pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
Authors: Kuan Fang, Yunfei Bai, Stefan Hinterstoisser, Silvio Savarese, Mrinal Kalakrishnan
Date: 2017-10-17 00:00:00
Tasks: Domain Adaptation, Instance Segmentation, Semantic Segmentation, Transfer Learning

Learning-based approaches to robotic manipulation are limited by the
scalability of data collection and accessibility of labels. In this paper, we
present a multi-task domain adaptation framework for instance grasping in
cluttered scenes by utilizing simulated robot experiments. Our neural network
takes monocular RGB images and the instance segmentation mask of a specified
target object as inputs, and predicts the probability of successfully grasping
the specified object for each candidate motor command. The proposed transfer
learning framework trains a model for instance grasping in simulation and uses
a domain-adversarial loss to transfer the trained model to real robots using
indiscriminate grasping data, which is available both in simulation and the
real world. We evaluate our model in real-world robot experiments, comparing it
with alternative model architectures as well as an indiscriminate grasping
baseline.
Discovering Underlying Plans Based on Shallow Models
Authors: Hankz Hankui Zhuo, Yantian Zha, Subbarao Kambhampati
Date: 2018-03-04 00:00:00

Plan recognition aims to discover target plans (i.e., sequences of actions)
behind observed actions, with history plan libraries or domain models in hand.
Previous approaches either discover plans by maximally "matching" observed
actions to plan libraries, assuming target plans are from plan libraries, or
infer plans by executing domain models to best explain the observed actions,
assuming that complete domain models are available. In real world applications,
however, target plans are often not from plan libraries, and complete domain
models are often not available, since building complete sets of plans and
complete domain models are often difficult or expensive. In this paper we view
plan libraries as corpora and learn vector representations of actions using the
corpora, we then discover target plans based on the vector representations.
Specifically, we propose two approaches, DUP and RNNPlanner, to discover target
plans based on vector representations of actions. DUP explores the EM-style
framework to capture local contexts of actions and discover target plans by
optimizing the probability of target plans, while RNNPlanner aims to leverage
long-short term contexts of actions based on RNNs (recurrent neural networks)
framework to help recognize target plans. In the experiments, we empirically
show that our approaches are capable of discovering underlying plans that are
not from plan libraries, without requiring domain models provided. We
demonstrate the effectiveness of our approaches by comparing its performance to
traditional plan recognition approaches in three planning domains. We also
compare DUP and RNNPlanner to see their advantages and disadvantages.
Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
Authors: Payam Karisani, Eugene Agichtein
Date: 2018-02-26 00:00:00
Tasks: Epidemiology, Semi-Supervised Text Classification, Text Classification

Millions of users share their experiences on social media sites, such as
Twitter, which in turn generate valuable data for public health monitoring,
digital epidemiology, and other analyses of population health at global scale.
The first, critical, task for these applications is classifying whether a
personal health event was mentioned, which we call the (PHM) problem. This task
is challenging for many reasons, including typically short length of social
media posts, inventive spelling and lexicons, and figurative language,
including hyperbole using diseases like "heart attack" or "cancer" for
emphasis, and not as a health self-report. This problem is even more
challenging for rarely reported, or frequent but ambiguously expressed
conditions, such as "stroke". To address this problem, we propose a general,
robust method for detecting PHMs in social media, which we call WESPAD, that
combines lexical, syntactic, word embedding-based, and context-based features.
WESPAD is able to generalize from few examples by automatically distorting the
word embedding space to most effectively detect the true health mentions.
Unlike previously proposed state-of-the-art supervised and deep-learning
techniques, WESPAD requires relatively little training data, which makes it
possible to adapt, with minimal effort, to each new disease and condition. We
evaluate WESPAD on both an established publicly available Flu detection
benchmark, and on a new dataset that we have constructed with mentions of
multiple health conditions. Our experiments show that WESPAD outperforms the
baselines and state-of-the-art methods, especially in cases when the number and
proportion of true health mentions in the training data is small.
Unsupervised Learning of Face Representations
Authors: Samyak Datta, Gaurav Sharma, C. V. Jawahar
Date: 2018-03-03 00:00:00

We present an approach for unsupervised training of CNNs in order to learn
discriminative face representations. We mine supervised training data by noting
that multiple faces in the same video frame must belong to different persons
and the same face tracked across multiple frames must belong to the same
person. We obtain millions of face pairs from hundreds of videos without using
any manual supervision. Although faces extracted from videos have a lower
spatial resolution than those which are available as part of standard
supervised face datasets such as LFW and CASIA-WebFace, the former represent a
much more realistic setting, e.g. in surveillance scenarios where most of the
faces detected are very small. We train our CNNs with the relatively low
resolution faces extracted from video frames collected, and achieve a higher
verification accuracy on the benchmark LFW dataset cf. hand-crafted features
such as LBPs, and even surpasses the performance of state-of-the-art deep
networks such as VGG-Face, when they are made to work with low resolution input
images.
Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications
Authors: Xiao Fu, Kejun Huang, Nicholas D. Sidiropoulos, Wing-Kin Ma
Date: 2018-03-03 00:00:00

Nonnegative matrix factorization (NMF) has become a workhorse for signal and
data analytics, triggered by its model parsimony and interpretability. Perhaps
a bit surprisingly, the understanding to its model identifiability---the major
reason behind the interpretability in many applications such as topic mining
and hyperspectral imaging---had been rather limited until recent years.
Beginning from the 2010s, the identifiability research of NMF has progressed
considerably: Many interesting and important results have been discovered by
the signal processing (SP) and machine learning (ML) communities. NMF
identifiability has a great impact on many aspects in practice, such as
ill-posed formulation avoidance and performance-guaranteed algorithm design. On
the other hand, there is no tutorial paper that introduces NMF from an
identifiability viewpoint. In this paper, we aim at filling this gap by
offering a comprehensive and deep tutorial on model identifiability of NMF as
well as the connections to algorithms and applications. This tutorial will help
researchers and graduate students grasp the essence and insights of NMF,
thereby avoiding typical `pitfalls' that are often times due to unidentifiable
NMF formulations. This paper will also help practitioners pick/design suitable
factorization tools for their own problems.
Understanding and Improving Multi-Sense Word Embeddings via Extended Robust Principal Component Analysis
Authors: Haoyue Shi, Yuqi Sun, Junfeng Hu
Date: 2018-03-03 00:00:00
Tasks: Dimensionality Reduction, Word Embeddings, Word Similarity

Unsupervised learned representations of polysemous words generate a large of
pseudo multi senses since unsupervised methods are overly sensitive to
contextual variations. In this paper, we address the pseudo multi-sense
detection for word embeddings by dimensionality reduction of sense pairs. We
propose a novel principal analysis method, termed Ex-RPCA, designed to detect
both pseudo multi senses and real multi senses. With Ex-RPCA, we empirically
show that pseudo multi senses are generated systematically in unsupervised
method. Moreover, the multi-sense word embeddings can by improved by a simple
linear transformation based on Ex-RPCA. Our improved word embedding outperform
the original one by 5.6 points on Stanford contextual word similarity (SCWS)
dataset. We hope our simple yet effective approach will help the linguistic
analysis of multi-sense word embeddings in the future.
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
Authors: Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, Zhenhui Li
Date: 2018-03-03 00:00:00
Tasks: Traffic Prediction

Traffic prediction has drawn increasing attention in AI research field due to
the increasing availability of large-scale traffic data and its importance in
the real world. For example, an accurate taxi demand prediction can assist taxi
companies in pre-allocating taxis. The key challenge of traffic prediction lies
in how to model the complex spatial dependencies and temporal dynamics.
Although both factors have been considered in modeling, existing works make
strong assumptions about spatial dependence and temporal dynamics, i.e.,
spatial dependence is stationary in time, and temporal dynamics is strictly
periodical. However, in practice, the spatial dependence could be dynamic
(i.e., changing from time to time), and the temporal dynamics could have some
perturbation from one period to another period. In this paper, we make two
important observations: (1) the spatial dependencies between locations are
dynamic; and (2) the temporal dependency follows daily and weekly pattern but
it is not strictly periodic for its dynamic temporal shifting. To address these
two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in
which a flow gating mechanism is introduced to learn the dynamic similarity
between locations, and a periodically shifted attention mechanism is designed
to handle long-term periodic temporal shifting. To the best of our knowledge,
this is the first work that tackles both issues in a unified framework. Our
experimental results on real-world traffic datasets verify the effectiveness of
the proposed method.
A Swift Heuristic Method for Work Order Scheduling under the Skilled-Workforce Constraint
Authors: Nima Safaei, Corey Kiassat
Date: 2018-03-03 00:00:00
Tasks: Scheduling

The considered problem is how to optimally allocate a set of jobs to
technicians of different skills such that the number of technicians of each
skill does not exceed the number of persons with that skill designation. The
key motivation is the quick sensitivity analysis in terms of the workforce size
which is quite necessary in many industries in the presence of unexpected work
orders. A time-indexed mathematical model is proposed to minimize the total
weighted completion time of the jobs. The proposed model is decomposed into a
number of single-skill sub-problems so that each one is a combination of a
series of nested binary Knapsack problems. A heuristic procedure is proposed to
solve the problem. Our experimental results, based on a real-world case study,
reveal that the proposed method quickly produces a schedule statistically close
to the optimal one while the classical optimal procedure is very
time-consuming.
Distributed Robust Subspace Recovery
Authors: Vahan Huroyan, Gilad Lerman
Date: 2017-05-25 00:00:00

We propose distributed solutions to the problem of Robust Subspace Recovery
(RSR). Our setting assumes a huge dataset in an ad hoc network without a
central processor, where each node has access only to one chunk of the dataset.
Furthermore, part of the whole dataset lies around a low-dimensional subspace
and the other part is composed of outliers that lie away from that subspace.
The goal is to recover the underlying subspace for the whole dataset, without
transferring the data itself between the nodes. We first apply the
Consensus-Based Gradient method to the Geometric Median Subspace algorithm for
RSR. For this purpose, we propose an iterative solution for the local dual
minimization problem and establish its r-linear convergence. We then explain
how to distributedly implement the Reaper and Fast Median Subspace algorithms
for RSR. The proposed algorithms display competitive performance on both
synthetic and real data.
An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits
Authors: Isaac J. Sledge, Jose C. Principe
Date: 2017-10-08 00:00:00
Tasks: Multi-Armed Bandits

In this paper, we propose an information-theoretic exploration strategy for
stochastic, discrete multi-armed bandits that achieves optimal regret. Our
strategy is based on the value of information criterion. This criterion
measures the trade-off between policy information and obtainable rewards. High
amounts of policy information are associated with exploration-dominant searches
of the space and yield high rewards. Low amounts of policy information favor
the exploitation of existing knowledge. Information, in this criterion, is
quantified by a parameter that can be varied during search. We demonstrate that
a simulated-annealing-like update of this parameter, with a sufficiently fast
cooling schedule, leads to an optimal regret that is logarithmic with respect
to the number of episodes.
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow
Authors: Xiao Zhang, Simon S. Du, Quanquan Gu
Date: 2018-03-03 00:00:00
Proceeding: ICML 2018 7
Tasks: Matrix Completion

We revisit the inductive matrix completion problem that aims to recover a
rank-$r$ matrix with ambient dimension $d$ given $n$ features as the side prior
information. The goal is to make use of the known $n$ features to reduce sample
and computational complexities. We present and analyze a new gradient-based
non-convex optimization algorithm that converges to the true underlying matrix
at a linear rate with sample complexity only linearly depending on $n$ and
logarithmically depending on $d$. To the best of our knowledge, all previous
algorithms either have a quadratic dependency on the number of features in
sample complexity or a sub-linear computational convergence rate. In addition,
we provide experiments on both synthetic and real world data to demonstrate the
effectiveness of our proposed algorithm.
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
Authors: Maayan Frid-Adar, Idit Diamant, Eyal Klang, Michal Amitai, Jacob Goldberger, Hayit Greenspan
Date: 2018-03-03 00:00:00
Tasks: Classification, Computed Tomography (CT), Data Augmentation, General Classification, Image Augmentation, image-classification, Image Classification, Lesion Classification, Medical Image Classification, Sensitivity, Specificity

Deep learning methods, and in particular convolutional neural networks
(CNNs), have led to an enormous breakthrough in a wide range of computer vision
tasks, primarily by using large-scale annotated datasets. However, obtaining
such datasets in the medical domain remains a challenge. In this paper, we
present methods for generating synthetic medical images using recently
presented deep learning Generative Adversarial Networks (GANs). Furthermore, we
show that generated medical images can be used for synthetic data augmentation,
and improve the performance of CNN for medical image classification. Our novel
method is demonstrated on a limited dataset of computed tomography (CT) images
of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). We first
exploit GAN architectures for synthesizing high quality liver lesion ROIs. Then
we present a novel scheme for liver lesion classification using CNN. Finally,
we train the CNN using classic data augmentation and our synthetic data
augmentation and compare performance. In addition, we explore the quality of
our synthesized examples using visualization and expert assessment. The
classification performance using only classic data augmentation yielded 78.6%
sensitivity and 88.4% specificity. By adding the synthetic data augmentation
the results increased to 85.7% sensitivity and 92.4% specificity. We believe
that this approach to synthetic data augmentation can generalize to other
medical classification applications and thus support radiologists' efforts to
improve diagnosis.
Deep Bayesian Active Semi-Supervised Learning
Authors: Matthias Rottmann, Karsten Kahl, Hanno Gottschalk
Date: 2018-03-03 00:00:00
Tasks: Active Learning, Data Augmentation

In many applications the process of generating label information is expensive
and time consuming. We present a new method that combines active and
semi-supervised deep learning to achieve high generalization performance from a
deep convolutional neural network with as few known labels as possible. In a
setting where a small amount of labeled data as well as a large amount of
unlabeled data is available, our method first learns the labeled data set. This
initialization is followed by an expectation maximization algorithm, where
further training reduces classification entropy on the unlabeled data by
targeting a low entropy fit which is consistent with the labeled data. In
addition the algorithm asks at a specified frequency an oracle for labels of
data with entropy above a certain entropy quantile. Using this active learning
component we obtain an agile labeling process that achieves high accuracy, but
requires only a small amount of known labels. For the MNIST dataset we report
an error rate of 2.06% using only 300 labels and 1.06% for 1000 labels. These
results are obtained without employing any special network architecture or data
augmentation.
WESPE: Weakly Supervised Photo Enhancer for Digital Cameras
Authors: Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, Luc van Gool
Date: 2017-09-04 00:00:00
Tasks: Generative Adversarial Network

Low-end and compact mobile cameras demonstrate limited photo quality mainly
due to space, hardware and budget constraints. In this work, we propose a deep
learning solution that translates photos taken by cameras with limited
capabilities into DSLR-quality photos automatically. We tackle this problem by
introducing a weakly supervised photo enhancer (WESPE) - a novel image-to-image
Generative Adversarial Network-based architecture. The proposed model is
trained by under weak supervision: unlike previous works, there is no need for
strong supervision in the form of a large annotated dataset of aligned
original/enhanced photo pairs. The sole requirement is two distinct datasets:
one from the source camera, and one composed of arbitrary high-quality images
that can be generally crawled from the Internet - the visual content they
exhibit may be unrelated. Hence, our solution is repeatable for any camera:
collecting the data and training can be achieved in a couple of hours. In this
work, we emphasize on extensive evaluation of obtained results. Besides
standard objective metrics and subjective user study, we train a virtual rater
in the form of a separate CNN that mimics human raters on Flickr data and use
this network to get reference scores for both original and enhanced photos. Our
experiments on the DPED, KITTI and Cityscapes datasets as well as pictures from
several generations of smartphones demonstrate that WESPE produces comparable
or improved qualitative results with state-of-the-art strongly supervised
methods.
Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning
Authors: Alexey Shvets, Alexander Rakhlin, Alexandr A. Kalinin, Vladimir Iglovikov
Date: 2018-03-03 00:00:00
Tasks: Deep Learning, Pose Estimation, Segmentation, Semantic Segmentation

Semantic segmentation of robotic instruments is an important problem for the
robot-assisted surgery. One of the main challenges is to correctly detect an
instrument's position for the tracking and pose estimation in the vicinity of
surgical scenes. Accurate pixel-wise instrument segmentation is needed to
address this challenge. In this paper we describe our winning solution for
MICCAI 2017 Endoscopic Vision SubChallenge: Robotic Instrument Segmentation.
Our approach demonstrates an improvement over the state-of-the-art results
using several novel deep neural network architectures. It addressed the binary
segmentation problem, where every pixel in an image is labeled as an instrument
or background from the surgery video feed. In addition, we solve a multi-class
segmentation problem, where we distinguish different instruments or different
parts of an instrument from the background. In this setting, our approach
outperforms other methods in every task subcategory for automatic instrument
segmentation thereby providing state-of-the-art solution for this problem. The
source code for our solution is made publicly available at
https://github.com/ternaus/robot-surgery-segmentation
Multiresolution Tensor Decomposition for Multiple Spatial Passing Networks
Authors: Shaobo Han, David B. Dunson
Date: 2018-03-03 00:00:00
Tasks: Tensor Decomposition

This article is motivated by soccer positional passing networks collected
across multiple games. We refer to these data as replicated spatial passing
networks---to accurately model such data it is necessary to take into account
the spatial positions of the passer and receiver for each passing event. This
spatial registration and replicates that occur across games represent key
differences with usual social network data. As a key step before investigating
how the passing dynamics influence team performance, we focus on developing
methods for summarizing different team's passing strategies. Our proposed
approach relies on a novel multiresolution data representation framework and
Poisson nonnegative block term decomposition model, which automatically
produces coarse-to-fine low-rank network motifs. The proposed methods are
applied to detailed passing record data collected from the 2014 FIFA World Cup.
Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation
Authors: Sergii Stirenko, Yuriy Kochura, Oleg Alienin, Oleksandr Rokovyi, Peng Gang, Wei Zeng, Yuri Gordienko
Date: 2018-03-03 00:00:00
Tasks: Data Augmentation, Segmentation

The results of chest X-ray (CXR) analysis of 2D images to get the
statistically reliable predictions (availability of tuberculosis) by
computer-aided diagnosis (CADx) on the basis of deep learning are presented.
They demonstrate the efficiency of lung segmentation, lossless and lossy data
augmentation for CADx of tuberculosis by deep convolutional neural network
(CNN) applied to the small and not well-balanced dataset even. CNN demonstrates
ability to train (despite overfitting) on the pre-processed dataset obtained
after lung segmentation in contrast to the original not-segmented dataset.
Lossless data augmentation of the segmented dataset leads to the lowest
validation loss (without overfitting) and nearly the same accuracy (within the
limits of standard deviation) in comparison to the original and other
pre-processed datasets after lossy data augmentation. The additional limited
lossy data augmentation results in the lower validation loss, but with a
decrease of the validation accuracy. In conclusion, besides the more complex
deep CNNs and bigger datasets, the better progress of CADx for the small and
not well-balanced datasets even could be obtained by better segmentation, data
augmentation, dataset stratification, and exclusion of non-evident outliers.