose an algorithm for optic disk segmentation
based on a local adaptive thresholding method. Location of the optic disk is
validated by intensity and average vessel width of retinal images. Then an
adaptive thresholding is applied on the temporal and nasal part of the optic
disc separately. Adaptive thresholding, makes our algorithm robust to
illumination variations and various image acquisition conditions. Moreover,
experimental results on the DRIVE and KHATAM databases show promising results
compared to the recent literature. In the DRIVE database, the optic disk in all
images is correctly located and the mean overlap reached to 43.21%. The optic
disk is correctly detected in 98% of the images with the mean overlap of 36.32%
in the KHATAM database.
Unsupervised Real-Time Control through Variational Empowerment
Authors: Maximilian Karl, Maximilian Soelch, Philip Becker-Ehmck, Djalel Benbouzid, Patrick van der Smagt, Justin Bayer
Date: 2017-10-13 00:00:00

We introduce a methodology for efficiently computing a lower bound to
empowerment, allowing it to be used as an unsupervised cost function for policy
learning in real-time control. Empowerment, being the channel capacity between
actions and states, maximises the influence of an agent on its near future. It
has been shown to be a good model of biological behaviour in the absence of an
extrinsic goal. But empowerment is also prohibitively hard to compute,
especially in nonlinear continuous spaces. We introduce an efficient, amortised
method for learning empowerment-maximising policies. We demonstrate that our
algorithm can reliably handle continuous dynamical systems using system
dynamics learned from raw data. The resulting policies consistently drive the
agents into states where they can use their full potential.
Patient-Driven Privacy Control through Generalized Distillation
Authors: Z. Berkay Celik, David Lopez-Paz, Patrick McDaniel
Date: 2016-11-26 00:00:00

The introduction of data analytics into medicine has changed the nature of
patient treatment. In this, patients are asked to disclose personal information
such as genetic markers, lifestyle habits, and clinical history. This data is
then used by statistical models to predict personalized treatments. However,
due to privacy concerns, patients often desire to withhold sensitive
information. This self-censorship can impede proper diagnosis and treatment,
which may lead to serious health complications and even death over time. In
this paper, we present privacy distillation, a mechanism which allows patients
to control the type and amount of information they wish to disclose to the
healthcare providers for use in statistical models. Meanwhile, it retains the
accuracy of models that have access to all patient data under a sufficient but
not full set of privacy-relevant information. We validate privacy distillation
using a corpus of patients prescribed to warfarin for a personalized dosage. We
use a deep neural network to implement privacy distillation for training and
making dose predictions. We find that privacy distillation with sufficient
privacy-relevant information i) retains accuracy almost as good as having all
patient data (only 3\% worse), and ii) is effective at preventing errors that
introduce health-related risks (only 3.9\% worse under- or over-prescriptions).
On the Ontological Modeling of Trees
Authors: David Carral, Pascal Hitzler, Hilmar Lapp, Sebastian Rudolph
Date: 2017-10-13 00:00:00

Trees -- i.e., the type of data structure known under this name -- are
central to many aspects of knowledge organization. We investigate some central
design choices concerning the ontological modeling of such trees. In
particular, we consider the limits of what is expressible in the Web Ontology
Language, and provide a reusable ontology design pattern for trees.
Learning Phrase Embeddings from Paraphrases with GRUs
Authors: Zhihao Zhou, Lifu Huang, Heng Ji
Date: 2017-10-13 00:00:00
Proceeding: WS 2017 11
Tasks: Machine Translation, Sentiment Analysis, Translation

Learning phrase representations has been widely explored in many Natural
Language Processing (NLP) tasks (e.g., Sentiment Analysis, Machine Translation)
and has shown promising improvements. Previous studies either learn
non-compositional phrase representations with general word embedding learning
techniques or learn compositional phrase representations based on syntactic
structures, which either require huge amounts of human annotations or cannot be
easily generalized to all phrases. In this work, we propose to take advantage
of large-scaled paraphrase database and present a pair-wise gated recurrent
units (pairwise-GRU) framework to generate compositional phrase
representations. Our framework can be re-used to generate representations for
any phrases. Experimental results show that our framework achieves
state-of-the-art results on several phrase similarity tasks.
Dropout as a Low-Rank Regularizer for Matrix Factorization
Authors: Jacopo Cavazza, Pietro Morerio, Benjamin Haeffele, Connor Lane, Vittorio Murino, Rene Vidal
Date: 2017-10-13 00:00:00

Regularization for matrix factorization (MF) and approximation problems has
been carried out in many different ways. Due to its popularity in deep
learning, dropout has been applied also for this class of problems. Despite its
solid empirical performance, the theoretical properties of dropout as a
regularizer remain quite elusive for this class of problems. In this paper, we
present a theoretical analysis of dropout for MF, where Bernoulli random
variables are used to drop columns of the factors. We demonstrate the
equivalence between dropout and a fully deterministic model for MF in which the
factors are regularized by the sum of the product of squared Euclidean norms of
the columns. Additionally, we inspect the case of a variable sized
factorization and we prove that dropout achieves the global minimum of a convex
approximation problem with (squared) nuclear norm regularization. As a result,
we conclude that dropout can be used as a low-rank regularizer with data
dependent singular-value thresholding.
A simple data discretizer
Authors: Gourab Mitra, Shashidhar Sundareisan, Bikash Kanti Sarkar
Date: 2017-10-13 00:00:00

Data discretization is an important step in the process of machine learning,
since it is easier for classifiers to deal with discrete attributes rather than
continuous attributes. Over the years, several methods of performing
discretization such as Boolean Reasoning, Equal Frequency Binning, Entropy have
been proposed, explored, and implemented. In this article, a simple supervised
discretization approach is introduced. The prime goal of MIL is to maximize
classification accuracy of classifier, minimizing loss of information while
discretization of continuous attributes. The performance of the suggested
approach is compared with the supervised discretization algorithm Minimum
Information Loss (MIL), using the state-of-the-art rule inductive algorithms-
J48 (Java implementation of C4.5 classifier). The presented approach is,
indeed, the modified version of MIL. The empirical results show that the
modified approach performs better in several cases in comparison to the
original MIL algorithm and Minimum Description Length Principle (MDLP) .
Burn-In Demonstrations for Multi-Modal Imitation Learning
Authors: Alex Kuefler, Mykel J. Kochenderfer
Date: 2017-10-13 00:00:00
Tasks: Autonomous Driving, Imitation Learning

Recent work on imitation learning has generated policies that reproduce
expert behavior from multi-modal data. However, past approaches have focused
only on recreating a small number of distinct, expert maneuvers, or have relied
on supervised learning techniques that produce unstable policies. This work
extends InfoGAIL, an algorithm for multi-modal imitation learning, to reproduce
behavior over an extended period of time. Our approach involves reformulating
the typical imitation learning setting to include "burn-in demonstrations" upon
which policies are conditioned at test time. We demonstrate that our approach
outperforms standard InfoGAIL in maximizing the mutual information between
predicted and unseen style labels in road scene simulations, and we show that
our method leads to policies that imitate expert autonomous driving systems
over long time horizons.
Cold-Start Reinforcement Learning with Softmax Policy Gradient
Authors: Nan Ding, Radu Soricut
Date: 2017-09-27 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Image Captioning, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Policy-gradient approaches to reinforcement learning have two common and
undesirable overhead procedures, namely warm-start training and sample variance
reduction. In this paper, we describe a reinforcement learning method based on
a softmax value function that requires neither of these procedures. Our method
combines the advantages of policy-gradient methods with the efficiency and
simplicity of maximum-likelihood approaches. We apply this new cold-start
reinforcement learning method in training sequence generation models for
structured output prediction problems. Empirical evidence validates this method
on automatic summarization and image captioning tasks.
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization
Authors: Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen
Date: 2017-10-13 00:00:00
Tasks: Distributed Computing, Distributed Optimization

Machine learning with big data often involves large optimization models. For
distributed optimization over a cluster of machines, frequent communication and
synchronization of all model parameters (optimization variables) can be very
costly. A promising solution is to use parameter servers to store different
subsets of the model parameters, and update them asynchronously at different
machines using local datasets. In this paper, we focus on distributed
optimization of large linear models with convex loss functions, and propose a
family of randomized primal-dual block coordinate algorithms that are
especially suitable for asynchronous distributed implementation with parameter
servers. In particular, we work with the saddle-point formulation of such
problems which allows simultaneous data and model partitioning, and exploit its
structure by doubly stochastic coordinate optimization with variance reduction
(DSCOVR). Compared with other first-order distributed algorithms, we show that
DSCOVR may require less amount of overall computation and communication, and
less or no synchronization. We discuss the implementation details of the DSCOVR
algorithms, and present numerical experiments on an industrial distributed
computing system.
Improving Shadow Suppression for Illumination Robust Face Recognition
Authors: Wuming Zhang, Xi Zhao, Jean-Marie Morvan, Liming Chen
Date: 2017-10-13 00:00:00
Tasks: Face Recognition, Face Verification, Robust Face Recognition

2D face analysis techniques, such as face landmarking, face recognition and
face verification, are reasonably dependent on illumination conditions which
are usually uncontrolled and unpredictable in the real world. An illumination
robust preprocessing method thus remains a significant challenge in reliable
face analysis. In this paper we propose a novel approach for improving lighting
normalization through building the underlying reflectance model which
characterizes interactions between skin surface, lighting source and camera
sensor, and elaborates the formation of face color appearance. Specifically,
the proposed illumination processing pipeline enables the generation of
Chromaticity Intrinsic Image (CII) in a log chromaticity space which is robust
to illumination variations. Moreover, as an advantage over most prevailing
methods, a photo-realistic color face image is subsequently reconstructed which
eliminates a wide variety of shadows whilst retaining the color information and
identity details. Experimental results under different scenarios and using
various face databases show the effectiveness of the proposed approach to deal
with lighting variations, including both soft and hard shadows, in face
recognition.
Automated Scalable Bayesian Inference via Hilbert Coresets
Authors: Trevor Campbell, Tamara Broderick
Date: 2017-10-13 00:00:00
Tasks: Bayesian Inference

The automation of posterior inference in Bayesian data analysis has enabled
experts and nonexperts alike to use more sophisticated models, engage in faster
exploratory modeling and analysis, and ensure experimental reproducibility.
However, standard automated posterior inference algorithms are not tractable at
the scale of massive modern datasets, and modifications to make them so are
typically model-specific, require expert tuning, and can break theoretical
guarantees on inferential quality. Building on the Bayesian coresets framework,
this work instead takes advantage of data redundancy to shrink the dataset
itself as a preprocessing step, providing fully-automated, scalable Bayesian
inference with theoretical guarantees. We begin with an intuitive reformulation
of Bayesian coreset construction as sparse vector sum approximation, and
demonstrate that its automation and performance-based shortcomings arise from
the use of the supremum norm. To address these shortcomings we develop Hilbert
coresets, i.e., Bayesian coresets constructed under a norm induced by an
inner-product on the log-likelihood function space. We propose two Hilbert
coreset construction algorithms---one based on importance sampling, and one
based on the Frank-Wolfe algorithm---along with theoretical guarantees on
approximation quality as a function of coreset size. Since the exact
computation of the proposed inner-products is model-specific, we automate the
construction with a random finite-dimensional projection of the log-likelihood
functions. The resulting automated coreset construction algorithm is simple to
implement, and experiments on a variety of models with real and synthetic
datasets show that it provides high-quality posterior approximations and a
significant reduction in the computational cost of inference.
Learning Independent Features with Adversarial Nets for Non-linear ICA
Authors: Philemon Brakel, Yoshua Bengio
Date: 2017-10-13 00:00:00
Proceeding: ICLR 2018 1

Reliable measures of statistical dependence could be useful tools for
learning independent features and performing tasks like source separation using
Independent Component Analysis (ICA). Unfortunately, many of such measures,
like the mutual information, are hard to estimate and optimize directly. We
propose to learn independent features with adversarial objectives which
optimize such measures implicitly. These objectives compare samples from the
joint distribution and the product of the marginals without the need to compute
any probability densities. We also propose two methods for obtaining samples
from the product of the marginals using either a simple resampling trick or a
separate parametric distribution. Our experiments show that this strategy can
easily be applied to different types of model architectures and solve both
linear and non-linear ICA problems.
Enabling Sparse Winograd Convolution by Native Pruning
Authors: Sheng Li, Jongsoo Park, Ping Tak Peter Tang
Date: 2017-02-28 00:00:00
Tasks: CPU

Sparse methods and the use of Winograd convolutions are two orthogonal
approaches, each of which significantly accelerates convolution computations in
modern CNNs. Sparse Winograd merges these two and thus has the potential to
offer a combined performance benefit. Nevertheless, training convolution layers
so that the resulting Winograd kernels are sparse has not hitherto been very
successful. By introducing a Winograd layer in place of a standard convolution
layer, we can learn and prune Winograd coefficients "natively" and obtain
sparsity level beyond 90% with only 0.1% accuracy loss with AlexNet on ImageNet
dataset. Furthermore, we present a sparse Winograd convolution algorithm and
implementation that exploits the sparsity, achieving up to 31.7 effective
TFLOP/s in 32-bit precision on a latest Intel Xeon CPU, which corresponds to a
5.4x speedup over a state-of-the-art dense convolution implementation.
The Stochastic Replica Approach to Machine Learning: Stability and Parameter Optimization
Authors: Patrick Chao, Tahereh Mazaheri, Bo Sun, Nicholas B. Weingartner, Zohar Nussinov
Date: 2017-08-18 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, regression

We introduce a statistical physics inspired supervised machine learning
algorithm for classification and regression problems. The method is based on
the invariances or stability of predicted results when known data is
represented as expansions in terms of various stochastic functions. The
algorithm predicts the classification/regression values of new data by
combining (via voting) the outputs of these numerous linear expansions in
randomly chosen functions. The few parameters (typically only one parameter is
used in all studied examples) that this model has may be automatically
optimized. The algorithm has been tested on 10 diverse training data sets of
various types and feature space dimensions. It has been shown to consistently
exhibit high accuracy and readily allow for optimization of parameters, while
simultaneously avoiding pitfalls of existing algorithms such as those
associated with class imbalance. We very briefly speculate on whether spatial
coordinates in physical theories may be viewed as emergent "features" that
enable a robust machine learning type description of data with generic low
order smooth functions.
Potential Conditional Mutual Information: Estimators, Properties and Applications
Authors: Arman Rahimzamani, Sreeram Kannan
Date: 2017-10-13 00:00:00
Tasks: Time Series, Time Series Analysis

The conditional mutual information I(X;Y|Z) measures the average information
that X and Y contain about each other given Z. This is an important primitive
in many learning problems including conditional independence testing, graphical
model inference, causal strength estimation and time-series problems. In
several applications, it is desirable to have a functional purely of the
conditional distribution p_{Y|X,Z} rather than of the joint distribution
p_{X,Y,Z}. We define the potential conditional mutual information as the
conditional mutual information calculated with a modified joint distribution
p_{Y|X,Z} q_{X,Z}, where q_{X,Z} is a potential distribution, fixed airport. We
develop K nearest neighbor based estimators for this functional, employing
importance sampling, and a coupling trick, and prove the finite k consistency
of such an estimator. We demonstrate that the estimator has excellent practical
performance and show an application in dynamical system inference.
Deep learning for source camera identification on mobile devices
Authors: David Freire-Obregón, Fabio Narducci, Silvio Barra, Modesto Castrillón-Santana
Date: 2017-09-30 00:00:00
Tasks: Deep Learning

In the present paper, we propose a source camera identification method for
mobile devices based on deep learning. Recently, convolutional neural networks
(CNNs) have shown a remarkable performance on several tasks such as image
recognition, video analysis or natural language processing. A CNN consists on a
set of layers where each layer is composed by a set of high pass filters which
are applied all over the input image. This convolution process provides the
unique ability to extract features automatically from data and to learn from
those features. Our proposal describes a CNN architecture which is able to
infer the noise pattern of mobile camera sensors (also known as camera
fingerprint) with the aim at detecting and identifying not only the mobile
device used to capture an image (with a 98\% of accuracy), but also from which
embedded camera the image was captured. More specifically, we provide an
extensive analysis on the proposed architecture considering different
configurations. The experiment has been carried out using the images captured
from different mobile devices cameras (MICHE-I Dataset was used) and the
obtained results have proved the robustness of the proposed method.
Complex Word Identification: Challenges in Data Annotation and System Performance
Authors: Marcos Zampieri, Shervin Malmasi, Gustavo Paetzold, Lucia Specia
Date: 2017-10-13 00:00:00
Proceeding: WS 2017 12
Tasks: Complex Word Identification, General Classification

This paper revisits the problem of complex word identification (CWI)
following up the SemEval CWI shared task. We use ensemble classifiers to
investigate how well computational methods can discriminate between complex and
non-complex words. Furthermore, we analyze the classification performance to
understand what makes lexical complexity challenging. Our findings show that
most systems performed poorly on the SemEval CWI dataset, and one of the
reasons for that is the way in which human annotation was performed.