pwc-archive/papers-with-abstracts huggingface dataset:
Learning-Based Quality Control for Cardiac MR Images
Authors: Giacomo Tarroni, Ozan Oktay, Wenjia Bai, Andreas Schuh, Hideaki Suzuki, Jonathan Passerat-Palmbach, Antonio de Marvao, Declan P. O'Regan, Stuart Cook, Ben Glocker, Paul M. Matthews, Daniel Rueckert
Date: 2018-03-25 00:00:00
Tasks: Motion Detection, Specificity

The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends
on the ability of the operator to correctly tune the acquisition parameters to
the subject being scanned and on the potential occurrence of imaging artefacts
such as cardiac and respiratory motion. In the clinical practice, a quality
control step is performed by visual assessment of the acquired images: however,
this procedure is strongly operator-dependent, cumbersome and sometimes
incompatible with the time constraints in clinical settings and large-scale
studies. We propose a fast, fully-automated, learning-based quality control
pipeline for CMR images, specifically for short-axis image stacks. Our pipeline
performs three important quality checks: 1) heart coverage estimation, 2)
inter-slice motion detection, 3) image contrast estimation in the cardiac
region. The pipeline uses a hybrid decision forest method - integrating both
regression and structured classification models - to extract landmarks as well
as probabilistic segmentation maps from both long- and short-axis images as a
basis to perform the quality checks. The technique was tested on up to 3000
cases from the UK Biobank as well as on 100 cases from the UK Digital Heart
Project, and validated against manual annotations and visual inspections
performed by expert interpreters. The results show the capability of the
proposed pipeline to correctly detect incomplete or corrupted scans (e.g. on UK
Biobank, sensitivity and specificity respectively 88% and 99% for heart
coverage estimation, 85% and 95% for motion detection), allowing their
exclusion from the analysed dataset or the triggering of a new acquisition.
Stochastic bandits robust to adversarial corruptions
Authors: Thodoris Lykouris, Vahab Mirrokni, Renato Paes Leme
Date: 2018-03-25 00:00:00

We introduce a new model of stochastic bandits with adversarial corruptions
which aims to capture settings where most of the input follows a stochastic
pattern but some fraction of it can be adversarially changed to trick the
algorithm, e.g., click fraud, fake reviews and email spam. The goal of this
model is to encourage the design of bandit algorithms that (i) work well in
mixed adversarial and stochastic models, and (ii) whose performance
deteriorates gracefully as we move from fully stochastic to fully adversarial
models.
  In our model, the rewards for all arms are initially drawn from a
distribution and are then altered by an adaptive adversary. We provide a simple
algorithm whose performance gracefully degrades with the total corruption the
adversary injected in the data, measured by the sum across rounds of the
biggest alteration the adversary made in the data in that round; this total
corruption is denoted by $C$. Our algorithm provides a guarantee that retains
the optimal guarantee (up to a logarithmic term) if the input is stochastic and
whose performance degrades linearly to the amount of corruption $C$, while
crucially being agnostic to it. We also provide a lower bound showing that this
linear degradation is necessary if the algorithm achieves optimal performance
in the stochastic setting (the lower bound works even for a known amount of
corruption, a special case in which our algorithm achieves optimal performance
without the extra logarithm).
Logistic Regression: The Importance of Being Improper
Authors: Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
Date: 2018-03-25 00:00:00
Tasks: regression

Learning linear predictors with the logistic loss---both in stochastic and
online settings---is a fundamental task in machine learning and statistics,
with direct connections to classification and boosting. Existing "fast rates"
for this setting exhibit exponential dependence on the predictor norm, and
Hazan et al. (2014) showed that this is unfortunately unimprovable. Starting
with the simple observation that the logistic loss is $1$-mixable, we design a
new efficient improper learning algorithm for online logistic regression that
circumvents the aforementioned lower bound with a regret bound exhibiting a
doubly-exponential improvement in dependence on the predictor norm. This
provides a positive resolution to a variant of the COLT 2012 open problem of
McMahan and Streeter (2012) when improper learning is allowed. This improvement
is obtained both in the online setting and, with some extra work, in the batch
statistical setting with high probability. We also show that the improved
dependence on predictor norm is near-optimal.
  Leveraging this improved dependency on the predictor norm yields the
following applications: (a) we give algorithms for online bandit multiclass
learning with the logistic loss with an $\tilde{O}(\sqrt{n})$ relative mistake
bound across essentially all parameter ranges, thus providing a solution to the
COLT 2009 open problem of Abernethy and Rakhlin (2009), and (b) we give an
adaptive algorithm for online multiclass boosting with optimal sample
complexity, thus partially resolving an open problem of Beygelzimer et al.
(2015) and Jung et al. (2017). Finally, we give information-theoretic bounds on
the optimal rates for improper logistic regression with general function
classes, thereby characterizing the extent to which our improvement for linear
classes extends to other parametric and even nonparametric settings.
DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes
Authors: Giles Tetteh, Velizar Efremov, Nils D. Forkert, Matthias Schneider, Jan Kirschke, Bruno Weber, Claus Zimmer, Marie Piraud, Bjoern H. Menze
Date: 2018-03-25 00:00:00
Tasks: Transfer Learning

We present DeepVesselNet, an architecture tailored to the challenges faced when extracting vessel networks or trees and corresponding features in 3-D angiographic volumes using deep learning. We discuss the problems of low execution speed and high memory requirements associated with full 3-D convolutional networks, high-class imbalance arising from the low percentage of vessel voxels, and unavailability of accurately annotated training data - and offer solutions as the building blocks of DeepVesselNet. First, we formulate 2-D orthogonal cross-hair filters which make use of 3-D context information at a reduced computational burden. Second, we introduce a class balancing cross-entropy loss function with false positive rate correction to handle the high-class imbalance and high false positive rate problems associated with existing loss functions. Finally, we generate synthetic dataset using a computational angiogenesis model capable of generating vascular trees under physiological constraints on local network structure and topology and use these data for transfer learning. DeepVesselNet is optimized for segmenting and analyzing vessels, and we test the performance on a range of angiographic volumes including clinical MRA data of the human brain, as well as X-ray tomographic microscopy scans of the rat brain. Our experiments show that, by replacing 3-D filters with cross-hair filters in our network, we achieve over 23% improvement in speed, lower memory footprint, lower network complexity which prevents overfitting and comparable accuracy (with a Cox-Wilcoxon paired sample significance test p-value of 0.07 when compared to full 3-D filters). Our class balancing metric is crucial for training the network and transfer learning with synthetic data is an efficient, robust, and very generalizable approach leading to a network that excels in a variety of angiography segmentation tasks.
Text Segmentation as a Supervised Learning Task
Authors: Omri Koshorek, Adir Cohen, Noam Mor, Michael Rotman, Jonathan Berant
Date: 2018-03-25 00:00:00
Proceeding: NAACL 2018 6
Tasks: Clustering, Segmentation, Text Segmentation

Text segmentation, the task of dividing a document into contiguous segments
based on its semantic structure, is a longstanding challenge in language
understanding. Previous work on text segmentation focused on unsupervised
methods such as clustering or graph search, due to the paucity in labeled data.
In this work, we formulate text segmentation as a supervised learning problem,
and present a large new dataset for text segmentation that is automatically
extracted and labeled from Wikipedia. Moreover, we develop a segmentation model
based on this dataset and show that it generalizes well to unseen natural text.
StarMap for Category-Agnostic Keypoint and Viewpoint Estimation
Authors: Xingyi Zhou, Arjun Karpur, Linjie Luo, Qi-Xing Huang
Date: 2018-03-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: Keypoint Detection, Viewpoint Estimation

Semantic keypoints provide concise abstractions for a variety of visual
understanding tasks. Existing methods define semantic keypoints separately for
each category with a fixed number of semantic labels in fixed indices. As a
result, this keypoint representation is in-feasible when objects have a varying
number of parts, e.g. chairs with varying number of legs. We propose a
category-agnostic keypoint representation, which combines a multi-peak heatmap
(StarMap) for all the keypoints and their corresponding features as 3D
locations in the canonical viewpoint (CanViewFeature) defined for each
instance. Our intuition is that the 3D locations of the keypoints in canonical
object views contain rich semantic and compositional information. Using our
flexible representation, we demonstrate competitive performance in keypoint
detection and localization compared to category-specific state-of-the-art
methods. Moreover, we show that when augmented with an additional depth channel
(DepthMap) to lift the 2D keypoints to 3D, our representation can achieve
state-of-the-art results in viewpoint estimation. Finally, we show that our
category-agnostic keypoint representation can be generalized to novel
categories.
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization
Authors: Jiong Zhang, Qi Lei, Inderjit S. Dhillon
Date: 2018-03-25 00:00:00
Proceeding: ICML 2018 7

Vanishing and exploding gradients are two of the main obstacles in training
deep neural networks, especially in capturing long range dependencies in
recurrent neural networks~(RNNs). In this paper, we present an efficient
parametrization of the transition matrix of an RNN that allows us to stabilize
the gradients that arise in its training. Specifically, we parameterize the
transition matrix by its singular value decomposition(SVD), which allows us to
explicitly track and control its singular values. We attain efficiency by using
tools that are common in numerical linear algebra, namely Householder
reflectors for representing the orthogonal matrices that arise in the SVD. By
explicitly controlling the singular values, our proposed Spectral-RNN method
allows us to easily solve the exploding gradient problem and we observe that it
empirically solves the vanishing gradient issue to a large extent. We note that
the SVD parameterization can be used for any rectangular weight matrix, hence
it can be easily extended to any deep neural network, such as a multi-layer
perceptron. Theoretically, we demonstrate that our parameterization does not
lose any expressive power, and show how it controls generalization of RNN for
the classification task. %, and show how it potentially makes the optimization
process easier. Our extensive experimental results also demonstrate that the
proposed framework converges faster, and has good generalization, especially in
capturing long range dependencies, as shown on the synthetic addition and copy
tasks, as well as on MNIST and Penn Tree Bank data sets.
Differentially Private Releasing via Deep Generative Model (Technical Report)
Authors: Xinyang Zhang, Shouling Ji, Ting Wang
Date: 2018-01-05 00:00:00
Tasks: Privacy Preserving

Privacy-preserving releasing of complex data (e.g., image, text, audio)
represents a long-standing challenge for the data mining research community.
Due to rich semantics of the data and lack of a priori knowledge about the
analysis task, excessive sanitization is often necessary to ensure privacy,
leading to significant loss of the data utility. In this paper, we present
dp-GAN, a general private releasing framework for semantic-rich data. Instead
of sanitizing and then releasing the data, the data curator publishes a deep
generative model which is trained using the original data in a differentially
private manner; with the generative model, the analyst is able to produce an
unlimited amount of synthetic data for arbitrary analysis tasks. In contrast of
alternative solutions, dp-GAN highlights a set of key features: (i) it provides
theoretical privacy guarantee via enforcing the differential privacy principle;
(ii) it retains desirable utility in the released model, enabling a variety of
otherwise impossible analyses; and (iii) most importantly, it achieves
practical training scalability and stability by employing multi-fold
optimization strategies. Through extensive empirical evaluation on benchmark
datasets and analyses, we validate the efficacy of dp-GAN.
SUNLayer: Stable denoising with generative networks
Authors: Dustin G. Mixon, Soledad Villar
Date: 2018-03-25 00:00:00
Tasks: compressed sensing, Denoising, Image Denoising, Super-Resolution

It has been experimentally established that deep neural networks can be used
to produce good generative models for real world data. It has also been
established that such generative models can be exploited to solve classical
inverse problems like compressed sensing and super resolution. In this work we
focus on the classical signal processing problem of image denoising. We propose
a theoretical setting that uses spherical harmonics to identify what
mathematical properties of the activation functions will allow signal denoising
with local methods.
Data-driven Discovery of Closure Models
Authors: Shaowu Pan, Karthik Duraisamy
Date: 2018-03-25 00:00:00
Tasks: Form

Derivation of reduced order representations of dynamical systems requires the
modeling of the truncated dynamics on the retained dynamics. In its most
general form, this so-called closure model has to account for memory effects.
In this work, we present a framework of operator inference to extract the
governing dynamics of closure from data in a compact, non-Markovian form. We
employ sparse polynomial regression and artificial neural networks to extract
the underlying operator. For a special class of non-linear systems,
observability of the closure in terms of the resolved dynamics is analyzed and
theoretical results are presented on the compactness of the memory. The
proposed framework is evaluated on examples consisting of linear to nonlinear
systems with and without chaotic dynamics, with an emphasis on predictive
performance on unseen data.
Frame-Recurrent Video Super-Resolution
Authors: Mehdi S. M. Sajjadi, Raviteja Vemulapalli, Matthew Brown
Date: 2018-01-14 00:00:00
Proceeding: CVPR 2018 6
Tasks: Motion Compensation, Multi-Frame Super-Resolution, Super-Resolution, Video Super-Resolution

Recent advances in video super-resolution have shown that convolutional
neural networks combined with motion compensation are able to merge information
from multiple low-resolution (LR) frames to generate high-quality images.
Current state-of-the-art methods process a batch of LR frames to generate a
single high-resolution (HR) frame and run this scheme in a sliding window
fashion over the entire video, effectively treating the problem as a large
number of separate multi-frame super-resolution tasks. This approach has two
main weaknesses: 1) Each input frame is processed and warped multiple times,
increasing the computational cost, and 2) each output frame is estimated
independently conditioned on the input frames, limiting the system's ability to
produce temporally consistent results.
  In this work, we propose an end-to-end trainable frame-recurrent video
super-resolution framework that uses the previously inferred HR estimate to
super-resolve the subsequent frame. This naturally encourages temporally
consistent results and reduces the computational cost by warping only one image
in each step. Furthermore, due to its recurrent nature, the proposed method has
the ability to assimilate a large number of previous frames without increased
computational demands. Extensive evaluations and comparisons with previous
methods validate the strengths of our approach and demonstrate that the
proposed framework is able to significantly outperform the current state of the
art.
Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB
Authors: Dushyant Mehta, Oleksandr Sotnychenko, Franziska Mueller, Weipeng Xu, Srinath Sridhar, Gerard Pons-Moll, Christian Theobalt
Date: 2017-12-09 00:00:00
Tasks: 3D Human Pose Estimation, 3D Multi-Person Pose Estimation (absolute), 3D Multi-Person Pose Estimation (root-relative), 3D Pose Estimation, Pose Estimation

We propose a new single-shot method for multi-person 3D pose estimation in
general scenes from a monocular RGB camera. Our approach uses novel
occlusion-robust pose-maps (ORPM) which enable full body pose inference even
under strong partial occlusions by other people and objects in the scene. ORPM
outputs a fixed number of maps which encode the 3D joint locations of all
people in the scene. Body part associations allow us to infer 3D pose for an
arbitrary number of people without explicit bounding box prediction. To train
our approach we introduce MuCo-3DHP, the first large scale training data set
showing real images of sophisticated multi-person interactions and occlusions.
We synthesize a large corpus of multi-person images by compositing images of
individual people (with ground truth from mutli-view performance capture). We
evaluate our method on our new challenging 3D annotated multi-person test set
MuPoTs-3D where we achieve state-of-the-art performance. To further stimulate
research in multi-person 3D pose estimation, we will make our new datasets, and
associated code publicly available for research purposes.
The Geometry of Culture: Analyzing Meaning through Word Embeddings
Authors: Austin C. Kozlowski, Matt Taddy, James A. Evans
Date: 2018-03-25 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Word Embeddings

We demonstrate the utility of a new methodological tool, neural-network word
embedding models, for large-scale text analysis, revealing how these models
produce richer insights into cultural associations and categories than possible
with prior methods. Word embeddings represent semantic relations between words
as geometric relationships between vectors in a high-dimensional space,
operationalizing a relational model of meaning consistent with contemporary
theories of identity and culture. We show that dimensions induced by word
differences (e.g. man - woman, rich - poor, black - white, liberal -
conservative) in these vector spaces closely correspond to dimensions of
cultural meaning, and the projection of words onto these dimensions reflects
widely shared cultural connotations when compared to surveyed responses and
labeled historical data. We pilot a method for testing the stability of these
associations, then demonstrate applications of word embeddings for
macro-cultural investigation with a longitudinal analysis of the coevolution of
gender and class associations in the United States over the 20th century and a
comparative analysis of historic distinctions between markers of gender and
class in the U.S. and Britain. We argue that the success of these
high-dimensional models motivates a move towards "high-dimensional theorizing"
of meanings, identities and cultural processes.
Synthesizing Skeletons for Reactive Systems
Authors: Bernd Finkbeiner, Hazem Torfah
Date: 2018-03-25 00:00:00

We present an analysis technique for temporal specifications of reactive
systems that identifies, on the level of individual system outputs over time,
which parts of the implementation are determined by the specification, and
which parts are still open. This information is represented in the form of a
labeled transition system, which we call skeleton. Each state of the skeleton
is labeled with a three-valued assignment to the output variables: each output
can be true, false, or open, where true or false means that the value must be
true or false, respectively, and open means that either value is still
possible. We present algorithms for the verification of skeletons and for the
learning-based synthesis of skeletons from specifications in linear-time
temporal logic (LTL). The algorithm returns a skeleton that satisfies the given
LTL specification in time polynomial in the size of the minimal skeleton. Our
new analysis technique can be used to recognize and repair specifications that
underspecify critical situations. The technique thus complements existing
methods for the recognition and repair of overspecifications via the
identification of unrealizable cores.
Evolutionary n-level Hypergraph Partitioning with Adaptive Coarsening
Authors: Richard J. Preen, Jim Smith
Date: 2018-03-25 00:00:00
Tasks: Evolutionary Algorithms, hypergraph partitioning

Hypergraph partitioning is an NP-hard problem that occurs in many computer
science applications where it is necessary to reduce large problems into a
number of smaller, computationally tractable sub-problems. Current techniques
use a multilevel approach wherein an initial partitioning is performed after
compressing the hypergraph to a predetermined level. This level is typically
chosen to produce very coarse hypergraphs in which heuristic algorithms are
fast and effective. This article presents a novel memetic algorithm which
remains effective on larger initial hypergraphs. This enables the exploitation
of information that can be lost during coarsening and results in improved final
solution quality. We use this algorithm to present an empirical analysis of the
space of possible initial hypergraphs in terms of its searchability at
different levels of coarsening. We find that the best results arise at
coarsening levels unique to each hypergraph. Based on this, we introduce an
adaptive scheme that stops coarsening when the rate of information loss in a
hypergraph becomes non-linear and show that this produces further improvements.
The results show that we have identified a valuable role for evolutionary
algorithms within the current state-of-the-art hypergraph partitioning
framework.
Adversarial Deep Learning for Robust Detection of Binary Encoded Malware
Authors: Abdullah Al-Dujaili, Alex Huang, Erik Hemberg, Una-May O'Reilly
Date: 2018-01-09 00:00:00
Tasks: Deep Learning

Malware is constantly adapting in order to avoid detection. Model based
malware detectors, such as SVM and neural networks, are vulnerable to so-called
adversarial examples which are modest changes to detectable malware that allows
the resulting malware to evade detection. Continuous-valued methods that are
robust to adversarial examples of images have been developed using saddle-point
optimization formulations. We are inspired by them to develop similar methods
for the discrete, e.g. binary, domain which characterizes the features of
malware. A specific extra challenge of malware is that the adversarial examples
must be generated in a way that preserves their malicious functionality. We
introduce methods capable of generating functionally preserved adversarial
malware examples in the binary domain. Using the saddle-point formulation, we
incorporate the adversarial examples into the training of models that are
robust to them. We evaluate the effectiveness of the methods and others in the
literature on a set of Portable Execution~(PE) files. Comparison prompts our
introduction of an online measure computed during training to assess general
expectation of robustness.