pwc-archive/papers-with-abstracts huggingface dataset:
Multimodal Attribute Extraction
Authors: Robert L. Logan IV, Samuel Humeau, Sameer Singh
Date: 2017-11-29 00:00:00
Tasks: Attribute, Attribute Extraction, Multimodal Attribute Value Extraction

The broad goal of information extraction is to derive structured information
from unstructured data. However, most existing methods focus solely on text,
ignoring other types of unstructured data such as images, video and audio which
comprise an increasing portion of the information on the web. To address this
shortcoming, we propose the task of multimodal attribute extraction. Given a
collection of unstructured and semi-structured contextual information about an
entity (such as a textual description, or visual depictions) the task is to
extract the entity's underlying attributes. In this paper, we provide a dataset
containing mixed-media data for over 2 million product items along with 7
million attribute-value pairs describing the items which can be used to train
attribute extractors in a weakly supervised manner. We provide a variety of
baselines which demonstrate the relative effectiveness of the individual modes
of information towards solving the task, as well as study human performance.
Towards Alzheimer's Disease Classification through Transfer Learning
Authors: Marcia Hon, Naimul Khan
Date: 2017-11-29 00:00:00
Tasks: Classification, General Classification, Transfer Learning

Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI
through machine learning have been a subject of intense research in recent
years. Recent success of deep learning in computer vision have progressed such
research further. However, common limitations with such algorithms are reliance
on a large number of training images, and requirement of careful optimization
of the architecture of deep networks. In this paper, we attempt solving these
issues with transfer learning, where state-of-the-art architectures such as VGG
and Inception are initialized with pre-trained weights from large benchmark
datasets consisting of natural images, and the fully-connected layer is
re-trained with only a small number of MRI images. We employ image entropy to
select the most informative slices for training. Through experimentation on the
OASIS MRI dataset, we show that with training size almost 10 times smaller than
the state-of-the-art, we reach comparable or even better performance than
current deep-learning based methods.
The Wisdom of Polarized Crowds
Authors: Feng Shi, Misha Teplitskiy, Eamon Duede, James Evans
Date: 2017-11-29 00:00:00
Tasks: Articles, Misinformation

As political polarization in the United States continues to rise, the
question of whether polarized individuals can fruitfully cooperate becomes
pressing. Although diversity of individual perspectives typically leads to
superior team performance on complex tasks, strong political perspectives have
been associated with conflict, misinformation and a reluctance to engage with
people and perspectives beyond one's echo chamber. It is unclear whether
self-selected teams of politically diverse individuals will create higher or
lower quality outcomes. In this paper, we explore the effect of team political
composition on performance through analysis of millions of edits to Wikipedia's
Political, Social Issues, and Science articles. We measure editors' political
alignments by their contributions to conservative versus liberal articles. A
survey of editors validates that those who primarily edit liberal articles
identify more strongly with the Democratic party and those who edit
conservative ones with the Republican party. Our analysis then reveals that
polarized teams---those consisting of a balanced set of politically diverse
editors---create articles of higher quality than politically homogeneous teams.
The effect appears most strongly in Wikipedia's Political articles, but is also
observed in Social Issues and even Science articles. Analysis of article "talk
pages" reveals that politically polarized teams engage in longer, more
constructive, competitive, and substantively focused but linguistically diverse
debates than political moderates. More intense use of Wikipedia policies by
politically diverse teams suggests institutional design principles to help
unleash the power of politically polarized teams.
Generalized Grounding Graphs: A Probabilistic Framework for Understanding Grounded Commands
Authors: Thomas Kollar, Stefanie Tellex, Matthew Walter, Albert Huang, Abraham Bachrach, Sachi Hemachandra, Emma Brunskill, Ashis Banerjee, Deb Roy, Seth Teller, Nicholas Roy
Date: 2017-11-29 00:00:00
Tasks: Diversity, Language Acquisition

Many task domains require robots to interpret and act upon natural language
commands which are given by people and which refer to the robot's physical
surroundings. Such interpretation is known variously as the symbol grounding
problem, grounded semantics and grounded language acquisition. This problem is
challenging because people employ diverse vocabulary and grammar, and because
robots have substantial uncertainty about the nature and contents of their
surroundings, making it difficult to associate the constitutive language
elements (principally noun phrases and spatial relations) of the command text
to elements of those surroundings. Symbolic models capture linguistic structure
but have not scaled successfully to handle the diverse language produced by
untrained users. Existing statistical approaches can better handle diversity,
but have not to date modeled complex linguistic structure, limiting achievable
accuracy. Recent hybrid approaches have addressed limitations in scaling and
complexity, but have not effectively associated linguistic and perceptual
features. Our framework, called Generalized Grounding Graphs (G^3), addresses
these issues by defining a probabilistic graphical model dynamically according
to the linguistic parse structure of a natural language command. This approach
scales effectively, handles linguistic diversity, and enables the system to
associate parts of a command with the specific objects, places, and events in
the external world to which they refer. We show that robots can learn word
meanings and use those learned meanings to robustly follow natural language
commands produced by untrained users. We demonstrate our approach for both
mobility commands and mobile manipulation commands involving a variety of
semi-autonomous robotic platforms, including a wheelchair, a micro-air vehicle,
a forklift, and the Willow Garage PR2.
Teacher-Student Curriculum Learning
Authors: Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman
Date: 2017-07-01 00:00:00
Tasks: Minecraft

We propose Teacher-Student Curriculum Learning (TSCL), a framework for
automatic curriculum learning, where the Student tries to learn a complex task
and the Teacher automatically chooses subtasks from a given set for the Student
to train on. We describe a family of Teacher algorithms that rely on the
intuition that the Student should practice more those tasks on which it makes
the fastest progress, i.e. where the slope of the learning curve is highest. In
addition, the Teacher algorithms address the problem of forgetting by also
choosing tasks where the Student's performance is getting worse. We demonstrate
that TSCL matches or surpasses the results of carefully hand-crafted curricula
in two tasks: addition of decimal numbers with LSTM and navigation in
Minecraft. Using our automatically generated curriculum enabled to solve a
Minecraft maze that could not be solved at all when training directly on
solving the maze, and the learning was an order of magnitude faster than
uniform sampling of subtasks.
Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Neural Networks
Authors: Nghi D. Q. Bui, Lingxiao Jiang, Yijun Yu
Date: 2017-10-17 00:00:00
Tasks: Binary Classification, C++ code, Classification, General Classification

Towards the vision of translating code that implements an algorithm from one
programming language into another, this paper proposes an approach for
automated program classification using bilateral tree-based convolutional
neural networks (BiTBCNNs). It is layered on top of two tree-based
convolutional neural networks (TBCNNs), each of which recognizes the algorithm
of code written in an individual programming language. The combination layer of
the networks recognizes the similarities and differences among code in
different programming languages. The BiTBCNNs are trained using the source code
in different languages but known to implement the same algorithms and/or
functionalities. For a preliminary evaluation, we use 3591 Java and 3534 C++
code snippets from 6 algorithms we crawled systematically from GitHub. We
obtained over 90% accuracy in the cross-language binary classification task to
tell whether any given two code snippets implement the same algorithm. Also,
for the algorithm classification task, i.e., to predict which one of the six
algorithm labels is implemented by an arbitrary C++ code snippet, we achieved
over 80% precision.
Deep Learning for identifying radiogenomic associations in breast cancer
Authors: Zhe Zhu, Ehab AlBadawy, Ashirbani Saha, Jun Zhang, Michael R. Harowicz, Maciej A. Mazurowski
Date: 2017-11-29 00:00:00
Tasks: Deep Learning, Transfer Learning

Purpose: To determine whether deep learning models can distinguish between
breast cancer molecular subtypes based on dynamic contrast-enhanced magnetic
resonance imaging (DCE-MRI). Materials and methods: In this institutional
review board-approved single-center study, we analyzed DCE-MR images of 270
patients at our institution. Lesions of interest were identified by
radiologists. The task was to automatically determine whether the tumor is of
the Luminal A subtype or of another subtype based on the MR image patches
representing the tumor. Three different deep learning approaches were used to
classify the tumor according to their molecular subtypes: learning from scratch
where only tumor patches were used for training, transfer learning where
networks pre-trained on natural images were fine-tuned using tumor patches, and
off-the-shelf deep features where the features extracted by neural networks
trained on natural images were used for classification with a support vector
machine. Network architectures utilized in our experiments were GoogleNet, VGG,
and CIFAR. We used 10-fold crossvalidation method for validation and area under
the receiver operating characteristic (AUC) as the measure of performance.
Results: The best AUC performance for distinguishing molecular subtypes was
0.65 (95% CI:[0.57,0.71]) and was achieved by the off-the-shelf deep features
approach. The highest AUC performance for training from scratch was 0.58 (95%
CI:[0.51,0.64]) and the best AUC performance for transfer learning was 0.60
(95% CI:[0.52,0.65]) respectively. For the off-the-shelf approach, the features
extracted from the fully connected layer performed the best. Conclusion: Deep
learning may play a role in discovering radiogenomic associations in breast
cancer.
Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations
Authors: Xiaohan Yan, Jacob Bien
Date: 2015-12-05 00:00:00
Tasks: Computational Efficiency, Time Series Analysis, Vocal Bursts Valence Prediction

Demanding sparsity in estimated models has become a routine practice in
statistics. In many situations, we wish to require that the sparsity patterns
attained honor certain problem-specific constraints. Hierarchical sparse
modeling (HSM) refers to situations in which these constraints specify that one
set of parameters be set to zero whenever another is set to zero. In recent
years, numerous papers have developed convex regularizers for this form of
sparsity structure, which arises in many areas of statistics including
interaction modeling, time series analysis, and covariance estimation. In this
paper, we observe that these methods fall into two frameworks, the group lasso
(GL) and latent overlapping group lasso (LOG), which have not been
systematically compared in the context of HSM. The purpose of this paper is to
provide a side-by-side comparison of these two frameworks for HSM in terms of
their statistical properties and computational efficiency. We call special
attention to GL's more aggressive shrinkage of parameters deep in the
hierarchy, a property not shared by LOG. In terms of computation, we introduce
a finite-step algorithm that exactly solves the proximal operator of LOG for a
certain simple HSM structure; we later exploit this to develop a novel
path-based block coordinate descent scheme for general HSM structures. Both
algorithms greatly improve the computational performance of LOG. Finally, we
compare the two methods in the context of covariance estimation, where we
introduce a new sparsely-banded estimator using LOG, which we show achieves the
statistical advantages of an existing GL-based method but is simpler to express
and more efficient to compute.
False Positive and Cross-relation Signals in Distant Supervision Data
Authors: Anca Dumitrache, Lora Aroyo, Chris Welty
Date: 2017-11-14 00:00:00
Tasks: General Classification, Relation, Relation Classification, Relation Extraction

Distant supervision (DS) is a well-established method for relation extraction
from text, based on the assumption that when a knowledge-base contains a
relation between a term pair, then sentences that contain that pair are likely
to express the relation. In this paper, we use the results of a crowdsourcing
relation extraction task to identify two problems with DS data quality: the
widely varying degree of false positives across different relations, and the
observed causal connection between relations that are not considered by the DS
method. The crowdsourcing data aggregation is performed using ambiguity-aware
CrowdTruth metrics, that are used to capture and interpret inter-annotator
disagreement. We also present preliminary results of using the crowd to enhance
DS training data for a relation classification model, without requiring the
crowd to annotate the entire set.
A fast nonconvex Compressed Sensing algorithm for highly low-sampled MR images reconstruction
Authors: Damiana Lazzaro, Elena Loli Piccolomini, Fabiana Zama
Date: 2017-11-29 00:00:00
Tasks: compressed sensing

In this paper we present a fast and efficient method for the reconstruction
of Magnetic Resonance Images (MRI) from severely under-sampled data. From the
Compressed Sensing theory we have mathematically modeled the problem as a
constrained minimization problem with a family of non-convex regularizing
objective functions depending on a parameter and a least squares data fit
constraint. We propose a fast and efficient algorithm, named Fast NonConvex
Reweighting (FNCR) algorithm, based on an iterative scheme where the non-convex
problem is approximated by its convex linearization and the penalization
parameter is automatically updated. The convex problem is solved by a
Forward-Backward procedure, where the Backward step is performed by a Split
Bregman strategy. Moreover, we propose a new efficient iterative solver for the
arising linear systems. We prove the convergence of the proposed FNCR method.
The results on synthetic phantoms and real images show that the algorithm is
very well performing and computationally efficient, even when compared to the
best performing methods proposed in the literature.
HSC: A Novel Method for Clustering Hierarchies of Networked Data
Authors: Antonia Korba
Date: 2017-11-29 00:00:00
Tasks: Clustering

Hierarchical clustering is one of the most powerful solutions to the problem of clustering, on the grounds that it performs a multi scale organization of the data. In recent years, research on hierarchical clustering methods has attracted considerable interest due to the demanding modern application domains. We present a novel divisive hierarchical clustering framework called Hierarchical Stochastic Clustering (HSC), that acts in two stages. In the first stage, it finds a primary hierarchy of clustering partitions in a dataset. In the second stage, feeds a clustering algorithm with each one of the clusters of the very detailed partition, in order to settle the final result. The output is a hierarchy of clusters. Our method is based on the previous research of Meyer and Weissel Stochastic Data Clustering and the theory of Simon and Ando on Variable Aggregation. Our experiments show that our framework builds a meaningful hierarchy of clusters and benefits consistently the clustering algorithm that acts in the second stage, not only computationally but also in terms of cluster quality. This result suggest that HSC framework is ideal for obtaining hierarchical solutions of large volumes of data.
Detection-aided liver lesion segmentation using deep learning
Authors: Miriam Bellver, Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Xavier Giro-i-Nieto, Jordi Torres, Luc van Gool
Date: 2017-11-29 00:00:00
Tasks: Computed Tomography (CT), Deep Learning, Lesion Segmentation, Medical Image Segmentation, Segmentation

A fully automatic technique for segmenting the liver and localizing its
unhealthy tissues is a convenient tool in order to diagnose hepatic diseases
and assess the response to the according treatments. In this work we propose a
method to segment the liver and its lesions from Computed Tomography (CT) scans
using Convolutional Neural Networks (CNNs), that have proven good results in a
variety of computer vision tasks, including medical imaging. The network that
segments the lesions consists of a cascaded architecture, which first focuses
on the region of the liver in order to segment the lesions on it. Moreover, we
train a detector to localize the lesions, and mask the results of the
segmentation network with the positive detections. The segmentation
architecture is based on DRIU, a Fully Convolutional Network (FCN) with side
outputs that work on feature maps of different resolutions, to finally benefit
from the multi-scale information learned by different stages of the network.
The main contribution of this work is the use of a detector to localize the
lesions, which we show to be beneficial to remove false positives triggered by
the segmentation network. Source code and models are available at
https://imatge-upc.github.io/liverseg-2017-nipsws/ .
Gaussian Process Neurons Learn Stochastic Activation Functions
Authors: Sebastian Urban, Marcus Basalla, Patrick van der Smagt
Date: 2017-11-29 00:00:00
Tasks: Bayesian Inference, Gaussian Processes

We propose stochastic, non-parametric activation functions that are fully
learnable and individual to each neuron. Complexity and the risk of overfitting
are controlled by placing a Gaussian process prior over these functions. The
result is the Gaussian process neuron, a probabilistic unit that can be used as
the basic building block for probabilistic graphical models that resemble the
structure of neural networks. The proposed model can intrinsically handle
uncertainties in its inputs and self-estimate the confidence of its
predictions. Using variational Bayesian inference and the central limit
theorem, a fully deterministic loss function is derived, allowing it to be
trained as efficiently as a conventional neural network using mini-batch
gradient descent. The posterior distribution of activation functions is
inferred from the training data alongside the weights of the network.
  The proposed model favorably compares to deep Gaussian processes, both in
model complexity and efficiency of inference. It can be directly applied to
recurrent or convolutional network structures, allowing its use in audio and
image processing tasks.
  As an preliminary empirical evaluation we present experiments on regression
and classification tasks, in which our model achieves performance comparable to
or better than a Dropout regularized neural network with a fixed activation
function. Experiments are ongoing and results will be added as they become
available.
A Multi-Horizon Quantile Recurrent Forecaster
Authors: Ruofeng Wen, Kari Torkkola, Balakrishnan Narayanaswamy, Dhruv Madeka
Date: 2017-11-29 00:00:00
Tasks: quantile regression, regression, Time Series, Time Series Analysis, Time Series Regression

We propose a framework for general probabilistic multi-step time series
regression. Specifically, we exploit the expressiveness and temporal nature of
Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutional
structures), the nonparametric nature of Quantile Regression and the efficiency
of Direct Multi-Horizon Forecasting. A new training scheme,
*forking-sequences*, is designed for sequential nets to boost stability and
performance. We show that the approach accommodates both temporal and static
covariates, learning across multiple related series, shifting seasonality,
future planned event spikes and cold-starts in real life large-scale
forecasting. The performance of the framework is demonstrated in an application
to predict the future demand of items sold on Amazon.com, and in a public
probabilistic forecasting competition to predict electricity price and load.
A Novel Data-Driven Framework for Risk Characterization and Prediction from Electronic Medical Records: A Case Study of Renal Failure
Authors: Prithwish Chakraborty, Vishrawas Gopalakrishnan, Sharon M. H. Alford, Faisal Farooq
Date: 2017-11-29 00:00:00

Electronic medical records (EMR) contain longitudinal information about
patients that can be used to analyze outcomes. Typically, studies on EMR data
have worked with established variables that have already been acknowledged to
be associated with certain outcomes. However, EMR data may also contain
hitherto unrecognized factors for risk association and prediction of outcomes
for a disease. In this paper, we present a scalable data-driven framework to
analyze EMR data corpus in a disease agnostic way that systematically uncovers
important factors influencing outcomes in patients, as supported by data and
without expert guidance. We validate the importance of such factors by using
the framework to predict for the relevant outcomes. Specifically, we analyze
EMR data covering approximately 47 million unique patients to characterize
renal failure (RF) among type 2 diabetic (T2DM) patients. We propose a
specialized L1 regularized Cox Proportional Hazards (CoxPH) survival model to
identify the important factors from those available from patient encounter
history. To validate the identified factors, we use a specialized generalized
linear model (GLM) to predict the probability of renal failure for individual
patients within a specified time window. Our experiments indicate that the
factors identified via our data-driven method overlap with the patient
characteristics recognized by experts. Our approach allows for scalable,
repeatable and efficient utilization of data available in EMRs, confirms prior
medical knowledge and can generate new hypothesis without expert supervision.
HoME: a Household Multimodal Environment
Authors: Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, Aaron Courville
Date: 2017-11-29 00:00:00
Tasks: OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce HoME: a Household Multimodal Environment for artificial agents
to learn from vision, audio, semantics, physics, and interaction with objects
and other agents, all within a realistic context. HoME integrates over 45,000
diverse 3D house layouts based on the SUNCG dataset, a scale which may
facilitate learning, generalization, and transfer. HoME is an open-source,
OpenAI Gym-compatible platform extensible to tasks in reinforcement learning,
language grounding, sound-based navigation, robotics, multi-agent learning, and
more. We hope HoME better enables artificial agents to learn as humans do: in
an interactive, multimodal, and richly contextualized setting.