pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing
Authors: Daniel Fernández-González, Carlos Gómez-Rodríguez
Date: 2018-10-25 00:00:00

We introduce novel dynamic oracles for training two of the most accurate
known shift-reduce algorithms for constituent parsing: the top-down and
in-order transition-based parsers. In both cases, the dynamic oracles manage to
notably increase their accuracy, in comparison to that obtained by performing
classic static training. In addition, by improving the performance of the
state-of-the-art in-order shift-reduce parser, we achieve the best accuracy to
date (92.0 F1) obtained by a fully-supervised single-model greedy shift-reduce
constituent parser on the WSJ benchmark.
GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks
Authors: Christopher Bowles, Liang Chen, Ricardo Guerrero, Paul Bentley, Roger Gunn, Alexander Hammers, David Alexander Dickie, Maria Valdés Hernández, Joanna Wardlaw, Daniel Rueckert
Date: 2018-10-25 00:00:00
Tasks: BIG-bench Machine Learning, Brain Segmentation

One of the biggest issues facing the use of machine learning in medical
imaging is the lack of availability of large, labelled datasets. The annotation
of medical images is not only expensive and time consuming but also highly
dependent on the availability of expert observers. The limited amount of
training data can inhibit the performance of supervised machine learning
algorithms which often need very large quantities of data on which to train to
avoid overfitting. So far, much effort has been directed at extracting as much
information as possible from what data is available. Generative Adversarial
Networks (GANs) offer a novel way to unlock additional information from a
dataset by generating synthetic samples with the appearance of real images.
This paper demonstrates the feasibility of introducing GAN derived synthetic
data to the training datasets in two brain segmentation tasks, leading to
improvements in Dice Similarity Coefficient (DSC) of between 1 and 5 percentage
points under different conditions, with the strongest effects seen fewer than
ten training image stacks are available.
Training of a Skull-Stripping Neural Network with efficient data augmentation
Authors: Gabriele Valvano, Nicola Martini, Andrea Leo, Gianmarco Santini, Daniele Della Latta, Emiliano Ricciardi, Dante Chiappino
Date: 2018-10-25 00:00:00
Tasks: Data Augmentation, Skull Stripping

Skull-stripping methods aim to remove the non-brain tissue from acquisition
of brain scans in magnetic resonance (MR) imaging. Although several methods
sharing this common purpose have been presented in literature, they all suffer
from the great variability of the MR images. In this work we propose a novel
approach based on Convolutional Neural Networks to automatically perform the
brain extraction obtaining cutting-edge performance in the NFBS public
database. Additionally, we focus on the efficient training of the neural
network designing an effective data augmentation pipeline. Obtained results are
evaluated through Dice metric, obtaining a value of 96.5%, and processing time,
with 4.5s per volume.
An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection
Authors: Liyan Sun, Jiexiang Wang, Yue Huang, Xinghao Ding, Hayit Greenspan, John Paisley
Date: 2018-10-25 00:00:00
Tasks: Data Augmentation, General Classification, Generative Adversarial Network, Image Generation, Lesion Detection, Lesion Segmentation, Prognosis, Segmentation, Translation

The identification of lesion within medical image data is necessary for
diagnosis, treatment and prognosis. Segmentation and classification approaches
are mainly based on supervised learning with well-paired image-level or
voxel-level labels. However, labeling the lesion in medical images is laborious
requiring highly specialized knowledge. We propose a medical image synthesis
model named abnormal-to-normal translation generative adversarial network
(ANT-GAN) to generate a normal-looking medical image based on its
abnormal-looking counterpart without the need for paired training data. Unlike
typical GANs, whose aim is to generate realistic samples with variations, our
more restrictive model aims at producing a normal-looking image corresponding
to one containing lesions, and thus requires a special design. Being able to
provide a "normal" counterpart to a medical image can provide useful side
information for medical imaging tasks like lesion segmentation or
classification validated by our experiments. In the other aspect, the ANT-GAN
model is also capable of producing highly realistic lesion-containing image
corresponding to the healthy one, which shows the potential in data
augmentation verified in our experiments.
Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction
Authors: Angelica I. Aviles-Rivero, Noémie Debroux, Guy Williams, Martin J. Graves, Carola-Bibiane Schonlieb
Date: 2018-10-25 00:00:00
Tasks: compressed sensing, Image Reconstruction, Motion Estimation, MRI Reconstruction

We address the problem of reconstructing high quality images from undersampled MRI data. This is a challenging task due to the highly ill-posed nature of the problem. In particular, in dynamic MRI scans, the interaction between the target structure and the physical motion affects the acquired measurements leading to blurring artefacts and loss of fine details. In this work, we propose a framework for dynamic MRI reconstruction framed under a new multi-task optimisation model called Compressed Sensing Plus Motion (CS+M). Firstly, we propose a single optimisation problem that simultaneously computes the MRI reconstruction and the physical motion. Secondly, we show our model can be efficiently solved by breaking it up into two more computationally tractable problems. The potentials and generalisation capabilities of our approach are demonstrated in different clinical applications including cardiac cine, cardiac perfusion and brain perfusion imaging. We show, through numerical and graphical experiments, that the proposed scheme reduces blurring artefacts and preserves the target shape and fine details. We also report the highest quality reconstruction under highly undersampling rates in comparison to several state of the art techniques.
HANDS18: Methods, Techniques and Applications for Hand Observation
Authors: Iason Oikonomidis, Guillermo Garcia-Hernando, Angela Yao, Antonis Argyros, Vincent Lepetit, Tae-Kyun Kim
Date: 2018-10-25 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition, Hand Pose Estimation, Pose Estimation

This report outlines the proceedings of the Fourth International Workshop on
Observing and Understanding Hands in Action (HANDS 2018). The fourth
instantiation of this workshop attracted significant interest from both
academia and the industry. The program of the workshop included regular papers
that are published as the workshop's proceedings, extended abstracts, invited
posters, and invited talks. Topics of the submitted works and invited talks and
posters included novel methods for hand pose estimation from RGB, depth, or
skeletal data, datasets for special cases and real-world applications, and
techniques for hand motion re-targeting and hand gesture recognition. The
invited speakers are leaders in their respective areas of specialization,
coming from both industry and academia. The main conclusions that can be drawn
are the turn of the community towards RGB data and the maturation of some
methods and techniques, which in turn has led to increasing interest for
real-world applications.
Adaptive Online Learning in Dynamic Environments
Authors: Lijun Zhang, Shiyin Lu, Zhi-Hua Zhou
Date: 2018-10-25 00:00:00
Proceeding: NeurIPS 2018 12

In this paper, we study online convex optimization in dynamic environments,
and aim to bound the dynamic regret with respect to any sequence of
comparators. Existing work have shown that online gradient descent enjoys an
$O(\sqrt{T}(1+P_T))$ dynamic regret, where $T$ is the number of iterations and
$P_T$ is the path-length of the comparator sequence. However, this result is
unsatisfactory, as there exists a large gap from the $\Omega(\sqrt{T(1+P_T)})$
lower bound established in our paper. To address this limitation, we develop a
novel online method, namely adaptive learning for dynamic environment (Ader),
which achieves an optimal $O(\sqrt{T(1+P_T)})$ dynamic regret. The basic idea
is to maintain a set of experts, each attaining an optimal dynamic regret for a
specific path-length, and combines them with an expert-tracking algorithm.
Furthermore, we propose an improved Ader based on the surrogate loss, and in
this way the number of gradient evaluations per round is reduced from $O(\log
T)$ to $1$. Finally, we extend Ader to the setting that a sequence of dynamical
models is available to characterize the comparators.
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
Authors: Vladimir Nekrasov, Hao Chen, Chunhua Shen, Ian Reid
Date: 2018-10-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Depth Estimation, Depth Prediction, GPU, image-classification, Image Classification, Image Segmentation, Knowledge Distillation, Language Modelling, Monocular Depth Estimation, Neural Architecture Search, Pose Estimation, Reinforcement Learning, Segmentation, Semantic Segmentation

Automated design of neural network architectures tailored for a specific task is an extremely promising, albeit inherently difficult, avenue to explore. While most results in this domain have been achieved on image classification and language modelling problems, here we concentrate on dense per-pixel tasks, in particular, semantic image segmentation using fully convolutional networks. In contrast to the aforementioned areas, the design choices of a fully convolutional network require several changes, ranging from the sort of operations that need to be used---e.g., dilated convolutions---to a solving of a more difficult optimisation problem. In this work, we are particularly interested in searching for high-performance compact segmentation architectures, able to run in real-time using limited resources. To achieve that, we intentionally over-parameterise the architecture during the training time via a set of auxiliary cells that provide an intermediate supervisory signal and can be omitted during the evaluation phase. The design of the auxiliary cell is emitted by a controller, a neural network with the fixed structure trained using reinforcement learning. More crucially, we demonstrate how to efficiently search for these architectures within limited time and computational budgets. In particular, we rely on a progressive strategy that terminates non-promising architectures from being further trained, and on Polyak averaging coupled with knowledge distillation to speed-up the convergence. Quantitatively, in 8 GPU-days our approach discovers a set of architectures performing on-par with state-of-the-art among compact models on the semantic segmentation, pose estimation and depth prediction tasks. Code will be made available here: https://github.com/drsleep/nas-segm-pytorch
Tackling Sequence to Sequence Mapping Problems with Neural Networks
Authors: Lei Yu
Date: 2018-10-25 00:00:00
Tasks: Domain Adaptation, Feature Engineering, speech-recognition, Speech Recognition

In Natural Language Processing (NLP), it is important to detect the
relationship between two sequences or to generate a sequence of tokens given
another observed sequence. We call the type of problems on modelling sequence
pairs as sequence to sequence (seq2seq) mapping problems. A lot of research has
been devoted to finding ways of tackling these problems, with traditional
approaches relying on a combination of hand-crafted features, alignment models,
segmentation heuristics, and external linguistic resources. Although great
progress has been made, these traditional approaches suffer from various
drawbacks, such as complicated pipeline, laborious feature engineering, and the
difficulty for domain adaptation. Recently, neural networks emerged as a
promising solution to many problems in NLP, speech recognition, and computer
vision. Neural models are powerful because they can be trained end to end,
generalise well to unseen examples, and the same framework can be easily
adapted to a new domain.
  The aim of this thesis is to advance the state-of-the-art in seq2seq mapping
problems with neural networks. We explore solutions from three major aspects:
investigating neural models for representing sequences, modelling interactions
between sequences, and using unpaired data to boost the performance of neural
models. For each aspect, we propose novel models and evaluate their efficacy on
various tasks of seq2seq mapping.
A Gaussian Process perspective on Convolutional Neural Networks
Authors: Anastasia Borovykh
Date: 2018-10-25 00:00:00
Tasks: Gaussian Processes

In this paper we cast the well-known convolutional neural network in a
Gaussian process perspective. In this way we hope to gain additional insights
into the performance of convolutional networks, in particular understand under
what circumstances they tend to perform well and what assumptions are
implicitly made in the network. While for fully-connected networks the
properties of convergence to Gaussian processes have been studied extensively,
little is known about situations in which the output from a convolutional
network approaches a multivariate normal distribution.
The Logoscope: a Semi-Automatic Tool for Detecting and Documenting French New Words
Authors: Ingrid Falk, Delphine Bernhard, Christophe Gérard
Date: 2018-10-25 00:00:00

In this article we present the design and implementation of the Logoscope,
the first tool especially developed to detect new words of the French language,
to document them and allow a public access through a web interface.
  This semi-automatic tool collects new words daily by browsing the online
versions of French well known newspapers such as Le Monde, Le Figaro, L'Equipe,
Lib\'eration, La Croix, Les \'Echos. In contrast to other existing tools
essentially dedicated to dictionary development, the Logoscope attempts to give
a more complete account of the context in which the new words occur. In
addition to the commonly given morpho-syntactic information it also provides
information about the textual and discursive contexts of the word creation; in
particular, it automatically determines the (journalistic) topics of the text
containing the new word.
  In this article we first give a general overview of the developed tool. We
then describe the approach taken, we discuss the linguistic background which
guided our design decisions and present the computational methods we used to
implement it.
Perceptual Visual Interactive Learning
Authors: Shenglan Liu, Xiang Liu, Yang Liu, Lin Feng, Hong Qiao, Jian Zhou, Yang Wang
Date: 2018-10-25 00:00:00
Tasks: Clustering, Dimensionality Reduction, General Classification, Retrieval

Supervised learning methods are widely used in machine learning. However, the
lack of labels in existing data limits the application of these technologies.
Visual interactive learning (VIL) compared with computers can avoid semantic
gap, and solve the labeling problem of small label quantity (SLQ) samples in a
groundbreaking way. In order to fully understand the importance of VIL to the
interaction process, we re-summarize the interactive learning related
algorithms (e.g. clustering, classification, retrieval etc.) from the
perspective of VIL. Note that, perception and cognition are two main visual
processes of VIL. On this basis, we propose a perceptual visual interactive
learning (PVIL) framework, which adopts gestalt principle to design interaction
strategy and multi-dimensionality reduction (MDR) to optimize the process of
visualization. The advantage of PVIL framework is that it combines computer's
sensitivity of detailed features and human's overall understanding of global
tasks. Experimental results validate that the framework is superior to
traditional computer labeling methods (such as label propagation) in both
accuracy and efficiency, which achieves significant classification results on
dense distribution and sparse classes dataset.
Supervised Classification Methods for Flash X-ray single particle diffraction Imaging
Authors: Jing Liu, Gijs van der Schot, Stefan Engblom
Date: 2018-10-25 00:00:00
Tasks: General Classification

Current Flash X-ray single-particle diffraction Imaging (FXI) experiments,
which operate on modern X-ray Free Electron Lasers (XFELs), can record millions
of interpretable diffraction patterns from individual biomolecules per day. Due
to the stochastic nature of the XFELs, those patterns will to a varying degree
include scatterings from contaminated samples. Also, the heterogeneity of the
sample biomolecules is unavoidable and complicates data processing. Reducing
the data volumes and selecting high-quality single-molecule patterns are
therefore critical steps in the experimental set-up.
  In this paper, we present two supervised template-based learning methods for
classifying FXI patterns. Our Eigen-Image and Log-Likelihood classifier can
find the best-matched template for a single-molecule pattern within a few
milliseconds. It is also straightforward to parallelize them so as to fully
match the XFEL repetition rate, thereby enabling processing at site.
Efficient Learning of Restricted Boltzmann Machines Using Covariance Estimates
Authors: Vidyadhar Upadhya, P. S. Sastry
Date: 2018-10-25 00:00:00

Learning RBMs using standard algorithms such as CD(k) involves gradient descent on the negative log-likelihood. One of the terms in the gradient, which involves expectation w.r.t. the model distribution, is intractable and is obtained through an MCMC estimate. In this work we show that the Hessian of the log-likelihood can be written in terms of covariances of hidden and visible units and hence, all elements of the Hessian can also be estimated using the same MCMC samples with small extra computational costs. Since inverting the Hessian may be computationally expensive, we propose an algorithm that uses inverse of the diagonal approximation of the Hessian, instead. This essentially results in parameter-specific adaptive learning rates for the gradient descent process and improves the efficiency of learning RBMs compared to the standard methods. Specifically we show that using the inverse of diagonal approximation of Hessian in the stochastic DC (difference of convex functions) program approach results in very efficient learning of RBMs.
Adversarially Robust Optimization with Gaussian Processes
Authors: Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka, Volkan Cevher
Date: 2018-10-25 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Gaussian Processes

In this paper, we consider the problem of Gaussian process (GP) optimization
with an added robustness requirement: The returned point may be perturbed by an
adversary, and we require the function value to remain as high as possible even
after this perturbation. This problem is motivated by settings in which the
underlying functions during optimization and implementation stages are
different, or when one is interested in finding an entire region of good inputs
rather than only a single point. We show that standard GP optimization
algorithms do not exhibit the desired robustness properties, and provide a
novel confidence-bound based algorithm StableOpt for this purpose. We
rigorously establish the required number of samples for StableOpt to find a
near-optimal point, and we complement this guarantee with an
algorithm-independent lower bound. We experimentally demonstrate several
potential applications of interest using real-world data sets, and we show that
StableOpt consistently succeeds in finding a stable maximizer where several
baseline methods fail.
Geometry and clustering with metrics derived from separable Bregman divergences
Authors: Erika Gomes-Gonçalves, Henryk Gzyl, Frank Nielsen
Date: 2018-10-25 00:00:00
Tasks: Clustering, Quantization

Separable Bregman divergences induce Riemannian metric spaces that are
isometric to the Euclidean space after monotone embeddings. We investigate
fixed rate quantization and its codebook Voronoi diagrams, and report on
experimental performances of partition-based, hierarchical, and soft clustering
algorithms with respect to these Riemann-Bregman distances.
Word Embedding based Edit Distance
Authors: Yilin Niu, chao qiao, Hang Li, Minlie Huang
Date: 2018-10-25 00:00:00
Tasks: text similarity

Text similarity calculation is a fundamental problem in natural language
processing and related fields. In recent years, deep neural networks have been
developed to perform the task and high performances have been achieved. The
neural networks are usually trained with labeled data in supervised learning,
and creation of labeled data is usually very costly. In this short paper, we
address unsupervised learning for text similarity calculation. We propose a new
method called Word Embedding based Edit Distance (WED), which incorporates word
embedding into edit distance. Experiments on three benchmark datasets show WED
outperforms state-of-the-art unsupervised methods including edit distance,
TF-IDF based cosine, word embedding based cosine, Jaccard index, etc.
Attack Graph Convolutional Networks by Adding Fake Nodes
Authors: Xiaoyun Wang, Minhao Cheng, Joe Eaton, Cho-Jui Hsieh, Felix Wu
Date: 2018-10-25 00:00:00
Proceeding: ICLR 2019 5

In this paper, we study the robustness of graph convolutional networks (GCNs). Previous work have shown that GCNs are vulnerable to adversarial perturbation on adjacency or feature matrices of existing nodes; however, such attacks are usually unrealistic in real applications. For instance, in social network applications, the attacker will need to hack into either the client or server to change existing links or features. In this paper, we propose a new type of "fake node attacks" to attack GCNs by adding malicious fake nodes. This is much more realistic than previous attacks; in social network applications, the attacker only needs to register a set of fake accounts and link to existing ones. To conduct fake node attacks, a greedy algorithm is proposed to generate edges of malicious nodes and their corresponding features aiming to minimize the classification accuracy on the target nodes. In addition, we introduce a discriminator to classify malicious nodes from real nodes, and propose a Greedy-GAN attack to simultaneously update the discriminator and the attacker, to make malicious nodes indistinguishable from the real ones. Our non-targeted attack decreases the accuracy of GCN down to 0.03, and our targeted attack reaches a success rate of 78% on a group of 100 nodes, and 90% on average for attacking a single target node.
Law and Adversarial Machine Learning
Authors: Ram Shankar Siva Kumar, David R. O'Brien, Kendra Albert, Salome Vilojen
Date: 2018-10-25 00:00:00
Tasks: BIG-bench Machine Learning

When machine learning systems fail because of adversarial manipulation, how
should society expect the law to respond? Through scenarios grounded in
adversarial ML literature, we explore how some aspects of computer crime,
copyright, and tort law interface with perturbation, poisoning, model stealing
and model inversion attacks to show how some attacks are more likely to result
in liability than others. We end with a call for action to ML researchers to
invest in transparent benchmarks of attacks and defenses; architect ML systems
with forensics in mind and finally, think more about adversarial machine
learning in the context of civil liberties. The paper is targeted towards ML
researchers who have no legal background.