pwc-archive/papers-with-abstracts huggingface dataset:
Enhanced Deep Residual Networks for Single Image Super-Resolution
Authors: Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, Kyoung Mu Lee
Date: 2017-07-10 00:00:00
Tasks: Image Super-Resolution, Spectral Reconstruction, Super-Resolution

Recent research on super-resolution has progressed with the development of
deep convolutional neural networks (DCNN). In particular, residual learning
techniques exhibit improved performance. In this paper, we develop an enhanced
deep super-resolution network (EDSR) with performance exceeding those of
current state-of-the-art SR methods. The significant performance improvement of
our model is due to optimization by removing unnecessary modules in
conventional residual networks. The performance is further improved by
expanding the model size while we stabilize the training procedure. We also
propose a new multi-scale deep super-resolution system (MDSR) and training
method, which can reconstruct high-resolution images of different upscaling
factors in a single model. The proposed methods show superior performance over
the state-of-the-art methods on benchmark datasets and prove its excellence by
winning the NTIRE2017 Super-Resolution Challenge.
Low Dose CT Image Reconstruction With Learned Sparsifying Transform
Authors: Xuehang Zheng, Zening Lu, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler
Date: 2017-07-10 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction, Image Reconstruction

A major challenge in computed tomography (CT) is to reduce X-ray dose to a
low or even ultra-low level while maintaining the high quality of reconstructed
images. We propose a new method for CT reconstruction that combines penalized
weighted-least squares reconstruction (PWLS) with regularization based on a
sparsifying transform (PWLS-ST) learned from a dataset of numerous CT images.
We adopt an alternating algorithm to optimize the PWLS-ST cost function that
alternates between a CT image update step and a sparse coding step. We adopt a
relaxed linearized augmented Lagrangian method with ordered-subsets (relaxed
OS-LALM) to accelerate the CT image update step by reducing the number of
forward and backward projections. Numerical experiments on the XCAT phantom
show that for low dose levels, the proposed PWLS-ST method dramatically
improves the quality of reconstructed images compared to PWLS reconstruction
with a nonadaptive edge-preserving regularizer (PWLS-EP).
A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning
Authors: Honglun Zhang, Liqiang Xiao, Yongkun Wang, Yaohui Jin
Date: 2017-07-10 00:00:00
Tasks: General Classification, Multi-Task Learning, text-classification, Text Classification

Multi-task learning leverages potential correlations among related tasks to
extract common features and yield performance gains. However, most previous
works only consider simple or weak interactions, thereby failing to model
complex correlations among three or more tasks. In this paper, we propose a
multi-task learning architecture with four types of recurrent neural layers to
fuse information across multiple related tasks. The architecture is
structurally flexible and considers various interactions among tasks, which can
be regarded as a generalized case of many previous works. Extensive experiments
on five benchmark datasets for text classification show that our model can
significantly improve performances of related tasks with additional information
from others.
Sample Efficient Actor-Critic with Experience Replay
Authors: Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Remi Munos, Koray Kavukcuoglu, Nando de Freitas
Date: 2016-11-03 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents an actor-critic deep reinforcement learning agent with
experience replay that is stable, sample efficient, and performs remarkably
well on challenging environments, including the discrete 57-game Atari domain
and several continuous control problems. To achieve this, the paper introduces
several innovations, including truncated importance sampling with bias
correction, stochastic dueling network architectures, and a new trust region
policy optimization method.
Learning human behaviors from motion capture by adversarial imitation
Authors: Josh Merel, Yuval Tassa, Dhruva TB, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, Nicolas Heess
Date: 2017-07-07 00:00:00
Tasks: Deep Reinforcement Learning, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Rapid progress in deep reinforcement learning has made it increasingly
feasible to train controllers for high-dimensional humanoid bodies. However,
methods that use pure reinforcement learning with simple reward functions tend
to produce non-humanlike and overly stereotyped movement behaviors. In this
work, we extend generative adversarial imitation learning to enable training of
generic neural network policies to produce humanlike movement patterns from
limited demonstrations consisting only of partially observed state features,
without access to actions, even when the demonstrations come from a body with
different and unknown physical parameters. We leverage this approach to build
sub-skill policies from motion capture data and show that they can be reused to
solve tasks when controlled by a higher level controller.
Adaptive Binarization for Weakly Supervised Affordance Segmentation
Authors: Johann Sawatzky, Juergen Gall
Date: 2017-07-10 00:00:00
Tasks: Binarization, Object, Segmentation

The concept of affordance is important to understand the relevance of object
parts for a certain functional interaction. Affordance types generalize across
object categories and are not mutually exclusive. This makes the segmentation
of affordance regions of objects in images a difficult task. In this work, we
build on an iterative approach that learns a convolutional neural network for
affordance segmentation from sparse keypoints. During this process, the
predictions of the network need to be binarized. In this work, we propose an
adaptive approach for binarization and estimate the parameters for
initialization by approximated cross validation. We evaluate our approach on
two affordance datasets where our approach outperforms the state-of-the-art for
weakly supervised affordance segmentation.
Scale-Regularized Filter Learning
Authors: Marco Loog, François Lauze
Date: 2017-07-10 00:00:00

We start out by demonstrating that an elementary learning task, corresponding
to the training of a single linear neuron in a convolutional neural network,
can be solved for feature spaces of very high dimensionality. In a second step,
acknowledging that such high-dimensional learning tasks typically benefit from
some form of regularization and arguing that the problem of scale has not been
taken care of in a very satisfactory manner, we come to a combined resolution
of both of these shortcomings by proposing a form of scale regularization.
Moreover, using variational method, this regularization problem can also be
solved rather efficiently and we demonstrate, on an artificial filter learning
problem, the capabilities of our basic linear neuron. From a more general
standpoint, we see this work as prime example of how learning and variational
methods could, or even should work to their mutual benefit.
Towards Crafting Text Adversarial Samples
Authors: Suranjana Samanta, Sameep Mehta
Date: 2017-07-10 00:00:00
Tasks: Adversarial Text, Sentiment Analysis

Adversarial samples are strategically modified samples, which are crafted
with the purpose of fooling a classifier at hand. An attacker introduces
specially crafted adversarial samples to a deployed classifier, which are being
mis-classified by the classifier. However, the samples are perceived to be
drawn from entirely different classes and thus it becomes hard to detect the
adversarial samples. Most of the prior works have been focused on synthesizing
adversarial samples in the image domain. In this paper, we propose a new method
of crafting adversarial text samples by modification of the original samples.
Modifications of the original text samples are done by deleting or replacing
the important or salient words in the text or by introducing new words in the
text sample. Our algorithm works best for the datasets which have
sub-categories within each of the classes of examples. While crafting
adversarial samples, one of the key constraint is to generate meaningful
sentences which can at pass off as legitimate from language (English)
viewpoint. Experimental results on IMDB movie review dataset for sentiment
analysis and Twitter dataset for gender detection show the efficiency of our
proposed method.
Non-parametric Estimation of Stochastic Differential Equations with Sparse Gaussian Processes
Authors: Constantino A. García, Abraham Otero, Paulo Félix, Jesús Presedo, David G. Márquez
Date: 2017-04-14 00:00:00
Tasks: Gaussian Processes, Time Series, Time Series Analysis

The application of Stochastic Differential Equations (SDEs) to the analysis
of temporal data has attracted increasing attention, due to their ability to
describe complex dynamics with physically interpretable equations. In this
paper, we introduce a non-parametric method for estimating the drift and
diffusion terms of SDEs from a densely observed discrete time series. The use
of Gaussian processes as priors permits working directly in a function-space
view and thus the inference takes place directly in this space. To cope with
the computational complexity that requires the use of Gaussian processes, a
sparse Gaussian process approximation is provided. This approximation permits
the efficient computation of predictions for the drift and diffusion terms by
using a distribution over a small subset of pseudo-samples. The proposed method
has been validated using both simulated data and real data from economy and
paleoclimatology. The application of the method to real data demonstrates its
ability to capture the behaviour of complex systems.
Hierarchical Temporal Representation in Linear Reservoir Computing
Authors: Claudio Gallicchio, Alessio Micheli, Luca Pedrelli
Date: 2017-05-16 00:00:00

Recently, studies on deep Reservoir Computing (RC) highlighted the role of
layering in deep recurrent neural networks (RNNs). In this paper, the use of
linear recurrent units allows us to bring more evidence on the intrinsic
hierarchical temporal representation in deep RNNs through frequency analysis
applied to the state signals. The potentiality of our approach is assessed on
the class of Multiple Superimposed Oscillator tasks. Furthermore, our
investigation provides useful insights to open a discussion on the main aspects
that characterize the deep learning framework in the temporal domain.
Block modelling in dynamic networks with non-homogeneous Poisson processes and exact ICL
Authors: Marco Corneli, Pierre Latouche, Fabrice Rossi
Date: 2017-07-10 00:00:00

We develop a model in which interactions between nodes of a dynamic network
are counted by non homogeneous Poisson processes. In a block modelling
perspective, nodes belong to hidden clusters (whose number is unknown) and the
intensity functions of the counting processes only depend on the clusters of
nodes. In order to make inference tractable we move to discrete time by
partitioning the entire time horizon in which interactions are observed in
fixed-length time sub-intervals. First, we derive an exact integrated
classification likelihood criterion and maximize it relying on a greedy search
approach. This allows to estimate the memberships to clusters and the number of
clusters simultaneously. Then a maximum-likelihood estimator is developed to
estimate non parametrically the integrated intensities. We discuss the
over-fitting problems of the model and propose a regularized version solving
these issues. Experiments on real and simulated data are carried out in order
to assess the proposed methodology.
Understanding State Preferences With Text As Data: Introducing the UN General Debate Corpus
Authors: Alexander Baturo, Niheer Dasandi, Slava J. Mikhaylov
Date: 2017-07-10 00:00:00

Every year at the United Nations, member states deliver statements during the
General Debate discussing major issues in world politics. These speeches
provide invaluable information on governments' perspectives and preferences on
a wide range of issues, but have largely been overlooked in the study of
international politics. This paper introduces a new dataset consisting of over
7,701 English-language country statements from 1970-2016. We demonstrate how
the UN General Debate Corpus (UNGDC) can be used to derive country positions on
different policy dimensions using text analytic methods. The paper provides
applications of these estimates, demonstrating the contribution the UNGDC can
make to the study of international politics.
Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networks
Authors: Marco Corneli, Pierre Latouche, Fabrice Rossi
Date: 2016-05-09 00:00:00
Tasks: Stochastic Block Model

The stochastic block model (SBM) is a flexible probabilistic tool that can be
used to model interactions between clusters of nodes in a network. However, it
does not account for interactions of time varying intensity between clusters.
The extension of the SBM developed in this paper addresses this shortcoming
through a temporal partition: assuming interactions between nodes are recorded
on fixed-length time intervals, the inference procedure associated with the
model we propose allows to cluster simultaneously the nodes of the network and
the time intervals. The number of clusters of nodes and of time intervals, as
well as the memberships to clusters, are obtained by maximizing an exact
integrated complete-data likelihood, relying on a greedy search approach.
Experiments on simulated and real data are carried out in order to assess the
proposed methodology.
Mean Absolute Percentage Error for regression models
Authors: Arnaud De Myttenaere, Boris Golden, Bénédicte Le Grand, Fabrice Rossi
Date: 2016-05-09 00:00:00
Tasks: regression

We study in this paper the consequences of using the Mean Absolute Percentage
Error (MAPE) as a measure of quality for regression models. We prove the
existence of an optimal MAPE model and we show the universal consistency of
Empirical Risk Minimization based on the MAPE. We also show that finding the
best model under the MAPE is equivalent to doing weighted Mean Absolute Error
(MAE) regression, and we apply this weighting strategy to kernel regression.
The behavior of the MAPE kernel regression is illustrated on simulated data.
Subdeterminant Maximization via Nonconvex Relaxations and Anti-concentration
Authors: Javad B. Ebrahimi, Damian Straszak, Nisheeth K. Vishnoi
Date: 2017-07-10 00:00:00
Tasks: Data Summarization, Fairness

Several fundamental problems that arise in optimization and computer science
can be cast as follows: Given vectors $v_1,\ldots,v_m \in \mathbb{R}^d$ and a
constraint family ${\cal B}\subseteq 2^{[m]}$, find a set $S \in \cal{B}$ that
maximizes the squared volume of the simplex spanned by the vectors in $S$. A
motivating example is the data-summarization problem in machine learning where
one is given a collection of vectors that represent data such as documents or
images. The volume of a set of vectors is used as a measure of their diversity,
and partition or matroid constraints over $[m]$ are imposed in order to ensure
resource or fairness constraints. Recently, Nikolov and Singh presented a
convex program and showed how it can be used to estimate the value of the most
diverse set when ${\cal B}$ corresponds to a partition matroid. This result was
recently extended to regular matroids in works of Straszak and Vishnoi, and
Anari and Oveis Gharan. The question of whether these estimation algorithms can
be converted into the more useful approximation algorithms -- that also output
a set -- remained open.
  The main contribution of this paper is to give the first approximation
algorithms for both partition and regular matroids. We present novel
formulations for the subdeterminant maximization problem for these matroids;
this reduces them to the problem of finding a point that maximizes the absolute
value of a nonconvex function over a Cartesian product of probability
simplices. The technical core of our results is a new anti-concentration
inequality for dependent random variables that allows us to relate the optimal
value of these nonconvex functions to their value at a random point. Unlike
prior work on the constrained subdeterminant maximization problem, our proofs
do not rely on real-stability or convexity and could be of independent interest
both in algorithms and complexity.
Improving speaker turn embedding by crossmodal transfer learning from face embedding
Authors: Nam Le, Jean-Marc Odobez
Date: 2017-07-10 00:00:00
Tasks: Clustering, Face Verification, Transfer Learning

Learning speaker turn embeddings has shown considerable improvement in
situations where conventional speaker modeling approaches fail. However, this
improvement is relatively limited when compared to the gain observed in face
embedding learning, which has been proven very successful for face verification
and clustering tasks. Assuming that face and voices from the same identities
share some latent properties (like age, gender, ethnicity), we propose three
transfer learning approaches to leverage the knowledge from the face domain
(learned from thousands of images and identities) for tasks in the speaker
domain. These approaches, namely target embedding transfer, relative distance
transfer, and clustering structure transfer, utilize the structure of the
source face embedding space at different granularities to regularize the target
speaker turn embedding space as optimizing terms. Our methods are evaluated on
two public broadcast corpora and yield promising advances over competitive
baselines in verification and audio clustering tasks, especially when dealing
with short speaker utterances. The analysis of the results also gives insight
into characteristics of the embedding spaces and shows their potential
applications.
Residual Value Forecasting Using Asymmetric Cost Functions
Authors: Korbinian Dress, Stefan Lessmann, Hans-Jörg von Mettenheim
Date: 2017-07-10 00:00:00

Leasing is a popular channel to market new cars. Pricing a leasing contract
is complicated because the leasing rate embodies an expectation of the residual
value of the car after contract expiration. To aid lessors in their pricing
decisions, the paper develops resale price forecasting models. A peculiarity of
the leasing business is that forecast errors entail different costs.
Identifying effective ways to address this characteristic is the main objective
of the paper. More specifically, the paper contributes to the literature
through i) consolidating and integrating previous work in forecasting with
asymmetric cost of error functions, ii) systematically evaluating previous
approaches and comparing them to a new approach, and iii) demonstrating that
forecasting with asymmetric cost of error functions enhances the quality of
decision support in car leasing. For example, under the assumption that the
costs of overestimating resale prices is twice that of the opposite error,
incorporating corresponding cost asymmetry into forecast model development
reduces decision costs by about eight percent, compared to a standard
forecasting model. Higher asymmetry produces even larger improvements.
Synthesis-based Robust Low Resolution Face Recognition
Authors: Sumit Shekhar, Vishal M. Patel, Rama Chellappa
Date: 2017-07-10 00:00:00
Tasks: Dictionary Learning, Face Recognition

Recognition of low resolution face images is a challenging problem in many
practical face recognition systems. Methods have been proposed in the face
recognition literature for the problem which assume that the probe is low
resolution, but a high resolution gallery is available for recognition. These
attempts have been aimed at modifying the probe image such that the resultant
image provides better discrimination. We formulate the problem differently by
leveraging the information available in the high resolution gallery image and
propose a dictionary learning approach for classifying the low-resolution probe
image. An important feature of our algorithm is that it can handle resolution
change along with illumination variations. Furthermore, we also kernelize the
algorithm to handle non-linearity in data and present a joint dictionary
learning technique for robust recognition at low resolutions. The effectiveness
of the proposed method is demonstrated using standard datasets and a
challenging outdoor face dataset. It is shown that our method is efficient and
can perform significantly better than many competitive low resolution face
recognition algorithms.
EAST: An Efficient and Accurate Scene Text Detector
Authors: Xinyu Zhou, Cong Yao, He Wen, Yuzhi Wang, Shuchang Zhou, Weiran He, Jiajun Liang
Date: 2017-04-11 00:00:00
Proceeding: CVPR 2017 7
Tasks: Curved Text Detection, Optical Character Recognition (OCR), Scene Text Detection, Text Detection

Previous approaches for scene text detection have already achieved promising
performances across various benchmarks. However, they usually fall short when
dealing with challenging scenarios, even when equipped with deep neural network
models, because the overall performance is determined by the interplay of
multiple stages and components in the pipelines. In this work, we propose a
simple yet powerful pipeline that yields fast and accurate text detection in
natural scenes. The pipeline directly predicts words or text lines of arbitrary
orientations and quadrilateral shapes in full images, eliminating unnecessary
intermediate steps (e.g., candidate aggregation and word partitioning), with a
single neural network. The simplicity of our pipeline allows concentrating
efforts on designing loss functions and neural network architecture.
Experiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500
demonstrate that the proposed algorithm significantly outperforms
state-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR
2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps
at 720p resolution.
Stochastic Variance Reduction Gradient for a Non-convex Problem Using Graduated Optimization
Authors: Li Chen, Shuisheng Zhou, Zhuan Zhang
Date: 2017-07-10 00:00:00

In machine learning, nonconvex optimization problems with multiple local
optimums are often encountered. Graduated Optimization Algorithm (GOA) is a
popular heuristic method to obtain global optimums of nonconvex problems
through progressively minimizing a series of convex approximations to the
nonconvex problems more and more accurate. Recently, such an algorithm GradOpt
based on GOA is proposed with amazing theoretical and experimental results, but
it mainly studies the problem which consists of one nonconvex part. This paper
aims to find the global solution of a nonconvex objective with a convex part
plus a nonconvex part based on GOA. By graduating approximating non-convex part
of the problem and minimizing them with the Stochastic Variance Reduced
Gradient (SVRG) or proximal SVRG, two new algorithms, SVRG-GOA and PSVRG-GOA,
are proposed. We prove that the new algorithms have lower iteration complexity
($O(1/\varepsilon)$) than GradOpt ($O(1/\varepsilon^2)$). Some tricks, such as
enlarging shrink factor, using project step, stochastic gradient, and
mini-batch skills, are also given to accelerate the convergence speed of the
proposed algorithms. Experimental results illustrate that the new algorithms
with the similar performance can converge to 'global' optimums of the nonconvex
problems, and they converge faster than the GradOpt and the nonconvex proximal
SVRG.