pwc-archive/papers-with-abstracts huggingface dataset:
DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks
Authors: Mostafa Karimi, Di wu, Zhangyang Wang, Yang shen
Date: 2018-06-20 00:00:00
Tasks: Drug Discovery, Transfer Learning

Motivation: Drug discovery demands rapid quantification of compound-protein
interaction (CPI). However, there is a lack of methods that can predict
compound-protein affinity from sequences alone with high applicability,
accuracy, and interpretability.
  Results: We present a seamless integration of domain knowledges and
learning-based approaches. Under novel representations of
structurally-annotated protein sequences, a semi-supervised deep learning model
that unifies recurrent and convolutional neural networks has been proposed to
exploit both unlabeled and labeled data, for jointly encoding molecular
representations and predicting affinities. Our representations and models
outperform conventional options in achieving relative error in IC$_{50}$ within
5-fold for test cases and 20-fold for protein classes not included for
training. Performances for new protein classes with few labeled data are
further improved by transfer learning. Furthermore, separate and joint
attention mechanisms are developed and embedded to our model to add to its
interpretability, as illustrated in case studies for predicting and explaining
selective drug-target interactions. Lastly, alternative representations using
protein sequences or compound graphs and a unified RNN/GCNN-CNN model using
graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead.
  Availability: Data and source codes are available at
https://github.com/Shen-Lab/DeepAffinity
  Supplementary Information: Supplementary data are available at
http://shen-lab.github.io/deep-affinity-bioinf18-supp-rev.pdf
Jack the Reader - A Machine Reading Framework
Authors: Dirk Weissenborn, Pasquale Minervini, Tim Dettmers, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bošnjak, Jeff Mitchell, Thomas Demeester, Pontus Stenetorp, Sebastian Riedel
Date: 2018-06-20 00:00:00
Tasks: Articles, Link Prediction, Natural Language Inference, Natural Language Understanding, Question Answering, Reading Comprehension

Many Machine Reading and Natural Language Understanding tasks require reading
supporting text in order to answer questions. For example, in Question
Answering, the supporting text can be newswire or Wikipedia articles; in
Natural Language Inference, premises can be seen as the supporting text and
hypotheses as questions. Providing a set of useful primitives operating in a
single framework of related tasks would allow for expressive modelling, and
easier model comparison and replication. To that end, we present Jack the
Reader (Jack), a framework for Machine Reading that allows for quick model
prototyping by component reuse, evaluation of new models on existing datasets
as well as integrating new datasets and applying them on a growing set of
implemented baseline models. Jack is currently supporting (but not limited to)
three tasks: Question Answering, Natural Language Inference, and Link
Prediction. It is developed with the aim of increasing research efficiency and
code reuse.
Francy - An Interactive Discrete Mathematics Framework for GAP
Authors: Manuel Machado Martins, Markus Pfeiffer
Date: 2018-06-19 00:00:00
Tasks: Data Visualization

Data visualization and interaction with large data sets is known to be
essential and critical in many businesses today, and the same applies to
research and teaching, in this case, when exploring large and complex
mathematical objects. GAP is a computer algebra system for computational
discrete algebra with an emphasis on computational group theory. The existing
XGAP package for GAP works exclusively on the X Window System. It lacks
abstraction between its mathematical and graphical cores, making it difficult
to extend, maintain, or port. In this paper, we present Francy, a graphical
semantics package for GAP. Francy is responsible for creating a
representational structure that can be rendered using many GUI frameworks
independent from any particular programming language or operating system.
Building on this, we use state of the art web technologies that take advantage
of an improved REPL environment, which is currently under development for GAP.
The integration of this project with Jupyter provides a rich graphical
environment full of features enhancing the usability and accessibility of GAP.
DARTS: Differentiable Architecture Search
Authors: Hanxiao Liu, Karen Simonyan, Yiming Yang
Date: 2018-06-24 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification, image-classification, Image Classification, Language Modeling, Language Modelling, Neural Architecture Search, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper addresses the scalability challenge of architecture search by
formulating the task in a differentiable manner. Unlike conventional approaches
of applying evolution or reinforcement learning over a discrete and
non-differentiable search space, our method is based on the continuous
relaxation of the architecture representation, allowing efficient search of the
architecture using gradient descent. Extensive experiments on CIFAR-10,
ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in
discovering high-performance convolutional architectures for image
classification and recurrent architectures for language modeling, while being
orders of magnitude faster than state-of-the-art non-differentiable techniques.
Our implementation has been made publicly available to facilitate further
research on efficient architecture search algorithms.
A Machine-learning framework for automatic reference-free quality assessment in MRI
Authors: Thomas Küstner, Sergios Gatidis, Annika Liebgott, Martin Schwartz, Lukas Mauch, Petros Martirosian, Holger Schmidt, Nina F. Schwenzer, Konstantin Nikolaou, Fabian Bamberg, Bin Yang, Fritz Schick
Date: 2018-06-25 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, Diagnostic, Image Quality Assessment

Magnetic resonance (MR) imaging offers a wide variety of imaging techniques.
A large amount of data is created per examination which needs to be checked for
sufficient quality in order to derive a meaningful diagnosis. This is a manual
process and therefore time- and cost-intensive. Any imaging artifacts
originating from scanner hardware, signal processing or induced by the patient
may reduce the image quality and complicate the diagnosis or any image
post-processing. Therefore, the assessment or the ensurance of sufficient image
quality in an automated manner is of high interest. Usually no reference image
is available or difficult to define. Therefore, classical reference-based
approaches are not applicable. Model observers mimicking the human observers
(HO) can assist in this task. Thus, we propose a new machine-learning-based
reference-free MR image quality assessment framework which is trained on
HO-derived labels to assess MR image quality immediately after each
acquisition. We include the concept of active learning and present an efficient
blinded reading platform to reduce the effort in the HO labeling procedure.
Derived image features and the applied classifiers (support-vector-machine,
deep neural network) are investigated for a cohort of 250 patients. The MR
image quality assessment framework can achieve a high test accuracy of 93.7$\%$
for estimating quality classes on a 5-point Likert-scale. The proposed MR image
quality assessment framework is able to provide an accurate and efficient
quality estimation which can be used as a prospective quality assurance
including automatic acquisition adaptation or guided MR scanner operation,
and/or as a retrospective quality assessment including support of diagnostic
decisions or quality control in cohort studies.
Stochastic natural gradient descent draws posterior samples in function space
Authors: Samuel L. Smith, Daniel Duckworth, Semon Rezchikov, Quoc V. Le, Jascha Sohl-Dickstein
Date: 2018-06-25 00:00:00
Tasks: valid

Recent work has argued that stochastic gradient descent can approximate the
Bayesian uncertainty in model parameters near local minima. In this work we
develop a similar correspondence for minibatch natural gradient descent (NGD).
We prove that for sufficiently small learning rates, if the model predictions
on the training set approach the true conditional distribution of labels given
inputs, the stationary distribution of minibatch NGD approaches a Bayesian
posterior near local minima. The temperature $T = \epsilon N / (2B)$ is
controlled by the learning rate $\epsilon$, training set size $N$ and batch
size $B$. However minibatch NGD is not parameterisation invariant and it does
not sample a valid posterior away from local minima. We therefore propose a
novel optimiser, "stochastic NGD", which introduces the additional correction
terms required to preserve both properties.
Tracking Emerges by Colorizing Videos
Authors: Carl Vondrick, Abhinav Shrivastava, Alireza Fathi, Sergio Guadarrama, Kevin Murphy
Date: 2018-06-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: Colorization, Optical Flow Estimation, Skeleton Based Action Recognition, Visual Tracking

We use large amounts of unlabeled video to learn models for visual tracking
without manual human supervision. We leverage the natural temporal coherency of
color to create a model that learns to colorize gray-scale videos by copying
colors from a reference frame. Quantitative and qualitative experiments suggest
that this task causes the model to automatically learn to track visual regions.
Although the model is trained without any ground-truth labels, our method
learns to track well enough to outperform the latest methods based on optical
flow. Moreover, our results suggest that failures to track are correlated with
failures to colorize, indicating that advancing video colorization may further
improve self-supervised visual tracking.
Fundamental limits of detection in the spiked Wigner model
Authors: Ahmed El Alaoui, Florent Krzakala, Michael. I. Jordan
Date: 2018-06-25 00:00:00

We study the fundamental limits of detecting the presence of an additive
rank-one perturbation, or spike, to a Wigner matrix. When the spike comes from
a prior that is i.i.d. across coordinates, we prove that the log-likelihood
ratio of the spiked model against the non-spiked one is asymptotically normal
below a certain reconstruction threshold which is not necessarily of a
"spectral" nature, and that it is degenerate above. This establishes the
maximal region of contiguity between the planted and null models. It is known
that this threshold also marks a phase transition for estimating the spike: the
latter task is possible above the threshold and impossible below. Therefore,
both estimation and detection undergo the same transition in this random matrix
model. We also provide further information about the performance of the optimal
test. Our proofs are based on Gaussian interpolation methods and a rigorous
incarnation of the cavity method, as devised by Guerra and Talagrand in their
study of the Sherrington--Kirkpatrick spin-glass model.
Learning Single-Image Depth from Videos using Quality Assessment Networks
Authors: Weifeng Chen, Shengyi Qian, Jia Deng
Date: 2018-06-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Depth Estimation

Depth estimation from a single image in the wild remains a challenging
problem. One main obstacle is the lack of high-quality training data for images
in the wild. In this paper we propose a method to automatically generate such
data through Structure-from-Motion (SfM) on Internet videos. The core of this
method is a Quality Assessment Network that identifies high-quality
reconstructions obtained from SfM. Using this method, we collect single-view
depth training data from a large number of YouTube videos and construct a new
dataset called YouTube3D. Experiments show that YouTube3D is useful in training
depth estimation networks and advances the state of the art of single-view
depth estimation in the wild.
Asymptotic Properties of Recursive Maximum Likelihood Estimation in Non-Linear State-Space Models
Authors: Vladislav Z. B. Tadic, Arnaud Doucet
Date: 2018-06-25 00:00:00
Tasks: State Space Models

Using stochastic gradient search and the optimal filter derivative, it is possible to perform recursive (i.e., online) maximum likelihood estimation in a non-linear state-space model. As the optimal filter and its derivative are analytically intractable for such a model, they need to be approximated numerically. In [Poyiadjis, Doucet and Singh, Biometrika 2018], a recursive maximum likelihood algorithm based on a particle approximation to the optimal filter derivative has been proposed and studied through numerical simulations. Here, this algorithm and its asymptotic behavior are analyzed theoretically. We show that the algorithm accurately estimates maxima to the underlying (average) log-likelihood when the number of particles is sufficiently large. We also derive (relatively) tight bounds on the estimation error. The obtained results hold under (relatively) mild conditions and cover several classes of non-linear state-space models met in practice.
IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation
Authors: Shuo Liu, Vijay John, Erik Blasch, Zheng Liu, Ying Huang
Date: 2018-06-25 00:00:00
Tasks: Translation

Context enhancement is critical for night vision (NV) applications,
especially for the dark night situation without any artificial lights. In this
paper, we present the infrared-to-visual (IR2VI) algorithm, a novel
unsupervised thermal-to-visible image translation framework based on generative
adversarial networks (GANs). IR2VI is able to learn the intrinsic
characteristics from VI images and integrate them into IR images. Since the
existing unsupervised GAN-based image translation approaches face several
challenges, such as incorrect mapping and lack of fine details, we propose a
structure connection module and a region-of-interest (ROI) focal loss method to
address the current limitations. Experimental results show the superiority of
the IR2VI algorithm over baseline methods.
Inference Trees: Adaptive Inference with Exploration
Authors: Tom Rainforth, Yuan Zhou, Xiaoyu Lu, Yee Whye Teh, Frank Wood, Hongseok Yang, Jan-Willem van de Meent
Date: 2018-06-25 00:00:00

We introduce inference trees (ITs), a new class of inference methods that
build on ideas from Monte Carlo tree search to perform adaptive sampling in a
manner that balances exploration with exploitation, ensures consistency, and
alleviates pathologies in existing adaptive methods. ITs adaptively sample from
hierarchical partitions of the parameter space, while simultaneously learning
these partitions in an online manner. This enables ITs to not only identify
regions of high posterior mass, but also maintain uncertainty estimates to
track regions where significant posterior mass may have been missed. ITs can be
based on any inference method that provides a consistent estimate of the
marginal likelihood. They are particularly effective when combined with
sequential Monte Carlo, where they capture long-range dependencies and yield
improvements beyond proposal adaptation alone.
Learning dynamical systems with particle stochastic approximation EM
Authors: Andreas Lindholm, Fredrik Lindsten
Date: 2018-06-25 00:00:00

We present the particle stochastic approximation EM (PSAEM) algorithm for learning of dynamical systems. The method builds on the EM algorithm, an iterative procedure for maximum likelihood inference in latent variable models. By combining stochastic approximation EM and particle Gibbs with ancestor sampling (PGAS), PSAEM obtains superior computational performance and convergence properties compared to plain particle-smoothing-based approximations of the EM algorithm. PSAEM can be used for plain maximum likelihood inference as well as for empirical Bayes learning of hyperparameters. Specifically, the latter point means that existing PGAS implementations easily can be extended with PSAEM to estimate hyperparameters at almost no extra computational cost. We discuss the convergence properties of the algorithm, and demonstrate it on several signal processing applications.
Towards Optimal Estimation of Bivariate Isotonic Matrices with Unknown Permutations
Authors: Cheng Mao, Ashwin Pananjady, Martin J. Wainwright
Date: 2018-06-25 00:00:00
Tasks: Graphon Estimation

Many applications, including rank aggregation, crowd-labeling, and graphon estimation, can be modeled in terms of a bivariate isotonic matrix with unknown permutations acting on its rows and/or columns. We consider the problem of estimating an unknown matrix in this class, based on noisy observations of (possibly, a subset of) its entries. We design and analyze polynomial-time algorithms that improve upon the state of the art in two distinct metrics, showing, in particular, that minimax optimal, computationally efficient estimation is achievable in certain settings. Along the way, we prove matching upper and lower bounds on the minimax radii of certain cone testing problems, which may be of independent interest.
Mapping Unparalleled Clinical Professional and Consumer Languages with Embedding Alignment
Authors: Wei-Hung Weng, Peter Szolovits
Date: 2018-06-25 00:00:00
Tasks: Retrieval, Word Embeddings

Mapping and translating professional but arcane clinical jargons to consumer
language is essential to improve the patient-clinician communication.
Researchers have used the existing biomedical ontologies and consumer health
vocabulary dictionary to translate between the languages. However, such
approaches are limited by expert efforts to manually build the dictionary,
which is hard to be generalized and scalable. In this work, we utilized the
embeddings alignment method for the word mapping between unparalleled clinical
professional and consumer language embeddings. To map semantically similar
words in two different word embeddings, we first independently trained word
embeddings on both the corpus with abundant clinical professional terms and the
other with mainly healthcare consumer terms. Then, we aligned the embeddings by
the Procrustes algorithm. We also investigated the approach with the
adversarial training with refinement. We evaluated the quality of the alignment
through the similar words retrieval both by computing the model precision and
as well as judging qualitatively by human. We show that the Procrustes
algorithm can be performant for the professional consumer language embeddings
alignment, whereas adversarial training with refinement may find some relations
between two languages.
Pushing the boundaries of parallel Deep Learning -- A practical approach
Authors: Paolo Viviani, Maurizio Drocco, Marco Aldinucci
Date: 2018-06-25 00:00:00
Tasks: Deep Learning

This work aims to assess the state of the art of data parallel deep neural
network training, trying to identify potential research tracks to be exploited
for performance improvement. Beside, it presents a design for a practical C++
library dedicated at implementing and unifying the current state of the art
methodologies for parallel training in a performance-conscious framework,
allowing the user to explore novel strategies without departing significantly
from its usual work-flow.
SkinNet: A Deep Learning Framework for Skin Lesion Segmentation
Authors: Sulaiman Vesal, Nishant Ravikumar, Andreas Maier
Date: 2018-06-25 00:00:00
Tasks: Deep Learning, Lesion Segmentation, Segmentation, Skin Lesion Segmentation

There has been a steady increase in the incidence of skin cancer worldwide,
with a high rate of mortality. Early detection and segmentation of skin lesions
are crucial for timely diagnosis and treatment, necessary to improve the
survival rate of patients. However, skin lesion segmentation is a challenging
task due to the low contrast of lesions and their high similarity in terms of
appearance, to healthy tissue. This underlines the need for an accurate and
automatic approach for skin lesion segmentation. To tackle this issue, we
propose a convolutional neural network (CNN) called SkinNet. The proposed CNN
is a modified version of U-Net. We compared the performance of our approach
with other state-of-the-art techniques, using the ISBI 2017 challenge dataset.
Our approach outperformed the others in terms of the Dice coefficient, Jaccard
index and sensitivity, evaluated on the held-out challenge test data set,
across 5-fold cross validation experiments. SkinNet achieved an average value
of 85.10, 76.67 and 93.0%, for the DC, JI, and SE, respectively.
Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy
Authors: Xingtong Liu, Ayushi Sinha, Mathias Unberath, Masaru Ishii, Gregory Hager, Russell H. Taylor, Austin Reiter
Date: 2018-06-25 00:00:00
Tasks: Anatomy, Depth Estimation, Depth Prediction, Self-Supervised Learning

We present a self-supervised approach to training convolutional neural
networks for dense depth estimation from monocular endoscopy data without a
priori modeling of anatomy or shading. Our method only requires sequential data
from monocular endoscopic videos and a multi-view stereo reconstruction method,
e.g. structure from motion, that supervises learning in a sparse but accurate
manner. Consequently, our method requires neither manual interaction, such as
scaling or labeling, nor patient CT in the training and application phases. We
demonstrate the performance of our method on sinus endoscopy data from two
patients and validate depth prediction quantitatively using corresponding
patient CT scans where we found submillimeter residual errors.
The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systems
Authors: Adaeze Adigwe, Noé Tits, Kevin El Haddad, Sarah Ostadabbas, Thierry Dutoit
Date: 2018-06-25 00:00:00
Tasks: Speech Emotion Recognition, Speech Synthesis, Text-To-Speech Synthesis

In this paper, we present a database of emotional speech intended to be
open-sourced and used for synthesis and generation purpose. It contains data
for male and female actors in English and a male actor in French. The database
covers 5 emotion classes so it could be suitable to build synthesis and voice
transformation systems with the potential to control the emotional dimension in
a continuous way. We show the data's efficiency by building a simple MLP system
converting neutral to angry speech style and evaluate it via a CMOS perception
test. Even though the system is a very simple one, the test show the efficiency
of the data which is promising for future work.
A Hierarchical Deep Learning Natural Language Parser for Fashion
Authors: José Marcelino, João Faria, Luís Baía, Ricardo Gamelas Sousa
Date: 2018-06-25 00:00:00
Tasks: Deep Learning

This work presents a hierarchical deep learning natural language parser for
fashion. Our proposal intends not only to recognize fashion-domain entities but
also to expose syntactic and morphologic insights. We leverage the usage of an
architecture of specialist models, each one for a different task (from parsing
to entity recognition). Such architecture renders a hierarchical model able to
capture the nuances of the fashion language. The natural language parser is
able to deal with textual ambiguities which are left unresolved by our
currently existing solution. Our empirical results establish a robust baseline,
which justifies the use of hierarchical architectures of deep learning models
while opening new research avenues to explore.