pwc-archive/papers-with-abstracts huggingface dataset:
Learning a Variational Network for Reconstruction of Accelerated MRI Data
Authors: Kerstin Hammernik, Teresa Klatzer, Erich Kobler, Michael P. Recht, Daniel K. Sodickson, Thomas Pock, Florian Knoll
Date: 2017-04-03 00:00:00
Tasks: compressed sensing, Image Reconstruction, Learning Theory

Purpose: To allow fast and high-quality reconstruction of clinical
accelerated multi-coil MR data by learning a variational network that combines
the mathematical structure of variational models with deep learning.
  Theory and Methods: Generalized compressed sensing reconstruction formulated
as a variational model is embedded in an unrolled gradient descent scheme. All
parameters of this formulation, including the prior model defined by filter
kernels and activation functions as well as the data term weights, are learned
during an offline training procedure. The learned model can then be applied
online to previously unseen data.
  Results: The variational network approach is evaluated on a clinical knee
imaging protocol. The variational network reconstructions outperform standard
reconstruction algorithms in terms of image quality and residual artifacts for
all tested acceleration factors and sampling patterns.
  Conclusion: Variational network reconstructions preserve the natural
appearance of MR images as well as pathologies that were not included in the
training data set. Due to its high computational performance, i.e.,
reconstruction time of 193 ms on a single graphics card, and the omission of
parameter tuning once the network is trained, this new approach to image
reconstruction can easily be integrated into clinical workflow.
Combining Lexical and Syntactic Features for Detecting Content-dense Texts in News
Authors: Yinfei Yang, Ani Nenkova
Date: 2017-04-03 00:00:00
Tasks: Articles, Question Answering

Content-dense news report important factual information about an event in
direct, succinct manner. Information seeking applications such as information
extraction, question answering and summarization normally assume all text they
deal with is content-dense. Here we empirically test this assumption on news
articles from the business, U.S. international relations, sports and science
journalism domains. Our findings clearly indicate that about half of the news
texts in our study are in fact not content-dense and motivate the development
of a supervised content-density detector. We heuristically label a large
training corpus for the task and train a two-layer classifying model based on
lexical and unlexicalized syntactic features. On manually annotated data, we
compare the performance of domain-specific classifiers, trained on data only
from a given news domain and a general classifier in which data from all four
domains is pooled together. Our annotation and prediction experiments
demonstrate that the concept of content density varies depending on the domain
and that naive annotators provide judgement biased toward the stereotypical
domain label. Domain-specific classifiers are more accurate for domains in
which content-dense texts are typically fewer. Domain independent classifiers
reproduce better naive crowdsourced judgements. Classification prediction is
high across all conditions, around 80%.
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation
Authors: Chunpeng Wu, Wei Wen, Tariq Afzal, Yongmei Zhang, Yiran Chen, Hai Li
Date: 2017-03-12 00:00:00
Proceeding: CVPR 2017 7
Tasks: Classification, Domain Adaptation, General Classification, image-classification, Image Classification, Model Compression

Recently, DNN model compression based on network architecture design, e.g.,
SqueezeNet, attracted a lot attention. No accuracy drop on image classification
is observed on these extremely compact networks, compared to well-known models.
An emerging question, however, is whether these model compression techniques
hurt DNN's learning ability other than classifying images on a single dataset.
Our preliminary experiment shows that these compression methods could degrade
domain adaptation (DA) ability, though the classification performance is
preserved. Therefore, we propose a new compact network architecture and
unsupervised DA method in this paper. The DNN is built on a new basic module
Conv-M which provides more diverse feature extractors without significantly
increasing parameters. The unified framework of our DA method will
simultaneously learn invariance across domains, reduce divergence of feature
representations, and adapt label prediction. Our DNN has 4.1M parameters, which
is only 6.7% of AlexNet or 59% of GoogLeNet. Experiments show that our DNN
obtains GoogLeNet-level accuracy both on classification and DA, and our DA
method slightly outperforms previous competitive ones. Put all together, our DA
strategy based on our DNN achieves state-of-the-art on sixteen of total
eighteen DA tasks on popular Office-31 and Office-Caltech datasets.
We Can "See" You via Wi-Fi - WiFi Action Recognition via Vision-based Methods
Authors: Jen-Yin Chang, Kuan-Ying Lee, Yu-Lin Wei, Kate Ching-Ju Lin, Winston Hsu
Date: 2016-08-19 00:00:00
Tasks: Action Recognition, Person Identification, Temporal Action Localization

Recently, Wi-Fi has caught tremendous attention for its ubiquity, and,
motivated by Wi-Fi's low cost and privacy preservation, researchers have been
putting lots of investigation into its potential on action recognition and even
person identification. In this paper, we offer an comprehensive overview on
these two topics in Wi-Fi. Also, through looking at these two topics from an
unprecedented perspective, we could achieve generality instead of designing
specific ad-hoc features for each scenario. Observing the great resemblance of
Channel State Information (CSI, a fine-grained information captured from the
received Wi-Fi signal) to texture, we proposed a brand-new framework based on
computer vision methods. To minimize the effect of location dependency embedded
in CSI, we propose a novel de-noising method based on Singular Value
Decomposition (SVD) to eliminate the background energy and effectively extract
the channel information of signals reflected by human bodies. From the
experiments conducted, we demonstrate the feasibility and efficacy of the
proposed methods. Also, we conclude factors that would affect the performance
and highlight a few promising issues that require further deliberation.
Max-Margin based Discriminative Feature Learning
Authors: Changsheng Li, Qingshan Liu, Weishan Dong, Xin Zhang, Lin Yang
Date: 2014-12-16 00:00:00
Tasks: General Classification, Multi-class Classification

In this paper, we propose a new max-margin based discriminative feature
learning method. Specifically, we aim at learning a low-dimensional feature
representation, so as to maximize the global margin of the data and make the
samples from the same class as close as possible. In order to enhance the
robustness to noise, a $l_{2,1}$ norm constraint is introduced to make the
transformation matrix in group sparsity. In addition, for multi-class
classification tasks, we further intend to learn and leverage the correlation
relationships among multiple class tasks for assisting in learning
discriminative features. The experimental results demonstrate the power of the
proposed method against the related state-of-the-art methods.
Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks
Authors: Le Hou, Chen-Ping Yu, Dimitris Samaras
Date: 2016-11-17 00:00:00
Tasks: Age Classification, General Classification

In the context of single-label classification, despite the huge success of
deep learning, the commonly used cross-entropy loss function ignores the
intricate inter-class relationships that often exist in real-life tasks such as
age classification. In this work, we propose to leverage these relationships
between classes by training deep nets with the exact squared Earth Mover's
Distance (also known as Wasserstein distance) for single-label classification.
The squared EMD loss uses the predicted probabilities of all classes and
penalizes the miss-predictions according to a ground distance matrix that
quantifies the dissimilarities between classes. We demonstrate that on datasets
with strong inter-class relationships such as an ordering between classes, our
exact squared EMD losses yield new state-of-the-art results. Furthermore, we
propose a method to automatically learn this matrix using the CNN's own
features during training. We show that our method can learn a ground distance
matrix efficiently with no inter-class relationship priors and yield the same
performance gain. Finally, we show that our method can be generalized to
applications that lack strong inter-class relationships and still maintain
state-of-the-art performance. Therefore, with limited computational overhead,
one can always deploy the proposed loss function on any dataset over the
conventional cross-entropy.
Self-Paced Multi-Task Learning
Authors: Changsheng Li, Junchi Yan, Fan Wei, Weishan Dong, Qingshan Liu, Hongyuan Zha
Date: 2016-04-06 00:00:00
Tasks: Multi-Task Learning

In this paper, we propose a novel multi-task learning (MTL) framework, called
Self-Paced Multi-Task Learning (SPMTL). Different from previous works treating
all tasks and instances equally when training, SPMTL attempts to jointly learn
the tasks by taking into consideration the complexities of both tasks and
instances. This is inspired by the cognitive process of human brain that often
learns from the easy to the hard. We construct a compact SPMTL formulation by
proposing a new task-oriented regularizer that can jointly prioritize the tasks
and the instances. Thus it can be interpreted as a self-paced learner for MTL.
A simple yet effective algorithm is designed for optimizing the proposed
objective function. An error bound for a simplified formulation is also
analyzed theoretically. Experimental results on toy and real-world datasets
demonstrate the effectiveness of the proposed approach, compared to the
state-of-the-art methods.
Joint Active Learning with Feature Selection via CUR Matrix Decomposition
Authors: Changsheng Li, Xiangfeng Wang, Weishan Dong, Junchi Yan, Qingshan Liu, Hongyuan Zha
Date: 2015-03-04 00:00:00
Tasks: Active Learning, feature selection

This paper presents an unsupervised learning approach for simultaneous sample
and feature selection, which is in contrast to existing works which mainly
tackle these two problems separately. In fact the two tasks are often
interleaved with each other: noisy and high-dimensional features will bring
adverse effect on sample selection, while informative or representative samples
will be beneficial to feature selection. Specifically, we propose a framework
to jointly conduct active learning and feature selection based on the CUR
matrix decomposition. From the data reconstruction perspective, both the
selected samples and features can best approximate the original dataset
respectively, such that the selected samples characterized by the features are
highly representative. In particular, our method runs in one-shot without the
procedure of iterative sample selection for progressive labeling. Thus, our
model is especially suitable when there are few labeled samples or even in the
absence of supervision, which is a particular challenge for existing methods.
As the joint learning problem is NP-hard, the proposed formulation involves a
convex but non-smooth optimization problem. We solve it efficiently by an
iterative algorithm, and prove its global convergence. Experimental results on
publicly available datasets corroborate the efficacy of our method compared
with the state-of-the-art.
Diversity of preferences can increase collective welfare in sequential exploration problems
Authors: Pantelis P. Analytis, Hrvoje Stojic, Alexandros Gelastopoulos, Mehdi Moussaïd
Date: 2017-03-28 00:00:00
Tasks: Diversity

In search engines, online marketplaces and other human-computer interfaces
large collectives of individuals sequentially interact with numerous
alternatives of varying quality. In these contexts, trial and error
(exploration) is crucial for uncovering novel high-quality items or solutions,
but entails a high cost for individual users. Self-interested decision makers,
are often better off imitating the choices of individuals who have already
incurred the costs of exploration. Although imitation makes sense at the
individual level, it deprives the group of additional information that could
have been gleaned by individual explorers. In this paper we show that in such
problems, preference diversity can function as a welfare enhancing mechanism.
It leads to a consistent increase in the quality of the consumed alternatives
that outweighs the increased cost of search for the users.
Unsupervised Holistic Image Generation from Key Local Patches
Authors: Donghoon Lee, Sangdoo Yun, Sungjoon Choi, Hwiyeon Yoo, Ming-Hsuan Yang, Songhwai Oh
Date: 2017-03-31 00:00:00
Proceeding: ECCV 2018 9
Tasks: Decoder, Image Generation

We introduce a new problem of generating an image based on a small number of
key local patches without any geometric prior. In this work, key local patches
are defined as informative regions of the target object or scene. This is a
challenging problem since it requires generating realistic images and
predicting locations of parts at the same time. We construct adversarial
networks to tackle this problem. A generator network generates a fake image as
well as a mask based on the encoder-decoder framework. On the other hand, a
discriminator network aims to detect fake images. The network is trained with
three losses to consider spatial, appearance, and adversarial information. The
spatial loss determines whether the locations of predicted parts are correct.
Input patches are restored in the output image without much modification due to
the appearance loss. The adversarial loss ensures output images are realistic.
The proposed network is trained without supervisory signals since no labels of
key parts are required. Experimental results on six datasets demonstrate that
the proposed algorithm performs favorably on challenging objects and scenes.
On Self-Adaptive Mutation Restarts for Evolutionary Robotics with Real Rotorcraft
Authors: Gerard David Howard
Date: 2017-03-31 00:00:00

Self-adaptive parameters are increasingly used in the field of Evolutionary
Robotics, as they allow key evolutionary rates to vary autonomously in a
context-sensitive manner throughout the optimisation process. A significant
limitation to self-adaptive mutation is that rates can be set unfavourably,
which hinders convergence. Rate restarts are typically employed to remedy this,
but thus far have only been applied in Evolutionary Robotics for mutation-only
algorithms. This paper focuses on the level at which evolutionary rate restarts
are applied in population-based algorithms with more than 1 evolutionary
operator. After testing on a real hexacopter hovering task, we conclude that
individual-level restarting results in higher fitness solutions without fitness
stagnation, and population restarts provide a more stable rate evolution.
Without restarts, experiments can become stuck in suboptimal controller/rate
combinations which can be difficult to escape from.
Identifying networks with common organizational principles
Authors: Anatol E. Wegner, Luis Ospina-Forero, Robert E. Gaunt, Charlotte M. Deane, Gesine Reinert
Date: 2017-04-02 00:00:00
Tasks: General Classification

Many complex systems can be represented as networks, and the problem of
network comparison is becoming increasingly relevant. There are many techniques
for network comparison, from simply comparing network summary statistics to
sophisticated but computationally costly alignment-based approaches. Yet it
remains challenging to accurately cluster networks that are of a different size
and density, but hypothesized to be structurally similar. In this paper, we
address this problem by introducing a new network comparison methodology that
is aimed at identifying common organizational principles in networks. The
methodology is simple, intuitive and applicable in a wide variety of settings
ranging from the functional classification of proteins to tracking the
evolution of a world trade network.
Simple rules for complex decisions
Authors: Jongbin Jung, Connor Concannon, Ravi Shroff, Sharad Goel, Daniel G. Goldstein
Date: 2017-02-15 00:00:00
Tasks: Causal Inference, counterfactual, Decision Making

From doctors diagnosing patients to judges setting bail, experts often base
their decisions on experience and intuition rather than on statistical models.
While understandable, relying on intuition over models has often been found to
result in inferior outcomes. Here we present a new method,
select-regress-and-round, for constructing simple rules that perform well for
complex decisions. These rules take the form of a weighted checklist, can be
applied mentally, and nonetheless rival the performance of modern machine
learning algorithms. Our method for creating these rules is itself simple, and
can be carried out by practitioners with basic statistics knowledge. We
demonstrate this technique with a detailed case study of judicial decisions to
release or detain defendants while they await trial. In this application, as in
many policy settings, the effects of proposed decision rules cannot be directly
observed from historical data: if a rule recommends releasing a defendant that
the judge in reality detained, we do not observe what would have happened under
the proposed action. We address this key counterfactual estimation problem by
drawing on tools from causal inference. We find that simple rules significantly
outperform judges and are on par with decisions derived from random forests
trained on all available features. Generalizing to 22 varied decision-making
domains, we find this basic result replicates. We conclude with an analytical
framework that helps explain why these simple decision rules perform as well as
they do.
Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings
Authors: Junki Matsuo, Mamoru Komachi, Katsuhito Sudoh
Date: 2017-04-02 00:00:00
Tasks: Machine Translation, Translation, Word Alignment, Word Embeddings

One of the most important problems in machine translation (MT) evaluation is
to evaluate the similarity between translation hypotheses with different
surface forms from the reference, especially at the segment level. We propose
to use word embeddings to perform word alignment for segment-level MT
evaluation. We performed experiments with three types of alignment methods
using word embeddings. We evaluated our proposed methods with various
translation datasets. Experimental results show that our proposed methods
outperform previous word embeddings-based methods.
Understanding Concept Drift
Authors: Geoffrey I. Webb, Loong Kuan Lee, François Petitjean, Bart Goethals
Date: 2017-04-02 00:00:00
Tasks: BIG-bench Machine Learning

Concept drift is a major issue that greatly affects the accuracy and
reliability of many real-world applications of machine learning. We argue that
to tackle concept drift it is important to develop the capacity to describe and
analyze it. We propose tools for this purpose, arguing for the importance of
quantitative descriptions of drift in marginal distributions. We present
quantitative drift analysis techniques along with methods for communicating
their results. We demonstrate their effectiveness by application to three
real-world learning tasks.
Linear convergence of SDCA in statistical estimation
Authors: Chao Qu, Huan Xu
Date: 2017-01-26 00:00:00
Tasks: regression

In this paper, we consider stochastic dual coordinate (SDCA) {\em without}
strongly convex assumption or convex assumption. We show that SDCA converges
linearly under mild conditions termed restricted strong convexity. This covers
a wide array of popular statistical models including Lasso, group Lasso, and
logistic regression with $\ell_1$ regularization, corrected Lasso and linear
regression with SCAD regularizer. This significantly improves previous
convergence results on SDCA for problems that are not strongly convex. As a by
product, we derive a dual free form of SDCA that can handle general
regularization term, which is of interest by itself.
Dense Multi-view 3D-reconstruction Without Dense Correspondences
Authors: Yvain Quéau, Jean Mélou, Jean-Denis Durou, Daniel Cremers
Date: 2017-04-02 00:00:00
Tasks: 3D Reconstruction, Multi-View 3D Reconstruction

We introduce a variational method for multi-view shape-from-shading under
natural illumination. The key idea is to couple PDE-based solutions for
single-image based shape-from-shading problems across multiple images and
multiple color channels by means of a variational formulation. Rather than
alternatingly solving the individual SFS problems and optimizing the
consistency across images and channels which is known to lead to suboptimal
results, we propose an efficient solution of the coupled problem by means of an
ADMM algorithm. In numerous experiments on both simulated and real imagery, we
demonstrate that the proposed fusion of multiple-view reconstruction and
shape-from-shading provides highly accurate dense reconstructions without the
need to compute dense correspondences. With the proposed variational
integration across multiple views shape-from-shading techniques become
applicable to challenging real-world reconstruction problems, giving rise to
highly detailed geometry even in areas of smooth brightness variation and
lacking texture.
Restoration of Images with Wavefront Aberrations
Authors: Claudius Zelenka, Reinhard Koch
Date: 2017-04-02 00:00:00
Tasks: Astronomy, Image Restoration

This contribution deals with image restoration in optical systems with
coherent illumination, which is an important topic in astronomy, coherent
microscopy and radar imaging. Such optical systems suffer from wavefront
distortions, which are caused by imperfect imaging components and conditions.
Known image restoration algorithms work well for incoherent imaging, they fail
in case of coherent images. In this paper a novel wavefront correction
algorithm is presented, which allows image restoration under coherent
conditions. In most coherent imaging systems, especially in astronomy, the
wavefront deformation is known. Using this information, the proposed algorithm
allows a high quality restoration even in case of severe wavefront distortions.
We present two versions of this algorithm, which are an evolution of the
Gerchberg-Saxton and the Hybrid-Input-Output algorithm. The algorithm is
verified on simulated and real microscopic images.
VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem
Authors: Ronald Clark, Sen Wang, Hongkai Wen, Andrew Markham, Niki Trigoni
Date: 2017-01-29 00:00:00
Tasks: Motion Estimation

In this paper we present an on-manifold sequence-to-sequence learning
approach to motion estimation using visual and inertial sensors. It is to the
best of our knowledge the first end-to-end trainable method for visual-inertial
odometry which performs fusion of the data at an intermediate
feature-representation level. Our method has numerous advantages over
traditional approaches. Specifically, it eliminates the need for tedious manual
synchronization of the camera and IMU as well as eliminating the need for
manual calibration between the IMU and camera. A further advantage is that our
model naturally and elegantly incorporates domain specific information which
significantly mitigates drift. We show that our approach is competitive with
state-of-the-art traditional methods when accurate calibration data is
available and can be trained to outperform them in the presence of calibration
and synchronization errors.