pwc-archive/papers-with-abstracts huggingface dataset:
Deriving Verb Predicates By Clustering Verbs with Arguments
Authors: Joao Sedoc, Derry Wijaya, Masoud Rouhizadeh, Andy Schwartz, Lyle Ungar
Date: 2017-08-01 00:00:00
Tasks: Clustering

Hand-built verb clusters such as the widely used Levin classes (Levin, 1993)
have proved useful, but have limited coverage. Verb classes automatically
induced from corpus data such as those from VerbKB (Wijaya, 2016), on the other
hand, can give clusters with much larger coverage, and can be adapted to
specific corpora such as Twitter. We present a method for clustering the
outputs of VerbKB: verbs with their multiple argument types, e.g.
"marry(person, person)", "feel(person, emotion)." We make use of a novel
low-dimensional embedding of verbs and their arguments to produce high quality
clusters in which the same verb can be in different clusters depending on its
argument type. The resulting verb clusters do a better job than hand-built
clusters of predicting sarcasm, sentiment, and locus of control in tweets.
Classifying Symmetrical Differences and Temporal Change in Mammography Using Deep Neural Networks
Authors: Thijs Kooi, Nico Karssemeijer
Date: 2017-03-22 00:00:00

We investigate the addition of symmetry and temporal context information to a
deep Convolutional Neural Network (CNN) with the purpose of detecting malignant
soft tissue lesions in mammography. We employ a simple linear mapping that
takes the location of a mass candidate and maps it to either the contra-lateral
or prior mammogram and Regions Of Interest (ROI) are extracted around each
location. We subsequently explore two different architectures (1) a fusion
model employing two datastreams were both ROIs are fed to the network during
training and testing and (2) a stage-wise approach where a single ROI CNN is
trained on the primary image and subsequently used as feature extractor for
both primary and symmetrical or prior ROIs. A 'shallow' Gradient Boosted Tree
(GBT) classifier is then trained on the concatenation of these features and
used to classify the joint representation. Results shown a significant increase
in performance using the first architecture and symmetry information, but only
marginal gains in performance using temporal data and the other setting. We
feel results are promising and can greatly be improved when more temporal data
becomes available.
Vision-Based Assessment of Parkinsonism and Levodopa-Induced Dyskinesia with Deep Learning Pose Estimation
Authors: Michael H. Li, Tiago A. Mestre, Susan H. Fox, Babak Taati
Date: 2017-07-25 00:00:00
Tasks: Deep Learning, Pose Estimation

Objective: To apply deep learning pose estimation algorithms for vision-based
assessment of parkinsonism and levodopa-induced dyskinesia (LID). Methods: Nine
participants with Parkinson's disease (PD) and LID completed a levodopa
infusion protocol, where symptoms were assessed at regular intervals using the
Unified Dyskinesia Rating Scale (UDysRS) and Unified Parkinson's Disease Rating
Scale (UPDRS). A state-of-the-art deep learning pose estimation method was used
to extract movement trajectories from videos of PD assessments. Features of the
movement trajectories were used to detect and estimate the severity of
parkinsonism and LID using random forest. Communication and drinking tasks were
used to assess LID, while leg agility and toe tapping tasks were used to assess
parkinsonism. Feature sets from tasks were also combined to predict total
UDysRS and UPDRS Part III scores. Results: For LID, the communication task
yielded the best results for dyskinesia (severity estimation: r = 0.661,
detection: AUC = 0.930). For parkinsonism, leg agility had better results for
severity estimation (r = 0.618), while toe tapping was better for detection
(AUC = 0.773). UDysRS and UPDRS Part III scores were predicted with r = 0.741
and 0.530, respectively. Conclusion: This paper presents the first application
of deep learning for vision-based assessment of parkinsonism and LID and
demonstrates promising performance for the future translation of deep learning
to PD clinical practices. Significance: The proposed system provides insight
into the potential of computer vision and deep learning for clinical
application in PD.
Momo: Monocular Motion Estimation on Manifolds
Authors: Johannes Graeter, Tobias Strauss, Martin Lauer
Date: 2017-08-01 00:00:00
Tasks: Autonomous Driving, Motion Estimation, Visual Odometry

Knowledge about the location of a vehicle is indispensable for autonomous
driving. In order to apply global localisation methods, a pose prior must be
known which can be obtained from visual odometry. The quality and robustness of
that prior determine the success of localisation. Momo is a monocular
frame-to-frame motion estimation methodology providing a high quality visual
odometry for that purpose. By taking into account the motion model of the
vehicle, reliability and accuracy of the pose prior are significantly improved.
We show that especially in low-structure environments Momo outperforms the
state of the art. Moreover, the method is designed so that multiple cameras
with or without overlap can be integrated. The evaluation on the KITTI-dataset
and on a proper multi-camera dataset shows that even with only 100--300 feature
matches the prior is estimated with high accuracy and in real-time.
A Continuously Growing Dataset of Sentential Paraphrases
Authors: Wuwei Lan, Siyu Qiu, Hua He, Wei Xu
Date: 2017-08-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Benchmarking, Paraphrase Identification, Sentence

A major challenge in paraphrase research is the lack of parallel corpora. In
this paper, we present a new method to collect large-scale sentential
paraphrases from Twitter by linking tweets through shared URLs. The main
advantage of our method is its simplicity, as it gets rid of the classifier or
human in the loop needed to select data before annotation and subsequent
application of paraphrase identification algorithms in the previous work. We
present the largest human-labeled paraphrase corpus to date of 51,524 sentence
pairs and the first cross-domain benchmarking for automatic paraphrase
identification. In addition, we show that more than 30,000 new sentential
paraphrases can be easily and continuously captured every month at ~70%
precision, and demonstrate their utility for downstream NLP tasks through
phrasal paraphrase extraction. We make our code and data freely available.
Online Adaptation of Convolutional Neural Networks for Video Object Segmentation
Authors: Paul Voigtlaender, Bastian Leibe
Date: 2017-06-28 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation, Visual Object Tracking

We tackle the task of semi-supervised video object segmentation, i.e.
segmenting the pixels belonging to an object in the video using the ground
truth pixel mask for the first frame. We build on the recently introduced
one-shot video object segmentation (OSVOS) approach which uses a pretrained
network and fine-tunes it on the first frame. While achieving impressive
performance, at test time OSVOS uses the fine-tuned network in unchanged form
and is not able to adapt to large changes in object appearance. To overcome
this limitation, we propose Online Adaptive Video Object Segmentation (OnAVOS)
which updates the network online using training examples selected based on the
confidence of the network and the spatial configuration. Additionally, we add a
pretraining step based on objectness, which is learned on PASCAL. Our
experiments show that both extensions are highly effective and improve the
state of the art on DAVIS to an intersection-over-union score of 85.7%.
Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network
Authors: Saddam Hussain, Syed Muhammad Anwar, Muhammad Majid
Date: 2017-08-01 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Tumor Segmentation

Detection of brain tumor using a segmentation based approach is critical in
cases, where survival of a subject depends on an accurate and timely clinical
diagnosis. Gliomas are the most commonly found tumors having irregular shape
and ambiguous boundaries, making them one of the hardest tumors to detect. The
automation of brain tumor segmentation remains a challenging problem mainly due
to significant variations in its structure. An automated brain tumor
segmentation algorithm using deep convolutional neural network (DCNN) is
presented in this paper. A patch based approach along with an inception module
is used for training the deep network by extracting two co-centric patches of
different sizes from the input images. Recent developments in deep neural
networks such as drop-out, batch normalization, non-linear activation and
inception module are used to build a new ILinear nexus architecture. The module
overcomes the over-fitting problem arising due to scarcity of data using
drop-out regularizer. Images are normalized and bias field corrected in the
pre-processing step and then extracted patches are passed through a DCNN, which
assigns an output label to the central pixel of each patch. Morphological
operators are used for post-processing to remove small false positives around
the edges. A two-phase weighted training method is introduced and evaluated
using BRATS 2013 and BRATS 2015 datasets, where it improves the performance
parameters of state-of-the-art techniques under similar settings.
Convergence of the Forward-Backward Algorithm: Beyond the Worst Case with the Help of Geometry
Authors: Guillaume Garrigos, Lorenzo Rosasco, Silvia Villa
Date: 2017-03-28 00:00:00

We provide a comprehensive study of the convergence of the forward-backward algorithm under suitable geometric conditions, such as conditioning or {\L}ojasiewicz properties. These geometrical notions are usually local by nature, and may fail to describe the fine geometry of objective functions relevant in inverse problems and signal processing, that have a nice behaviour on manifolds, or sets open with respect to a weak topology. Motivated by this observation, we revisit those geometric notions over arbitrary sets. In turn, this allows us to present several new results as well as collect in a unified view a variety of results scattered in the literature. Our contributions include the analysis of infinite dimensional convex minimization problems, showing the first {\L}ojasiewicz inequality for a quadratic function associated to a compact operator, and the derivation of new linear rates for problems arising from inverse problems with low-complexity priors. Our approach allows to establish unexpected connections between geometry and a priori conditions in inverse problems, such as source conditions, or restricted isometry properties.
Self-Supervised Learning for Spinal MRIs
Authors: Amir Jamaludin, Timor Kadir, Andrew Zisserman
Date: 2017-08-01 00:00:00
Tasks: Classification, General Classification, Self-Supervised Learning

A significant proportion of patients scanned in a clinical setting have
follow-up scans. We show in this work that such longitudinal scans alone can be
used as a form of 'free' self-supervision for training a deep network. We
demonstrate this self-supervised learning for the case of T2-weighted sagittal
lumbar Magnetic Resonance Images (MRIs). A Siamese convolutional neural network
(CNN) is trained using two losses: (i) a contrastive loss on whether the scan
is of the same person (i.e. longitudinal) or not, together with (ii) a
classification loss on predicting the level of vertebral bodies. The
performance of this pre-trained network is then assessed on a grading
classification task. We experiment on a dataset of 1016 subjects, 423
possessing follow-up scans, with the end goal of learning the disc degeneration
radiological gradings attached to the intervertebral discs. We show that the
performance of the pre-trained CNN on the supervised classification task is (i)
superior to that of a network trained from scratch; and (ii) requires far fewer
annotated training samples to reach an equivalent performance to that of the
network trained from scratch.
Learning the kernel matrix by resampling
Authors: Xiao-Lei Zhang
Date: 2017-08-01 00:00:00
Tasks: Clustering

In this abstract paper, we introduce a new kernel learning method by a
nonparametric density estimator. The estimator consists of a group of
k-centroids clusterings. Each clustering randomly selects data points with
randomly selected features as its centroids, and learns a one-hot encoder by
one-nearest-neighbor optimization. The estimator generates a sparse
representation for each data point. Then, we construct a nonlinear kernel
matrix from the sparse representation of data. One major advantage of the
proposed kernel method is that it is relatively insensitive to its free
parameters, and therefore, it can produce reasonable results without parameter
tuning. Another advantage is that it is simple. We conjecture that the proposed
method can find its applications in many learning tasks or methods where sparse
representation or kernel matrix is explored. In this preliminary study, we have
applied the kernel matrix to spectral clustering. Our experimental results
demonstrate that the kernel generated by the proposed method outperforms the
well-tuned Gaussian RBF kernel. This abstract paper is used to protect the
idea, full versions will be updated later.
Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning
Authors: Andrew P. Norton, Yanjun Qi
Date: 2017-08-01 00:00:00
Tasks: Adversarial Attack, Adversarial Defense, Deep Learning, image-classification, Image Classification

Recent studies have shown that attackers can force deep learning models to
misclassify so-called "adversarial examples": maliciously generated images
formed by making imperceptible modifications to pixel values. With growing
interest in deep learning for security applications, it is important for
security experts and users of machine learning to recognize how learning
systems may be attacked. Due to the complex nature of deep learning, it is
challenging to understand how deep models can be fooled by adversarial
examples. Thus, we present a web-based visualization tool,
Adversarial-Playground, to demonstrate the efficacy of common adversarial
methods against a convolutional neural network (CNN) system.
Adversarial-Playground is educational, modular and interactive. (1) It enables
non-experts to compare examples visually and to understand why an adversarial
example can fool a CNN-based image classifier. (2) It can help security experts
explore more vulnerability of deep learning as a software module. (3) Building
an interactive visualization is challenging in this domain due to the large
feature space of image classification (generating adversarial examples is slow
in general and visualizing images are costly). Through multiple novel design
choices, our tool can provide fast and accurate responses to user requests.
Empirically, we find that our client-server division strategy reduced the
response time by an average of 1.5 seconds per sample. Our other innovation, a
faster variant of JSMA evasion algorithm, empirically performed twice as fast
as JSMA and yet maintains a comparable evasion rate.
  Project source code and data from our experiments available at:
https://github.com/QData/AdversarialDNN-Playground
Exact Approaches for the Travelling Thief Problem
Authors: Junhua Wu, Markus Wagner, Sergey Polyakovskiy, Frank Neumann
Date: 2017-08-01 00:00:00

Many evolutionary and constructive heuristic approaches have been introduced
in order to solve the Traveling Thief Problem (TTP). However, the accuracy of
such approaches is unknown due to their inability to find global optima. In
this paper, we propose three exact algorithms and a hybrid approach to the TTP.
We compare these with state-of-the-art approaches to gather a comprehensive
overview on the accuracy of heuristic methods for solving small TTP instances.
Generative Semantic Manipulation with Contrasting GAN
Authors: Xiaodan Liang, Hao Zhang, Eric P. Xing
Date: 2017-08-01 00:00:00
Tasks: Image-to-Image Translation, Style Transfer

Generative Adversarial Networks (GANs) have recently achieved significant
improvement on paired/unpaired image-to-image translation, such as
photo$\rightarrow$ sketch and artist painting style transfer. However, existing
models can only be capable of transferring the low-level information (e.g.
color or texture changes), but fail to edit high-level semantic meanings (e.g.,
geometric structure or content) of objects. On the other hand, while some
researches can synthesize compelling real-world images given a class label or
caption, they cannot condition on arbitrary shapes or structures, which largely
limits their application scenarios and interpretive capability of model
results. In this work, we focus on a more challenging semantic manipulation
task, which aims to modify the semantic meaning of an object while preserving
its own characteristics (e.g. viewpoints and shapes), such as
cow$\rightarrow$sheep, motor$\rightarrow$ bicycle, cat$\rightarrow$dog. To
tackle such large semantic changes, we introduce a contrasting GAN
(contrast-GAN) with a novel adversarial contrasting objective. Instead of
directly making the synthesized samples close to target data as previous GANs
did, our adversarial contrasting objective optimizes over the distance
comparisons between samples, that is, enforcing the manipulated data be
semantically closer to the real data with target category than the input data.
Equipped with the new contrasting objective, a novel mask-conditional
contrast-GAN architecture is proposed to enable disentangle image background
with object semantic changes. Experiments on several semantic manipulation
tasks on ImageNet and MSCOCO dataset show considerable performance gain by our
contrast-GAN over other conditional GANs. Quantitative results further
demonstrate the superiority of our model on generating manipulated results with
high visual fidelity and reasonable object semantics.
SenGen: Sentence Generating Neural Variational Topic Model
Authors: Ramesh Nallapati, Igor Melnyk, Abhishek Kumar, Bo-Wen Zhou
Date: 2017-08-01 00:00:00
Tasks: Decoder, model, Sentence

We present a new topic model that generates documents by sampling a topic for
one whole sentence at a time, and generating the words in the sentence using an
RNN decoder that is conditioned on the topic of the sentence. We argue that
this novel formalism will help us not only visualize and model the topical
discourse structure in a document better, but also potentially lead to more
interpretable topics since we can now illustrate topics by sampling
representative sentences instead of bag of words or phrases. We present a
variational auto-encoder approach for learning in which we use a factorized
variational encoder that independently models the posterior over topical
mixture vectors of documents using a feed-forward network, and the posterior
over topic assignments to sentences using an RNN. Our preliminary experiments
on two different datasets indicate early promise, but also expose many
challenges that remain to be addressed.
Recursive Partitioning for Personalization using Observational Data
Authors: Nathan Kallus
Date: 2016-08-31 00:00:00
Proceeding: ICML 2017 8

We study the problem of learning to choose from m discrete treatment options
(e.g., news item or medical drug) the one with best causal effect for a
particular instance (e.g., user or patient) where the training data consists of
passive observations of covariates, treatment, and the outcome of the
treatment. The standard approach to this problem is regress and compare: split
the training data by treatment, fit a regression model in each split, and, for
a new instance, predict all m outcomes and pick the best. By reformulating the
problem as a single learning task rather than m separate ones, we propose a new
approach based on recursively partitioning the data into regimes where
different treatments are optimal. We extend this approach to an optimal
partitioning approach that finds a globally optimal partition, achieving a
compact, interpretable, and impactful personalization model. We develop new
tools for validating and evaluating personalization models on observational
data and use these to demonstrate the power of our novel approaches in a
personalized medicine and a job training application.
Two-sample Hypothesis Testing for Inhomogeneous Random Graphs
Authors: Debarghya Ghoshdastidar, Maurilio Gutzeit, Alexandra Carpentier, Ulrike Von Luxburg
Date: 2017-07-04 00:00:00
Tasks: Two-sample testing, Vocal Bursts Valence Prediction

The study of networks leads to a wide range of high dimensional inference problems. In many practical applications, one needs to draw inference from one or few large sparse networks. The present paper studies hypothesis testing of graphs in this high-dimensional regime, where the goal is to test between two populations of inhomogeneous random graphs defined on the same set of $n$ vertices. The size of each population $m$ is much smaller than $n$, and can even be a constant as small as 1. The critical question in this context is whether the problem is solvable for small $m$. We answer this question from a minimax testing perspective. Let $P,Q$ be the population adjacencies of two sparse inhomogeneous random graph models, and $d$ be a suitably defined distance function. Given a population of $m$ graphs from each model, we derive minimax separation rates for the problem of testing $P=Q$ against $d(P,Q)>\rho$. We observe that if $m$ is small, then the minimax separation is too large for some popular choices of $d$, including total variation distance between corresponding distributions. This implies that some models that are widely separated in $d$ cannot be distinguished for small $m$, and hence, the testing problem is generally not solvable in these cases. We also show that if $m>1$, then the minimax separation is relatively small if $d$ is the Frobenius norm or operator norm distance between $P$ and $Q$. For $m=1$, only the latter distance provides small minimax separation. Thus, for these distances, the problem is solvable for small $m$. We also present near-optimal two-sample tests in both cases, where tests are adaptive with respect to sparsity level of the graphs.
Concurrent Segmentation and Localization for Tracking of Surgical Instruments
Authors: Iro Laina, Nicola Rieke, Christian Rupprecht, Josué Page Vizcaíno, Abouzar Eslami, Federico Tombari, Nassir Navab
Date: 2017-03-30 00:00:00
Tasks: Pose Estimation, Position, regression

Real-time instrument tracking is a crucial requirement for various
computer-assisted interventions. In order to overcome problems such as specular
reflections and motion blur, we propose a novel method that takes advantage of
the interdependency between localization and segmentation of the surgical tool.
In particular, we reformulate the 2D instrument pose estimation as heatmap
regression and thereby enable a concurrent, robust and near real-time
regression of both tasks via deep learning. As demonstrated by our experimental
results, this modeling leads to a significantly improved performance than
directly regressing the tool position and allows our method to outperform the
state of the art on a Retinal Microsurgery benchmark and the MICCAI EndoVis
Challenge 2015.