pwc-archive/papers-with-abstracts huggingface dataset:
UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition
Authors: Jiankang Deng, Shiyang Cheng, Niannan Xue, Yuxiang Zhou, Stefanos Zafeiriou
Date: 2017-12-13 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Face Alignment, Face Alignment, Face Model, Face Recognition, Robust Face Recognition, Texture Classification

Recently proposed robust 3D face alignment methods establish either dense or
sparse correspondence between a 3D face model and a 2D facial image. The use of
these methods presents new challenges as well as opportunities for facial
texture analysis. In particular, by sampling the image using the fitted model,
a facial UV can be created. Unfortunately, due to self-occlusion, such a UV map
is always incomplete. In this paper, we propose a framework for training Deep
Convolutional Neural Network (DCNN) to complete the facial UV map extracted
from in-the-wild images. To this end, we first gather complete UV maps by
fitting a 3D Morphable Model (3DMM) to various multiview image and video
datasets, as well as leveraging on a new 3D dataset with over 3,000 identities.
Second, we devise a meticulously designed architecture that combines local and
global adversarial DCNNs to learn an identity-preserving facial UV completion
model. We demonstrate that by attaching the completed UV to the fitted mesh and
generating instances of arbitrary poses, we can increase pose variations for
training deep face recognition/verification models, and minimise pose
discrepancy during testing, which lead to better performance. Experiments on
both controlled and in-the-wild UV datasets prove the effectiveness of our
adversarial UV completion model. We achieve state-of-the-art verification
accuracy, $94.05\%$, under the CFP frontal-profile protocol only by combining
pose augmentation during training and pose discrepancy reduction during
testing. We will release the first in-the-wild UV dataset (we refer as WildUV)
that comprises of complete facial UV maps from 1,892 identities for research
purposes.
ClustGeo: an R package for hierarchical clustering with spatial constraints
Authors: Marie Chavent, Vanessa Kuentz-Simonet, Amaury Labenne, Jérôme Saracco
Date: 2017-07-12 00:00:00
Tasks: Clustering

In this paper, we propose a Ward-like hierarchical clustering algorithm
including spatial/geographical constraints. Two dissimilarity matrices $D_0$
and $D_1$ are inputted, along with a mixing parameter $\alpha \in [0,1]$. The
dissimilarities can be non-Euclidean and the weights of the observations can be
non-uniform. The first matrix gives the dissimilarities in the "feature space"
and the second matrix gives the dissimilarities in the "constraint space". The
criterion minimized at each stage is a convex combination of the homogeneity
criterion calculated with $D_0$ and the homogeneity criterion calculated with
$D_1$. The idea is then to determine a value of $\alpha$ which increases the
spatial contiguity without deteriorating too much the quality of the solution
based on the variables of interest i.e. those of the feature space. This
procedure is illustrated on a real dataset using the R package ClustGeo.
Learning of state-space models with highly informative observations: a tempered Sequential Monte Carlo solution
Authors: Andreas Svensson, Thomas B. Schön, Fredrik Lindsten
Date: 2017-02-06 00:00:00
Tasks: State Space Models

Probabilistic (or Bayesian) modeling and learning offers interesting
possibilities for systematic representation of uncertainty using probability
theory. However, probabilistic learning often leads to computationally
challenging problems. Some problems of this type that were previously
intractable can now be solved on standard personal computers thanks to recent
advances in Monte Carlo methods. In particular, for learning of unknown
parameters in nonlinear state-space models, methods based on the particle
filter (a Monte Carlo method) have proven very useful. A notoriously
challenging problem, however, still occurs when the observations in the
state-space model are highly informative, i.e. when there is very little or no
measurement noise present, relative to the amount of process noise. The
particle filter will then struggle in estimating one of the basic components
for probabilistic learning, namely the likelihood $p($data$|$parameters$)$. To
this end we suggest an algorithm which initially assumes that there is
substantial amount of artificial measurement noise present. The variance of
this noise is sequentially decreased in an adaptive fashion such that we, in
the end, recover the original problem or possibly a very close approximation of
it. The main component in our algorithm is a sequential Monte Carlo (SMC)
sampler, which gives our proposed method a clear resemblance to the SMC^2
method. Another natural link is also made to the ideas underlying the
approximate Bayesian computation (ABC). We illustrate it with numerical
examples, and in particular show promising results for a challenging
Wiener-Hammerstein benchmark problem.
Stability Selection for Structured Variable Selection
Authors: George Philipp, Seunghak Lee, Eric P. Xing
Date: 2017-12-13 00:00:00
Tasks: Variable Selection

In variable or graph selection problems, finding a right-sized model or
controlling the number of false positives is notoriously difficult. Recently, a
meta-algorithm called Stability Selection was proposed that can provide
reliable finite-sample control of the number of false positives. Its benefits
were demonstrated when used in conjunction with the lasso and orthogonal
matching pursuit algorithms.
  In this paper, we investigate the applicability of stability selection to
structured selection algorithms: the group lasso and the structured
input-output lasso. We find that using stability selection often increases the
power of both algorithms, but that the presence of complex structure reduces
the reliability of error control under stability selection. We give strategies
for setting tuning parameters to obtain a good model size under stability
selection, and highlight its strengths and weaknesses compared to competing
methods screen and clean and cross-validation. We give guidelines about when to
use which error control method.
Learning Disentangling and Fusing Networks for Face Completion Under Structured Occlusions
Authors: Zhihang Li, Yibo Hu, Ran He
Date: 2017-12-13 00:00:00
Tasks: Decoder, Facial Inpainting, Generative Adversarial Network

Face completion aims to generate semantically new pixels for missing facial
components. It is a challenging generative task due to large variations of face
appearance. This paper studies generative face completion under structured
occlusions. We treat the face completion and corruption as disentangling and
fusing processes of clean faces and occlusions, and propose a jointly
disentangling and fusing Generative Adversarial Network (DF-GAN). First, three
domains are constructed, corresponding to the distributions of occluded faces,
clean faces and structured occlusions. The disentangling and fusing processes
are formulated as the transformations between the three domains. Then the
disentangling and fusing networks are built to learn the transformations from
unpaired data, where the encoder-decoder structure is adopted and allows DF-GAN
to simulate structure occlusions by modifying the latent representations.
Finally, the disentangling and fusing processes are unified into a dual
learning framework along with an adversarial strategy. The proposed method is
evaluated on Meshface verification problem. Experimental results on four
Meshface databases demonstrate the effectiveness of our proposed method for the
face completion under structured occlusions.
Stochastic Low-Rank Bandits
Authors: Branislav Kveton, Csaba Szepesvari, Anup Rao, Zheng Wen, Yasin Abbasi-Yadkori, S. Muthukrishnan
Date: 2017-12-13 00:00:00
Tasks: Recommendation Systems

Many problems in computer vision and recommender systems involve low-rank
matrices. In this work, we study the problem of finding the maximum entry of a
stochastic low-rank matrix from sequential observations. At each step, a
learning agent chooses pairs of row and column arms, and receives the noisy
product of their latent values as a reward. The main challenge is that the
latent values are unobserved. We identify a class of non-negative matrices
whose maximum entry can be found statistically efficiently and propose an
algorithm for finding them, which we call LowRankElim. We derive a
$\DeclareMathOperator{\poly}{poly} O((K + L) \poly(d) \Delta^{-1} \log n)$
upper bound on its $n$-step regret, where $K$ is the number of rows, $L$ is the
number of columns, $d$ is the rank of the matrix, and $\Delta$ is the minimum
gap. The bound depends on other problem-specific constants that clearly do not
depend $K L$. To the best of our knowledge, this is the first such result in
the literature.
The Effectiveness of Data Augmentation in Image Classification using Deep Learning
Authors: Luis Perez, Jason Wang
Date: 2017-12-13 00:00:00
Tasks: Data Augmentation, General Classification, image-classification, Image Classification

In this paper, we explore and compare multiple solutions to the problem of
data augmentation in image classification. Previous work has demonstrated the
effectiveness of data augmentation through simple techniques, such as cropping,
rotating, and flipping input images. We artificially constrain our access to
data to a small subset of the ImageNet dataset, and compare each data
augmentation technique in turn. One of the more successful data augmentations
strategies is the traditional transformations mentioned above. We also
experiment with GANs to generate images of different styles. Finally, we
propose a method to allow a neural net to learn augmentations that best improve
the classifier, which we call neural augmentation. We discuss the successes and
shortcomings of this method on various datasets.
Transfer Adversarial Hashing for Hamming Space Retrieval
Authors: Zhangjie Cao, Mingsheng Long, Chao Huang, Jian-Min Wang
Date: 2017-12-13 00:00:00
Tasks: Deep Hashing, Image Retrieval, Retrieval

Hashing is widely applied to large-scale image retrieval due to the storage
and retrieval efficiency. Existing work on deep hashing assumes that the
database in the target domain is identically distributed with the training set
in the source domain. This paper relaxes this assumption to a transfer
retrieval setting, which allows the database and the training set to come from
different but relevant domains. However, the transfer retrieval setting will
introduce two technical difficulties: first, the hash model trained on the
source domain cannot work well on the target domain due to the large
distribution gap; second, the domain gap makes it difficult to concentrate the
database points to be within a small Hamming ball. As a consequence, transfer
retrieval performance within Hamming Radius 2 degrades significantly in
existing hashing methods. This paper presents Transfer Adversarial Hashing
(TAH), a new hybrid deep architecture that incorporates a pairwise
$t$-distribution cross-entropy loss to learn concentrated hash codes and an
adversarial network to align the data distributions between the source and
target domains. TAH can generate compact transfer hash codes for efficient
image retrieval on both source and target domains. Comprehensive experiments
validate that TAH yields state of the art Hamming space retrieval performance
on standard datasets.
Inverse Reinforcement Learning for Marketing
Authors: Igor Halperin
Date: 2017-12-13 00:00:00
Tasks: Marketing, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning customer preferences from an observed behaviour is an important
topic in the marketing literature. Structural models typically model
forward-looking customers or firms as utility-maximizing agents whose utility
is estimated using methods of Stochastic Optimal Control. We suggest an
alternative approach to study dynamic consumer demand, based on Inverse
Reinforcement Learning (IRL). We develop a version of the Maximum Entropy IRL
that leads to a highly tractable model formulation that amounts to
low-dimensional convex optimization in the search for optimal model parameters.
Using simulations of consumer demand, we show that observational noise for
identical customers can be easily confused with an apparent consumer
heterogeneity.
Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples
Authors: Jihun Hamm, Akshay Mehra
Date: 2017-11-12 00:00:00
Proceeding: ICLR 2018 1

Recently, researchers have discovered that the state-of-the-art object
classifiers can be fooled easily by small perturbations in the input
unnoticeable to human eyes. It is also known that an attacker can generate
strong adversarial examples if she knows the classifier parameters. Conversely,
a defender can robustify the classifier by retraining if she has access to the
adversarial examples. We explain and formulate this adversarial example problem
as a two-player continuous zero-sum game, and demonstrate the fallacy of
evaluating a defense or an attack as a static problem. To find the best
worst-case defense against whitebox attacks, we propose a continuous minimax
optimization algorithm. We demonstrate the minimax defense with two types of
attack classes -- gradient-based and neural network-based attacks. Experiments
with the MNIST and the CIFAR-10 datasets demonstrate that the defense found by
numerical minimax optimization is indeed more robust than non-minimax defenses.
We discuss directions for improving the result toward achieving robustness
against multiple types of attack classes.
Review. Machine learning techniques for traffic sign detection
Authors: Rinat Mukhometzianov, Ying Wang
Date: 2017-12-12 00:00:00
Tasks: BIG-bench Machine Learning, Traffic Sign Detection

An automatic road sign detection system localizes road signs from within
images captured by an on-board camera of a vehicle, and support the driver to
properly ride the vehicle. Most existing algorithms include a preprocessing
step, feature extraction and detection step. This paper arranges the methods
applied to road sign detection into two groups: general machine learning,
neural networks. In this review, the issues related to automatic road sign
detection are addressed, the popular existing methods developed to tackle the
road sign detection problem are reviewed, and a comparison of the features of
these methods is composed.
Sparse Phase Retrieval via Sparse PCA Despite Model Misspecification: A Simplified and Extended Analysis
Authors: Yan Shuo Tan
Date: 2017-12-12 00:00:00
Tasks: Retrieval

We consider the problem of high-dimensional misspecified phase retrieval.
This is where we have an $s$-sparse signal vector $\mathbf{x}_*$ in
$\mathbb{R}^n$, which we wish to recover using sampling vectors
$\textbf{a}_1,\ldots,\textbf{a}_m$, and measurements $y_1,\ldots,y_m$, which
are related by the equation $f(\left<\textbf{a}_i,\textbf{x}_*\right>) = y_i$.
Here, $f$ is an unknown link function satisfying a positive correlation with
the quadratic function. This problem was analyzed in a recent paper by Neykov,
Wang and Liu, who provided recovery guarantees for a two-stage algorithm with
sample complexity $m = O(s^2\log n)$. In this paper, we show that the first
stage of their algorithm suffices for signal recovery with the same sample
complexity, and extend the analysis to non-Gaussian measurements. Furthermore,
we show how the algorithm can be generalized to recover a signal vector
$\textbf{x}_*$ efficiently given geometric prior information other than
sparsity.
Multi-focus Attention Network for Efficient Deep Reinforcement Learning
Authors: Jinyoung Choi, Beom-Jin Lee, Byoung-Tak Zhang
Date: 2017-12-13 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (DRL) has shown incredible performance in
learning various tasks to the human level. However, unlike human perception,
current DRL models connect the entire low-level sensory input to the
state-action values rather than exploiting the relationship between and among
entities that constitute the sensory input. Because of this difference, DRL
needs vast amount of experience samples to learn. In this paper, we propose a
Multi-focus Attention Network (MANet) which mimics human ability to spatially
abstract the low-level sensory input into multiple entities and attend to them
simultaneously. The proposed method first divides the low-level input into
several segments which we refer to as partial states. After this segmentation,
parallel attention layers attend to the partial states relevant to solving the
task. Our model estimates state-action values using these attended partial
states. In our experiments, MANet attains highest scores with significantly
less experience samples. Additionally, the model shows higher performance
compared to the Deep Q-network and the single attention model as benchmarks.
Furthermore, we extend our model to attentive communication model for
performing multi-agent cooperative tasks. In multi-agent cooperative task
experiments, our model shows 20% faster learning than existing state-of-the-art
model.
Zero-Shot Learning via Category-Specific Visual-Semantic Mapping
Authors: Li Niu, Jianfei Cai, Ashok Veeraraghavan
Date: 2017-11-16 00:00:00
Tasks: General Classification, image-classification, Image Classification, Zero-Shot Learning

Zero-Shot Learning (ZSL) aims to classify a test instance from an unseen
category based on the training instances from seen categories, in which the gap
between seen categories and unseen categories is generally bridged via
visual-semantic mapping between the low-level visual feature space and the
intermediate semantic space. However, the visual-semantic mapping learnt based
on seen categories may not generalize well to unseen categories because the
data distributions between seen categories and unseen categories are
considerably different, which is known as the projection domain shift problem
in ZSL. To address this domain shift issue, we propose a method named Adaptive
Embedding ZSL (AEZSL) to learn an adaptive visual-semantic mapping for each
unseen category based on the similarities between each unseen category and all
the seen categories. Then, we further make two extensions based on our AEZSL
method. Firstly, in order to utilize the unlabeled test instances from unseen
categories, we extend our AEZSL to a semi-supervised approach named AEZSL with
Label Refinement (AEZSL_LR), in which a progressive approach is developed to
update the visual classifiers and refine the predicted test labels
alternatively based on the similarities among test instances and among unseen
categories. Secondly, to avoid learning visual-semantic mapping for each unseen
category in the large-scale classification task, we extend our AEZSL to a deep
adaptive embedding model named Deep AEZSL (DAEZSL) sharing the similar idea
(i.e., visual-semantic mapping should be category-specific and related to the
semantic space) with AEZSL, which only needs to be trained once, but can be
applied to arbitrary number of unseen categories. Extensive experiments
demonstrate that our proposed methods achieve the state-of-the-art results for
image classification on four benchmark datasets.
Understanding and Optimizing the Performance of Distributed Machine Learning Applications on Apache Spark
Authors: Celestine Dünner, Thomas Parnell, Kubilay Atasu, Manolis Sifalakis, Haralampos Pozidis
Date: 2016-12-05 00:00:00
Tasks: BIG-bench Machine Learning, Computational Efficiency

In this paper we explore the performance limits of Apache Spark for machine
learning applications. We begin by analyzing the characteristics of a
state-of-the-art distributed machine learning algorithm implemented in Spark
and compare it to an equivalent reference implementation using the high
performance computing framework MPI. We identify critical bottlenecks of the
Spark framework and carefully study their implications on the performance of
the algorithm. In order to improve Spark performance we then propose a number
of practical techniques to alleviate some of its overheads. However, optimizing
computational efficiency and framework related overheads is not the only key to
performance -- we demonstrate that in order to get the best performance out of
any implementation it is necessary to carefully tune the algorithm to the
respective trade-off between computation time and communication latency. The
optimal trade-off depends on both the properties of the distributed algorithm
as well as infrastructure and framework-related characteristics. Finally, we
apply these technical and algorithmic optimizations to three different
distributed linear machine learning algorithms that have been implemented in
Spark. We present results using five large datasets and demonstrate that by
using the proposed optimizations, we can achieve a reduction in the performance
difference between Spark and MPI from 20x to 2x.
Consideration on Example 2 of "An Algorithm of General Fuzzy InferenceWith The Reductive Property"
Authors: Son-Il Kwak, Oh-Chol Gwon, Chung-Jin Kwak
Date: 2017-12-13 00:00:00

In this paper, we will show that (1) the results about the fuzzy reasoning
algoritm obtained in the paper "Computer Sciences Vol. 34, No.4, pp.145-148,
2007" according to the paper "IEEE Transactions On systems, Man and
cybernetics, 18, pp.1049-1056, 1988" are correct; (2) example 2 in the paper
"An Algorithm of General Fuzzy Inference With The Reductive Property" presented
by He Ying-Si, Quan Hai-Jin and Deng Hui-Wen according to the paper "An
approximate analogical reasoning approach based on similarity measures"
presented by Tursken I.B. and Zhong zhao is incorrect; (3) the mistakes in
their paper are modified and then a calculation example of FMT is supplemented.
A Novel Document Generation Process for Topic Detection based on Hierarchical Latent Tree Models
Authors: Peixian Chen, Zhourong Chen, Nevin L. Zhang
Date: 2017-12-12 00:00:00
Tasks: Topic Models

We propose a novel document generation process based on hierarchical latent tree models (HLTMs) learned from data. An HLTM has a layer of observed word variables at the bottom and multiple layers of latent variables on top. For each document, we first sample values for the latent variables layer by layer via logic sampling, then draw relative frequencies for the words conditioned on the values of the latent variables, and finally generate words for the document using the relative word frequencies. The motivation for the work is to take word counts into consideration with HLTMs. In comparison with LDA-based hierarchical document generation processes, the new process achieves drastically better model fit with much fewer parameters. It also yields more meaningful topics and topic hierarchies. It is the new state-of-the-art for the hierarchical topic detection.
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning
Authors: Zhen He, Shao-Bing Gao, Liang Xiao, Daxue Liu, Hangen He, David Barber
Date: 2017-11-05 00:00:00
Proceeding: NeurIPS 2017 12

Long Short-Term Memory (LSTM) is a popular approach to boosting the ability
of Recurrent Neural Networks to store longer term temporal information. The
capacity of an LSTM network can be increased by widening and adding layers.
However, usually the former introduces additional parameters, while the latter
increases the runtime. As an alternative we propose the Tensorized LSTM in
which the hidden states are represented by tensors and updated via a
cross-layer convolution. By increasing the tensor size, the network can be
widened efficiently without additional parameters since the parameters are
shared across different locations in the tensor; by delaying the output, the
network can be deepened implicitly with little additional runtime since deep
computations for each timestep are merged into temporal computations of the
sequence. Experiments conducted on five challenging sequence learning tasks
show the potential of the proposed model.