pwc-archive/papers-with-abstracts huggingface dataset:
Learning to diagnose from scratch by exploiting dependencies among labels
Authors: Li Yao, Eric Poblenz, Dmitry Dagunts, Ben Covington, Devon Bernard, Kevin Lyman
Date: 2017-10-28 00:00:00
Proceeding: ICLR 2018 1
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Translation

The field of medical diagnostics contains a wealth of challenges which
closely resemble classical machine learning problems; practical constraints,
however, complicate the translation of these endpoints naively into classical
architectures. Many tasks in radiology, for example, are largely problems of
multi-label classification wherein medical images are interpreted to indicate
multiple present or suspected pathologies. Clinical settings drive the
necessity for high accuracy simultaneously across a multitude of pathological
outcomes and greatly limit the utility of tools which consider only a subset.
This issue is exacerbated by a general scarcity of training data and maximizes
the need to extract clinically relevant features from available samples --
ideally without the use of pre-trained models which may carry forward
undesirable biases from tangentially related tasks. We present and evaluate a
partial solution to these constraints in using LSTMs to leverage
interdependencies among target labels in predicting 14 pathologic patterns from
chest x-rays and establish state of the art results on the largest publicly
available chest x-ray dataset from the NIH without pre-training. Furthermore,
we propose and discuss alternative evaluation metrics and their relevance in
clinical practice.
Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions
Authors: Nikolas Lessmann, Bram van Ginneken, Majd Zreik, Pim A. de Jong, Bob D. de Vos, Max A. Viergever, Ivana Išgum
Date: 2017-11-01 00:00:00

Heavy smokers undergoing screening with low-dose chest CT are affected by
cardiovascular disease as much as by lung cancer. Low-dose chest CT scans
acquired in screening enable quantification of atherosclerotic calcifications
and thus enable identification of subjects at increased cardiovascular risk.
This paper presents a method for automatic detection of coronary artery,
thoracic aorta and cardiac valve calcifications in low-dose chest CT using two
consecutive convolutional neural networks. The first network identifies and
labels potential calcifications according to their anatomical location and the
second network identifies true calcifications among the detected candidates.
This method was trained and evaluated on a set of 1744 CT scans from the
National Lung Screening Trial. To determine whether any reconstruction or only
images reconstructed with soft tissue filters can be used for calcification
detection, we evaluated the method on soft and medium/sharp filter
reconstructions separately. On soft filter reconstructions, the method achieved
F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta,
aortic valve and mitral valve calcifications, respectively. On sharp filter
reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively.
Linearly weighted kappa coefficients for risk category assignment based on per
subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter
reconstructions, respectively. These results demonstrate that the presented
method enables reliable automatic cardiovascular risk assessment in all
low-dose chest CT scans acquired for lung cancer screening.
Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer Learning
Authors: Vladimir Ilievski, Claudiu Musat, Andreea Hossmann, Michael Baeriswyl
Date: 2018-02-01 00:00:00
Tasks: Chatbot, Deep Reinforcement Learning, Management, Natural Language Understanding, Reinforcement Learning, Specificity, Transfer Learning

Goal-Oriented (GO) Dialogue Systems, colloquially known as goal oriented
chatbots, help users achieve a predefined goal (e.g. book a movie ticket)
within a closed domain. A first step is to understand the user's goal by using
natural language understanding techniques. Once the goal is known, the bot must
manage a dialogue to achieve that goal, which is conducted with respect to a
learnt policy. The success of the dialogue system depends on the quality of the
policy, which is in turn reliant on the availability of high-quality training
data for the policy learning method, for instance Deep Reinforcement Learning.
  Due to the domain specificity, the amount of available data is typically too
low to allow the training of good dialogue policies. In this paper we introduce
a transfer learning method to mitigate the effects of the low in-domain data
availability. Our transfer learning based approach improves the bot's success
rate by 20% in relative terms for distant domains and we more than double it
for close domains, compared to the model without transfer learning. Moreover,
the transfer learning chatbots learn the policy up to 5 to 10 times faster.
Finally, as the transfer learning approach is complementary to additional
processing such as warm-starting, we show that their joint application gives
the best outcomes.
Stochastic Learning of Nonstationary Kernels for Natural Language Modeling
Authors: Sahil Garg, Greg Ver Steeg, Aram Galstyan
Date: 2018-01-11 00:00:00
Tasks: Language Modeling, Language Modelling

Natural language processing often involves computations with semantic or
syntactic graphs to facilitate sophisticated reasoning based on structural
relationships. While convolution kernels provide a powerful tool for comparing
graph structure based on node (word) level relationships, they are difficult to
customize and can be computationally expensive. We propose a generalization of
convolution kernels, with a nonstationary model, for better expressibility of
natural languages in supervised settings. For a scalable learning of the
parameters introduced with our model, we propose a novel algorithm that
leverages stochastic sampling on k-nearest neighbor graphs, along with
approximations based on locality-sensitive hashing. We demonstrate the
advantages of our approach on a challenging real-world (structured inference)
problem of automatically extracting biological models from the text of
scientific papers.
A New Registration Approach for Dynamic Analysis of Calcium Signals in Organs
Authors: Peixian Liang, Jianxu Chen, Pavel A. Brodskiy, Qinfeng Wu, Yejia Zhang, Yizhe Zhang, Lin Yang, Jeremiah J. Zartman, Danny Z. Chen
Date: 2018-02-01 00:00:00

Wing disc pouches of fruit flies are a powerful genetic model for studying
physiological intercellular calcium ($Ca^{2+}$) signals for dynamic analysis of
cell signaling in organ development and disease studies. A key to analyzing
spatial-temporal patterns of $Ca^{2+}$ signal waves is to accurately align the
pouches across image sequences. However, pouches in different image frames may
exhibit extensive intensity oscillations due to $Ca^{2+}$ signaling dynamics,
and commonly used multimodal non-rigid registration methods may fail to achieve
satisfactory results. In this paper, we develop a new two-phase non-rigid
registration approach to register pouches in image sequences. First, we conduct
segmentation of the region of interest. (i.e., pouches) using a deep neural
network model. Second, we obtain an optimal transformation and align pouches
across the image sequences. Evaluated using both synthetic data and real pouch
data, our method considerably outperforms the state-of-the-art non-rigid
registration methods.
Feature Space Transfer for Data Augmentation
Authors: Bo Liu, Xudong Wang, Mandar Dixit, Roland Kwitt, Nuno Vasconcelos
Date: 2018-01-13 00:00:00
Proceeding: CVPR 2018 6
Tasks: Data Augmentation, Decoder, Object, Object Recognition, Transfer Learning

The problem of data augmentation in feature space is considered. A new
architecture, denoted the FeATure TransfEr Network (FATTEN), is proposed for
the modeling of feature trajectories induced by variations of object pose. This
architecture exploits a parametrization of the pose manifold in terms of pose
and appearance. This leads to a deep encoder/decoder network architecture,
where the encoder factors into an appearance and a pose predictor. Unlike
previous attempts at trajectory transfer, FATTEN can be efficiently trained
end-to-end, with no need to train separate feature transfer functions. This is
realized by supplying the decoder with information about a target pose and the
use of a multi-task loss that penalizes category- and pose-mismatches. In
result, FATTEN discourages discontinuous or non-smooth trajectories that fail
to capture the structure of the pose manifold, and generalizes well on object
recognition tasks involving large pose variation. Experimental results on the
artificial ModelNet database show that it can successfully learn to map source
features to target features of a desired pose, while preserving class identity.
Most notably, by using feature space transfer for data augmentation (w.r.t.
pose and depth) on SUN-RGBD objects, we demonstrate considerable performance
improvements on one/few-shot object recognition in a transfer learning setup,
compared to current state-of-the-art methods.
Learning random-walk label propagation for weakly-supervised semantic segmentation
Authors: Paul Vernaza, Manmohan Chandraker
Date: 2018-02-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Large-scale training for semantic segmentation is challenging due to the
expense of obtaining training data for this task relative to other vision
tasks. We propose a novel training approach to address this difficulty. Given
cheaply-obtained sparse image labelings, we propagate the sparse labels to
produce guessed dense labelings. A standard CNN-based segmentation network is
trained to mimic these labelings. The label-propagation process is defined via
random-walk hitting probabilities, which leads to a differentiable
parameterization with uncertainty estimates that are incorporated into our
loss. We show that by learning the label-propagator jointly with the
segmentation predictor, we are able to effectively learn semantic edges given
no direct edge supervision. Experiments also show that training a segmentation
network in this way outperforms the naive approach.
Nearly Optimal Dynamic $k$-Means Clustering for High-Dimensional Data
Authors: Wei Hu, Zhao Song, Lin F. Yang, Peilin Zhong
Date: 2018-02-01 00:00:00
Tasks: Clustering, Vocal Bursts Intensity Prediction

We consider the $k$-means clustering problem in the dynamic streaming
setting, where points from a discrete Euclidean space $\{1, 2, \ldots,
\Delta\}^d$ can be dynamically inserted to or deleted from the dataset. For
this problem, we provide a one-pass coreset construction algorithm using space
$\tilde{O}(k\cdot \mathrm{poly}(d, \log\Delta))$, where $k$ is the target
number of centers. To our knowledge, this is the first dynamic geometric data
stream algorithm for $k$-means using space polynomial in dimension and nearly
optimal (linear) in $k$.
DensePose: Dense Human Pose Estimation In The Wild
Authors: Riza Alp Güler, Natalia Neverova, Iasonas Kokkinos
Date: 2018-02-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Human Pose Estimation, Monocular 3D Human Pose Estimation, Pose Estimation

In this work, we establish dense correspondences between RGB image and a
surface-based representation of the human body, a task we refer to as dense
human pose estimation. We first gather dense correspondences for 50K persons
appearing in the COCO dataset by introducing an efficient annotation pipeline.
We then use our dataset to train CNN-based systems that deliver dense
correspondence 'in the wild', namely in the presence of background, occlusions
and scale variations. We improve our training set's effectiveness by training
an 'inpainting' network that can fill in missing groundtruth values and report
clear improvements with respect to the best results that would be achievable in
the past. We experiment with fully-convolutional networks and region-based
models and observe a superiority of the latter; we further improve accuracy
through cascading, obtaining a system that delivers highly0accurate results in
real time. Supplementary materials and videos are provided on the project page
http://densepose.org
Linearized Binary Regression
Authors: Andrew S. Lan, Mung Chiang, Christoph Studer
Date: 2018-02-01 00:00:00
Tasks: regression

Probit regression was first proposed by Bliss in 1934 to study mortality
rates of insects. Since then, an extensive body of work has analyzed and used
probit or related binary regression methods (such as logistic regression) in
numerous applications and fields. This paper provides a fresh angle to such
well-established binary regression methods. Concretely, we demonstrate that
linearizing the probit model in combination with linear estimators performs on
par with state-of-the-art nonlinear regression methods, such as posterior mean
or maximum aposteriori estimation, for a broad range of real-world regression
problems. We derive exact, closed-form, and nonasymptotic expressions for the
mean-squared error of our linearized estimators, which clearly separates them
from nonlinear regression methods that are typically difficult to analyze. We
showcase the efficacy of our methods and results for a number of synthetic and
real-world datasets, which demonstrates that linearized binary regression finds
potential use in a variety of inference, estimation, signal processing, and
machine learning applications that deal with binary-valued observations or
measurements.
Dense 3D Object Reconstruction from a Single Depth View
Authors: Bo Yang, Stefano Rosa, Andrew Markham, Niki Trigoni, Hongkai Wen
Date: 2018-02-01 00:00:00
Tasks: 3D geometry, 3D Object Reconstruction, Object, Object Reconstruction

In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs
the complete 3D structure of a given object from a single arbitrary depth view
using generative adversarial networks. Unlike existing work which typically
requires multiple views of the same object or class labels to recover the full
3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation
of a depth view of the object as input, and is able to generate the complete 3D
occupancy grid with a high resolution of 256^3 by recovering the
occluded/missing regions. The key idea is to combine the generative
capabilities of autoencoders and the conditional Generative Adversarial
Networks (GAN) framework, to infer accurate and fine-grained 3D structures of
objects in high-dimensional voxel space. Extensive experiments on large
synthetic datasets and real-world Kinect datasets show that the proposed
3D-RecGAN++ significantly outperforms the state of the art in single view 3D
object reconstruction, and is able to reconstruct unseen types of objects.
Disunited Nations? A Multiplex Network Approach to Detecting Preference Affinity Blocs using Texts and Votes
Authors: Caleb Pomeroy, Niheer Dasandi, Slava J. Mikhaylov
Date: 2018-02-01 00:00:00
Tasks: Community Detection, Word Embeddings

This paper contributes to an emerging literature that models votes and text in tandem to better understand polarization of expressed preferences. It introduces a new approach to estimate preference polarization in multidimensional settings, such as international relations, based on developments in the natural language processing and network science literatures -- namely word embeddings, which retain valuable syntactical qualities of human language, and community detection in multilayer networks, which locates densely connected actors across multiple, complex networks. We find that the employment of these tools in tandem helps to better estimate states' foreign policy preferences expressed in UN votes and speeches beyond that permitted by votes alone. The utility of these located affinity blocs is demonstrated through an application to conflict onset in International Relations, though these tools will be of interest to all scholars faced with the measurement of preferences and polarization in multidimensional settings.
Classifying medical notes into standard disease codes using Machine Learning
Authors: Amitabha Karmakar
Date: 2018-02-01 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

We investigate the automatic classification of patient discharge notes into
standard disease labels. We find that Convolutional Neural Networks with
Attention outperform previous algorithms used in this task, and suggest further
areas for improvement.
Eigen-Distortions of Hierarchical Representations
Authors: Alexander Berardino, Johannes Ballé, Valero Laparra, Eero P. Simoncelli
Date: 2017-10-06 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Object Recognition, Sensitivity

We develop a method for comparing hierarchical image representations in terms
of their ability to explain perceptual sensitivity in humans. Specifically, we
utilize Fisher information to establish a model-derived prediction of
sensitivity to local perturbations of an image. For a given image, we compute
the eigenvectors of the Fisher information matrix with largest and smallest
eigenvalues, corresponding to the model-predicted most- and least-noticeable
image distortions, respectively. For human subjects, we then measure the amount
of each distortion that can be reliably detected when added to the image. We
use this method to test the ability of a variety of representations to mimic
human perceptual sensitivity. We find that the early layers of VGG16, a deep
neural network optimized for object recognition, provide a better match to
human perception than later layers, and a better match than a 4-stage
convolutional neural network (CNN) trained on a database of human ratings of
distorted image quality. On the other hand, we find that simple models of early
visual processing, incorporating one or more stages of local gain control,
trained on the same database of distortion ratings, provide substantially
better predictions of human sensitivity than either the CNN, or any combination
of layers of VGG16.
Online and Stable Learning of Analysis Operators
Authors: Michael Sandbichler, Karin Schnass
Date: 2017-04-01 00:00:00
Tasks: Denoising, Operator learning

In this paper four iterative algorithms for learning analysis operators are
presented. They are built upon the same optimisation principle underlying both
Analysis K-SVD and Analysis SimCO. The Forward and Sequential Analysis Operator
Learning (FAOL and SAOL) algorithms are based on projected gradient descent
with optimally chosen step size. The Implicit AOL (IAOL) algorithm is inspired
by the implicit Euler scheme for solving ordinary differential equations and
does not require to choose a step size. The fourth algorithm, Singular Value
AOL (SVAOL), uses a similar strategy as Analysis K-SVD while avoiding its high
computational cost. All algorithms are proven to decrease or preserve the
target function in each step and a characterisation of their stationary points
is provided. Further they are tested on synthetic and image data, compared to
Analysis SimCO and found to give better recovery rates and faster decay of the
objective function respectively. In a final denoising experiment the presented
algorithms are again shown to perform similar to or better than the
state-of-the-art algorithm ASimCO.
Automatic Prediction of Discourse Connectives
Authors: Eric Malmi, Daniele Pighin, Sebastian Krause, Mikhail Kozhevnikov
Date: 2017-02-03 00:00:00
Proceeding: LREC 2018 5
Tasks: Prediction, Sentence

Accurate prediction of suitable discourse connectives (however, furthermore,
etc.) is a key component of any system aimed at building coherent and fluent
discourses from shorter sentences and passages. As an example, a dialog system
might assemble a long and informative answer by sampling passages extracted
from different documents retrieved from the Web. We formulate the task of
discourse connective prediction and release a dataset of 2.9M sentence pairs
separated by discourse connectives for this task. Then, we evaluate the
hardness of the task for human raters, apply a recently proposed decomposable
attention (DA) model to this task and observe that the automatic predictor has
a higher F1 than human raters (32 vs. 30). Nevertheless, under specific
conditions the raters still outperform the DA model, suggesting that there is
headroom for future improvements.
Machine Translation using Semantic Web Technologies: A Survey
Authors: Diego Moussallem, Matthias Wauer, Axel-Cyrille Ngonga Ngomo
Date: 2017-11-26 00:00:00
Tasks: Machine Translation, Survey, Translation

A large number of machine translation approaches have recently been developed
to facilitate the fluid migration of content across languages. However, the
literature suggests that many obstacles must still be dealt with to achieve
better automatic translations. One of these obstacles is lexical and syntactic
ambiguity. A promising way of overcoming this problem is using Semantic Web
technologies. This article presents the results of a systematic review of
machine translation approaches that rely on Semantic Web technologies for
translating texts. Overall, our survey suggests that while Semantic Web
technologies can enhance the quality of machine translation outputs for various
problems, the combination of both is still in its infancy.
Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach
Authors: Pierre Courtiol, Eric W. Tramel, Marc Sanselme, Gilles Wainrib
Date: 2018-02-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification, Multiple Instance Learning, object-detection, Object Detection, Semantic Segmentation, Weakly-supervised Learning, whole slide images

Analysis of histopathology slides is a critical step for many diagnoses, and in particular in oncology where it defines the gold standard. In the case of digital histopathological analysis, highly trained pathologists must review vast whole-slide-images of extreme digital resolution ($100,000^2$ pixels) across multiple zoom levels in order to locate abnormal regions of cells, or in some cases single cells, out of millions. The application of deep learning to this problem is hampered not only by small sample sizes, as typical datasets contain only a few hundred samples, but also by the generation of ground-truth localized annotations for training interpretable classification and segmentation models. We propose a method for disease localization in the context of weakly supervised learning, where only image-level labels are available during training. Even without pixel-level annotations, we are able to demonstrate performance comparable with models trained with strong annotations on the Camelyon-16 lymph node metastases detection challenge. We accomplish this through the use of pre-trained deep convolutional networks, feature embedding, as well as learning via top instances and negative evidence, a multiple instance learning technique from the field of semantic segmentation and object detection.
Elements of Effective Deep Reinforcement Learning towards Tactical Driving Decision Making
Authors: Jingchu Liu, Pengfei Hou, Lisen Mu, Yinan Yu, Chang Huang
Date: 2018-02-01 00:00:00
Tasks: Autonomous Driving, Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Tactical driving decision making is crucial for autonomous driving systems
and has attracted considerable interest in recent years. In this paper, we
propose several practical components that can speed up deep reinforcement
learning algorithms towards tactical decision making tasks: 1) non-uniform
action skipping as a more stable alternative to action-repetition frame
skipping, 2) a counter-based penalty for lanes on which ego vehicle has less
right-of-road, and 3) heuristic inference-time action masking for apparently
undesirable actions. We evaluate the proposed components in a realistic driving
simulator and compare them with several baselines. Results show that the
proposed scheme provides superior performance in terms of safety, efficiency,
and comfort.