pwc-archive/papers-with-abstracts huggingface dataset:
Building Models for Biopathway Dynamics Using Intrinsic Dimensionality Analysis
Authors: Emilia M. Wysocka, Valery Dzutsati, Tirthankar Bandyopadhyay, Laura Condon, Sahil Garg
Date: 2018-04-29 00:00:00
Tasks: Dimensionality Reduction, Time Series, Time Series Analysis

An important task for many if not all the scientific domains is efficient
knowledge integration, testing and codification. It is often solved with model
construction in a controllable computational environment. In spite of that, the
throughput of in-silico simulation-based observations become similarly
intractable for thorough analysis. This is especially the case in molecular
biology, which served as a subject for this study. In this project, we aimed to
test some approaches developed to deal with the curse of dimensionality. Among
these we found dimension reduction techniques especially appealing. They can be
used to identify irrelevant variability and help to understand critical
processes underlying high-dimensional datasets. Additionally, we subjected our
data sets to nonlinear time series analysis, as those are well established
methods for results comparison. To investigate the usefulness of dimension
reduction methods, we decided to base our study on a concrete sample set. The
example was taken from the domain of systems biology concerning dynamic
evolution of sub-cellular signaling. Particularly, the dataset relates to the
yeast pheromone pathway and is studied in-silico with a stochastic model. The
model reconstructs signal propagation stimulated by a mating pheromone. In the
paper, we elaborate on the reason of multidimensional analysis problem in the
context of molecular signaling, and next, we introduce the model of choice,
simulation details and obtained time series dynamics. A description of used
methods followed by a discussion of results and their biological interpretation
finalize the paper.
Precision Medicine as an Accelerator for Next Generation Cognitive Supercomputing
Authors: Edmon Begoli, Jim Brase, Bambi DeLaRosa, Penelope Jones, Dimitri Kusnezov, Jason Paragas, Rick Stevens, Fred Streitz, Georgia Tourassi
Date: 2018-04-29 00:00:00

In the past several years, we have taken advantage of a number of
opportunities to advance the intersection of next generation high-performance
computing AI and big data technologies through partnerships in precision
medicine. Today we are in the throes of piecing together what is likely the
most unique convergence of medical data and computer technologies. But more
deeply, we observe that the traditional paradigm of computer simulation and
prediction needs fundamental revision. This is the time for a number of
reasons. We will review what the drivers are, why now, how this has been
approached over the past several years, and where we are heading.
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network
Authors: Hongming Shan, Yi Zhang, Qingsong Yang, Uwe Kruger, Mannudeep K. Kalra, Ling Sun, Wenxiang Cong, Ge Wang
Date: 2018-02-15 00:00:00
Tasks: Computed Tomography (CT), Decoder, Denoising, Diagnostic, Generative Adversarial Network, Transfer Learning

Low-dose computed tomography (CT) has attracted a major attention in the
medical imaging field, since CT-associated x-ray radiation carries health risks
for patients. The reduction of CT radiation dose, however, compromises the
signal-to-noise ratio, and may compromise the image quality and the diagnostic
performance. Recently, deep-learning-based algorithms have achieved promising
results in low-dose CT denoising, especially convolutional neural network (CNN)
and generative adversarial network (GAN). This article introduces a Contracting
Path-based Convolutional Encoder-decoder (CPCE) network in 2D and 3D
configurations within the GAN framework for low-dose CT denoising. A novel
feature of our approach is that an initial 3D CPCE denoising model can be
directly obtained by extending a trained 2D CNN and then fine-tuned to
incorporate 3D spatial information from adjacent slices. Based on the transfer
learning from 2D to 3D, the 3D network converges faster and achieves a better
denoising performance than that trained from scratch. By comparing the CPCE
with recently published methods based on the simulated Mayo dataset and the
real MGH dataset, we demonstrate that the 3D CPCE denoising model has a better
performance, suppressing image noise and preserving subtle structures.
Semi-parametric Image Synthesis
Authors: Xiaojuan Qi, Qifeng Chen, Jiaya Jia, Vladlen Koltun
Date: 2018-04-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Generation, Image-to-Image Translation, Semantic Segmentation

We present a semi-parametric approach to photographic image synthesis from
semantic layouts. The approach combines the complementary strengths of
parametric and nonparametric techniques. The nonparametric component is a
memory bank of image segments constructed from a training set of images. Given
a novel semantic layout at test time, the memory bank is used to retrieve
photographic references that are provided as source material to a deep network.
The synthesis is performed by a deep network that draws on the provided
photographic material. Experiments on multiple semantic segmentation datasets
show that the presented approach yields considerably more realistic images than
recent purely parametric techniques. The results are shown in the supplementary
video at https://youtu.be/U4Q98lenGLQ
Subspace Support Vector Data Description
Authors: Fahad Sohrab, Jenni Raitoharju, Moncef Gabbouj, Alexandros Iosifidis
Date: 2018-02-12 00:00:00
Tasks: Classification, General Classification, One-Class Classification

This paper proposes a novel method for solving one-class classification
problems. The proposed approach, namely Subspace Support Vector Data
Description, maps the data to a subspace that is optimized for one-class
classification. In that feature space, the optimal hypersphere enclosing the
target class is then determined. The method iteratively optimizes the data
mapping along with data description in order to define a compact class
representation in a low-dimensional feature space. We provide both linear and
non-linear mappings for the proposed method. Experiments on 14 publicly
available datasets indicate that the proposed Subspace Support Vector Data
Description provides better performance compared to baselines and other
recently proposed one-class classification methods.
Unique Information via Dependency Constraints
Authors: Ryan G. James, Jeffrey Emenheiser, James P. Crutchfield
Date: 2017-09-19 00:00:00

The partial information decomposition (PID) is perhaps the leading proposal
for resolving information shared between a set of sources and a target into
redundant, synergistic, and unique constituents. Unfortunately, the PID
framework has been hindered by a lack of a generally agreed-upon, multivariate
method of quantifying the constituents. Here, we take a step toward rectifying
this by developing a decomposition based on a new method that quantifies unique
information. We first develop a broadly applicable method---the dependency
decomposition---that delineates how statistical dependencies influence the
structure of a joint distribution. The dependency decomposition then allows us
to define a measure of the information about a target that can be uniquely
attributed to a particular source as the least amount which the source-target
statistical dependency can influence the information shared between the sources
and the target. The result is the first measure that satisfies the core axioms
of the PID framework while not satisfying the Blackwell relation, which depends
on a particular interpretation of how the variables are related. This makes a
key step forward to a practical PID.
Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers
Authors: Stephan R. Richter, Stefan Roth
Date: 2018-04-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D geometry, 3D Object Reconstruction, 3D Shape Reconstruction

In this paper, we develop novel, efficient 2D encodings for 3D geometry,
which enable reconstructing full 3D shapes from a single image at high
resolution. The key idea is to pose 3D shape reconstruction as a 2D prediction
problem. To that end, we first develop a simple baseline network that predicts
entire voxel tubes at each pixel of a reference view. By leveraging well-proven
architectures for 2D pixel-prediction tasks, we attain state-of-the-art
results, clearly outperforming purely voxel-based approaches. We scale this
baseline to higher resolutions by proposing a memory-efficient shape encoding,
which recursively decomposes a 3D shape into nested shape layers, similar to
the pieces of a Matryoshka doll. This allows reconstructing highly detailed
shapes with complex topology, as demonstrated in extensive experiments; we
clearly outperform previous octree-based approaches despite having a much
simpler architecture using standard network components. Our Matryoshka networks
further enable reconstructing shapes from IDs or shape similarity, as well as
shape sampling.
From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction
Authors: Zihang Dai, Qizhe Xie, Eduard Hovy
Date: 2018-04-29 00:00:00
Proceeding: ACL 2018 7
Tasks: Prediction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this work, we study the credit assignment problem in reward augmented
maximum likelihood (RAML) learning, and establish a theoretical equivalence
between the token-level counterpart of RAML and the entropy regularized
reinforcement learning. Inspired by the connection, we propose two sequence
prediction algorithms, one extending RAML with fine-grained credit assignment
and the other improving Actor-Critic with a systematic entropy regularization.
On two benchmark datasets, we show the proposed algorithms outperform RAML and
Actor-Critic respectively, providing new alternatives to sequence prediction.
Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata
Authors: Lucie-Aimée Kaffee, Hady Elsahar, Pavlos Vougiouklis, Christophe Gravier, Frédérique Laforest, Jonathon Hare, Elena Simperl
Date: 2018-03-19 00:00:00
Proceeding: NAACL 2018 6
Tasks: Sentence

While Wikipedia exists in 287 languages, its content is unevenly distributed
among them. In this work, we investigate the generation of open domain
Wikipedia summaries in underserved languages using structured data from
Wikidata. To this end, we propose a neural network architecture equipped with
copy actions that learns to generate single-sentence and comprehensible textual
summaries from Wikidata triples. We demonstrate the effectiveness of the
proposed approach by evaluating it against a set of baselines on two languages
of different natures: Arabic, a morphological rich language with a larger
vocabulary than English, and Esperanto, a constructed language known for its
easy acquisition.
Simultaneous Parameter Learning and Bi-Clustering for Multi-Response Models
Authors: Ming Yu, Karthikeyan Natesan Ramamurthy, Addie Thompson, Aurélie Lozano
Date: 2018-04-29 00:00:00
Tasks: Clustering, parameter estimation

We consider multi-response and multitask regression models, where the
parameter matrix to be estimated is expected to have an unknown grouping
structure. The groupings can be along tasks, or features, or both, the last one
indicating a bi-cluster or "checkerboard" structure. Discovering this grouping
structure along with parameter inference makes sense in several applications,
such as multi-response Genome-Wide Association Studies. This additional
structure can not only can be leveraged for more accurate parameter estimation,
but it also provides valuable information on the underlying data mechanisms
(e.g. relationships among genotypes and phenotypes in GWAS). In this paper, we
propose two formulations to simultaneously learn the parameter matrix and its
group structures, based on convex regularization penalties. We present
optimization approaches to solve the resulting problems and provide numerical
convergence guarantees. Our approaches are validated on extensive simulations
and real datasets concerning phenotypes and genotypes of plant varieties.
Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming
Authors: Daniel Hein, Steffen Udluft, Thomas A. Runkler
Date: 2018-04-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Autonomously training interpretable control strategies, called policies,
using pre-existing plant trajectory data is of great interest in industrial
applications. Fuzzy controllers have been used in industry for decades as
interpretable and efficient system controllers. In this study, we introduce a
fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning
(FGPRL) that can select the relevant state features, determine the size of the
required fuzzy rule set, and automatically adjust all the controller parameters
simultaneously. Each GP individual's fitness is computed using model-based
batch reinforcement learning (RL), which first trains a model using available
system samples and subsequently performs Monte Carlo rollouts to predict each
policy candidate's performance. We compare FGPRL to an extended version of a
related method called fuzzy particle swarm reinforcement learning (FPSRL),
which uses swarm intelligence to tune the fuzzy policy parameters. Experiments
using an industrial benchmark show that FGPRL is able to autonomously learn
interpretable fuzzy policies with high control performance.
Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates
Authors: Taku Kudo
Date: 2018-04-29 00:00:00
Proceeding: ACL 2018 7
Tasks: Language Modeling, Language Modelling, Machine Translation, NMT, Segmentation, Translation

Subword units are an effective way to alleviate the open vocabulary problems
in neural machine translation (NMT). While sentences are usually converted into
unique subword sequences, subword segmentation is potentially ambiguous and
multiple segmentations are possible even with the same vocabulary. The question
addressed in this paper is whether it is possible to harness the segmentation
ambiguity as a noise to improve the robustness of NMT. We present a simple
regularization method, subword regularization, which trains the model with
multiple subword segmentations probabilistically sampled during training. In
addition, for better subword sampling, we propose a new subword segmentation
algorithm based on a unigram language model. We experiment with multiple
corpora and report consistent improvements especially on low resource and
out-of-domain settings.
Learning Data Dependency with Communication Cost
Authors: Hyeryung Jang, HyungSeok Song, Yung Yi
Date: 2018-04-29 00:00:00

In this paper, we consider the problem of recovering a graph that represents
the statistical data dependency among nodes for a set of data samples generated
by nodes, which provides the basic structure to perform an inference task, such
as MAP (maximum a posteriori). This problem is referred to as structure
learning. When nodes are spatially separated in different locations, running an
inference algorithm requires a non-negligible amount of message passing,
incurring some communication cost. We inevitably have the trade-off between the
accuracy of structure learning and the cost we need to pay to perform a given
message-passing based inference task because the learnt edge structures of data
dependency and physical connectivity graph are often highly different. In this
paper, we formalize this trade-off in an optimization problem which outputs the
data dependency graph that jointly considers learning accuracy and
message-passing costs. We focus on a distributed MAP as the target inference
task, and consider two different implementations, ASYNC-MAP and SYNC-MAP that
have different message-passing mechanisms and thus different cost structures.
In ASYNC- MAP, we propose a polynomial time learning algorithm that is optimal,
motivated by the problem of finding a maximum weight spanning tree. In
SYNC-MAP, we first prove that it is NP-hard and propose a greedy heuristic. For
both implementations, we then quantify how the probability that the resulting
data graphs from those learning algorithms differ from the ideal data graph
decays as the number of data samples grows, using the large deviation
principle, where the decaying rate is characterized by some topological
structures of both original data dependency and physical connectivity graphs as
well as the degree of the trade-off. We validate our theoretical findings
through extensive simulations, which confirms that it has a good match.
OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction
Authors: Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf
Date: 2018-04-29 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Motivation: Ontologies are widely used in biology for data annotation,
integration, and analysis. In addition to formally structured axioms,
ontologies contain meta-data in the form of annotation axioms which provide
valuable pieces of information that characterize ontology classes. Annotations
commonly used in ontologies include class labels, descriptions, or synonyms.
Despite being a rich source of semantic information, the ontology meta-data are
generally unexploited by ontology-based analysis methods such as semantic
similarity measures. Results: We propose a novel method, OPA2Vec, to generate
vector representations of biological entities in ontologies by combining formal
ontology axioms and annotation axioms from the ontology meta-data. We apply a
Word2Vec model that has been pre-trained on PubMed abstracts to produce feature
vectors from our collected data. We validate our method in two different ways:
first, we use the obtained vector representations of proteins as a similarity
measure to predict protein-protein interaction (PPI) on two different datasets.
Second, we evaluate our method on predicting gene-disease associations based on
phenotype similarity by generating vector representations of genes and diseases
using a phenotype ontology, and applying the obtained vectors to predict
gene-disease associations. These two experiments are just an illustration of
the possible applications of our method. OPA2Vec can be used to produce vector
representations of any biomedical entity given any type of biomedical ontology.
Availability: https://github.com/bio-ontology-research-group/opa2vec Contact:
robert.hoehndorf@kaust.edu.sa and xin.gao@kaust.edu.sa.
Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss
Authors: Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Pheng-Ann Heng
Date: 2018-04-29 00:00:00
Tasks: Domain Adaptation, Transfer Learning, Unsupervised Domain Adaptation

Convolutional networks (ConvNets) have achieved great successes in various
challenging vision tasks. However, the performance of ConvNets would degrade
when encountering the domain shift. The domain adaptation is more significant
while challenging in the field of biomedical image analysis, where
cross-modality data have largely different distributions. Given that annotating
the medical data is especially expensive, the supervised transfer learning
approaches are not quite optimal. In this paper, we propose an unsupervised
domain adaptation framework with adversarial learning for cross-modality
biomedical image segmentations. Specifically, our model is based on a dilated
fully convolutional network for pixel-wise prediction. Moreover, we build a
plug-and-play domain adaptation module (DAM) to map the target input to
features which are aligned with source domain feature space. A domain critic
module (DCM) is set up for discriminating the feature space of both domains. We
optimize the DAM and DCM via an adversarial loss without using any target
domain label. Our proposed method is validated by adapting a ConvNet trained
with MRI images to unpaired CT data for cardiac structures segmentations, and
achieved very promising results.
An Investigation on Support Vector Clustering for Big Data in Quantum Paradigm
Authors: Arit Kumar Bishwas, Ashish Mani, Vasile Palade
Date: 2018-04-29 00:00:00
Tasks: Clustering

The support vector clustering algorithm is a well-known clustering algorithm based on support vector machines using Gaussian or polynomial kernels. The classical support vector clustering algorithm works well in general, but its performance degrades when applied on big data. In this paper, we have investigated the performance of support vector clustering algorithm implemented in a quantum paradigm for possible run-time improvements. We have developed and analyzed a quantum version of the support vector clustering algorithm. The proposed approach is based on the quantum support vector machine and quantum kernels (i.e., Gaussian and polynomial). The proposed quantum version of the SVM clustering method demonstrates a significant speed-up gain on the overall run-time complexity as compared to the classical counterpart.
Crawling in Rogue's dungeons with (partitioned) A3C
Authors: Andrea Asperti, Daniele Cortesi, Francesco Sovrano
Date: 2018-04-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Rogue is a famous dungeon-crawling video-game of the 80ies, the ancestor of
its gender. Rogue-like games are known for the necessity to explore partially
observable and always different randomly-generated labyrinths, preventing any
form of level replay. As such, they serve as a very natural and challenging
task for reinforcement learning, requiring the acquisition of complex,
non-reactive behaviors involving memory and planning. In this article we show
how, exploiting a version of A3C partitioned on different situations, the agent
is able to reach the stairs and descend to the next level in 98% of cases.
Learning Generalized Reactive Policies using Deep Neural Networks
Authors: Edward Groshev, Maxwell Goldstein, Aviv Tamar, Siddharth Srivastava, Pieter Abbeel
Date: 2017-08-24 00:00:00
Tasks: Decision Making, feature selection

We present a new approach to learning for planning, where knowledge acquired
while solving a given set of planning problems is used to plan faster in
related, but new problem instances. We show that a deep neural network can be
used to learn and represent a \emph{generalized reactive policy} (GRP) that
maps a problem instance and a state to an action, and that the learned GRPs
efficiently solve large classes of challenging problem instances. In contrast
to prior efforts in this direction, our approach significantly reduces the
dependence of learning on handcrafted domain knowledge or feature selection.
Instead, the GRP is trained from scratch using a set of successful execution
traces. We show that our approach can also be used to automatically learn a
heuristic function that can be used in directed search algorithms. We evaluate
our approach using an extensive suite of experiments on two challenging
planning problem domains and show that our approach facilitates learning
complex decision making policies and powerful heuristic functions with minimal
human input. Videos of our results are available at goo.gl/Hpy4e3.