pwc-archive/papers-with-abstracts huggingface dataset:
Linear Regression with Sparsely Permuted Data
Authors: Martin Slawski, Emanuel Ben-David
Date: 2017-10-16 00:00:00
Tasks: regression

In regression analysis of multivariate data, it is tacitly assumed that
response and predictor variables in each observed response-predictor pair
correspond to the same entity or unit. In this paper, we consider the situation
of "permuted data" in which this basic correspondence has been lost. Several
recent papers have considered this situation without further assumptions on the
underlying permutation. In applications, the latter is often to known to have
additional structure that can be leveraged. Specifically, we herein consider
the common scenario of "sparsely permuted data" in which only a small fraction
of the data is affected by a mismatch between response and predictors. However,
an adverse effect already observed for sparsely permuted data is that the least
squares estimator as well as other estimators not accounting for such partial
mismatch are inconsistent. One approach studied in detail herein is to treat
permuted data as outliers which motivates the use of robust regression
formulations to estimate the regression parameter. The resulting estimate can
subsequently be used to recover the permutation. A notable benefit of the
proposed approach is its computational simplicity given the general lack of
procedures for the above problem that are both statistically sound and
computationally appealing.
Understanding the Changing Roles of Scientific Publications via Citation Embeddings
Authors: Jiangen He, Chaomei Chen
Date: 2017-11-15 00:00:00

Researchers may describe different aspects of past scientific publications in
their publications and the descriptions may keep changing in the evolution of
science. The diverse and changing descriptions (i.e., citation context) on a
publication characterize the impact and contributions of the past publication.
In this article, we aim to provide an approach to understanding the changing
and complex roles of a publication characterized by its citation context. We
described a method to represent the publications' dynamic roles in science
community in different periods as a sequence of vectors by training temporal
embedding models. The temporal representations can be used to quantify how much
the roles of publications changed and interpret how they changed. Our study in
the biomedical domain shows that our metric on the changes of publications'
roles is stable over time at the population level but significantly distinguish
individuals. We also show the interpretability of our methods by a concrete
example.
Generative Adversarial Active Learning
Authors: Jia-Jie Zhu, José Bento
Date: 2017-02-25 00:00:00
Tasks: Active Learning

We propose a new active learning by query synthesis approach using Generative
Adversarial Networks (GAN). Different from regular active learning, the
resulting algorithm adaptively synthesizes training instances for querying to
increase learning speed. We generate queries according to the uncertainty
principle, but our idea can work with other active learning principles. We
report results from various numerical experiments to demonstrate the
effectiveness the proposed approach. In some settings, the proposed algorithm
outperforms traditional pool-based approaches. To the best our knowledge, this
is the first active learning work using GAN.
K3, L3, LP, RM3, A3, FDE: How to Make Many-Valued Logics Work for You
Authors: Allen P. Hazen, Francis Jeffry Pelletier
Date: 2017-11-15 00:00:00

We investigate some well-known (and a few not-so-well-known) many-valued
logics that have a small number (3 or 4) of truth values. For some of them we
complain that they do not have any \emph{logical} use (despite their perhaps
having some intuitive semantic interest) and we look at ways to add features so
as to make them useful, while retaining their intuitive appeal. At the end, we
show some surprising results in the system FDE, and its relationships with
features of other logics. We close with some new examples of "synonymous
logics." An Appendix contains a natural deduction system for our augmented FDE,
and proofs of soundness and completeness.
Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans
Authors: Huaiyang Zhong, Xiaocheng Li, David Lobell, Stefano Ermon, Margaret L. Brandeau
Date: 2017-11-15 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Weather Forecasting

Eradicating hunger and malnutrition is a key development goal of the 21st
century. We address the problem of optimally identifying seed varieties to
reliably increase crop yield within a risk-sensitive decision-making framework.
Specifically, we introduce a novel hierarchical machine learning mechanism for
predicting crop yield (the yield of different seed varieties of the same crop).
We integrate this prediction mechanism with a weather forecasting model, and
propose three different approaches for decision making under uncertainty to
select seed varieties for planting so as to balance yield maximization and
risk.We apply our model to the problem of soybean variety selection given in
the 2016 Syngenta Crop Challenge. Our prediction model achieves a median
absolute error of 3.74 bushels per acre and thus provides good estimates for
input into the decision models.Our decision models identify the selection of
soybean varieties that appropriately balance yield and risk as a function of
the farmer's risk aversion level. More generally, our models support farmers in
decision making about which seed varieties to plant.
Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes
Authors: Guowei Wan, Xiaolong Yang, Renlan Cai, Hao Li, Hao Wang, Shiyu Song
Date: 2017-11-15 00:00:00
Tasks: 3D geometry, Autonomous Driving, Sensor Fusion

We present a robust and precise localization system that achieves centimeter-level localization accuracy in disparate city scenes. Our system adaptively uses information from complementary sensors such as GNSS, LiDAR, and IMU to achieve high localization accuracy and resilience in challenging scenes, such as urban downtown, highways, and tunnels. Rather than relying only on LiDAR intensity or 3D geometry, we make innovative use of LiDAR intensity and altitude cues to significantly improve localization system accuracy and robustness. Our GNSS RTK module utilizes the help of the multi-sensor fusion framework and achieves a better ambiguity resolution success rate. An error-state Kalman filter is applied to fuse the localization measurements from different sources with novel uncertainty estimation. We validate, in detail, the effectiveness of our approaches, achieving 5-10cm RMS accuracy and outperforming previous state-of-the-art systems. Importantly, our system, while deployed in a large autonomous driving fleet, made our vehicles fully autonomous in crowded city streets despite road construction that occurred from time to time. A dataset including more than 60 km real traffic driving in various urban roads is used to comprehensively test our system.
ORBIT: Ordering Based Information Transfer Across Space and Time for Global Surface Water Monitoring
Authors: Ankush Khandelwal, Anuj Karpatne, Vipin Kumar
Date: 2017-11-15 00:00:00
Tasks: Earth Observation

Many earth science applications require data at both high spatial and
temporal resolution for effective monitoring of various ecosystem resources.
Due to practical limitations in sensor design, there is often a trade-off in
different resolutions of spatio-temporal datasets and hence a single sensor
alone cannot provide the required information. Various data fusion methods have
been proposed in the literature that mainly rely on individual timesteps when
both datasets are available to learn a mapping between features values at
different resolutions using local relationships between pixels. Earth
observation data is often plagued with spatially and temporally correlated
noise, outliers and missing data due to atmospheric disturbances which pose a
challenge in learning the mapping from a local neighborhood at individual
timesteps. In this paper, we aim to exploit time-independent global
relationships between pixels for robust transfer of information across
different scales. Specifically, we propose a new framework, ORBIT (Ordering
Based Information Transfer) that uses relative ordering constraint among pixels
to transfer information across both time and scales. The effectiveness of the
framework is demonstrated for global surface water monitoring using both
synthetic and real-world datasets.
Finer Grained Entity Typing with TypeNet
Authors: Shikhar Murty, Patrick Verga, Luke Vilnis, Andrew McCallum
Date: 2017-11-15 00:00:00
Tasks: Entity Typing

We consider the challenging problem of entity typing over an extremely fine
grained set of types, wherein a single mention or entity can have many
simultaneous and often hierarchically-structured types. Despite the importance
of the problem, there is a relative lack of resources in the form of
fine-grained, deep type hierarchies aligned to existing knowledge bases. In
response, we introduce TypeNet, a dataset of entity types consisting of over
1941 types organized in a hierarchy, obtained by manually annotating a mapping
from 1081 Freebase types to WordNet. We also experiment with several models
comparable to state-of-the-art systems and explore techniques to incorporate a
structure loss on the hierarchy with the standard mention typing loss, as a
first step towards future research on this dataset.
CMU LiveMedQA at TREC 2017 LiveQA: A Consumer Health Question Answering System
Authors: Yuan Yang, Jingcheng Yu, Ye Hu, Xiaoyao Xu, Eric Nyberg
Date: 2017-11-15 00:00:00
Tasks: Answer Generation, General Classification, Question Answering, Retrieval, text-classification, Text Classification

In this paper, we present LiveMedQA, a question answering system that is
optimized for consumer health question. On top of the general QA system
pipeline, we introduce several new features that aim to exploit domain-specific
knowledge and entity structures for better performance. This includes a
question type/focus analyzer based on deep text classification model, a
tree-based knowledge graph for answer generation and a complementary
structure-aware searcher for answer retrieval. LiveMedQA system is evaluated in
the TREC 2017 LiveQA medical subtask, where it received an average score of
0.356 on a 3 point scale. Evaluation results revealed 3 substantial drawbacks
in current LiveMedQA system, based on which we provide a detailed discussion
and propose a few solutions that constitute the main focus of our subsequent
work.
End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design
Authors: Li Shen
Date: 2017-11-15 00:00:00
Tasks: All

We develop an end-to-end training algorithm for whole-image breast cancer
diagnosis based on mammograms. It requires lesion annotations only at the first
stage of training. After that, a whole image classifier can be trained using
only image level labels. This greatly reduced the reliance on lesion
annotations. Our approach is implemented using an all convolutional design that
is simple yet provides superior performance in comparison with the previous
methods. On DDSM, our best single-model achieves a per-image AUC score of 0.88
and three-model averaging increases the score to 0.91. On INbreast, our best
single-model achieves a per-image AUC score of 0.96. Using DDSM as benchmark,
our models compare favorably with the current state-of-the-art. We also
demonstrate that a whole image model trained on DDSM can be easily transferred
to INbreast without using its lesion annotations and using only a small amount
of training data. Code availability: https://github.com/lishen/end2end-all-conv
Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train
Authors: Valeriu Codreanu, Damian Podareanu, Vikram Saletore
Date: 2017-11-12 00:00:00
Tasks: Representation Learning

For the past 5 years, the ILSVRC competition and the ImageNet dataset have
attracted a lot of interest from the Computer Vision community, allowing for
state-of-the-art accuracy to grow tremendously. This should be credited to the
use of deep artificial neural network designs. As these became more complex,
the storage, bandwidth, and compute requirements increased. This means that
with a non-distributed approach, even when using the most high-density server
available, the training process may take weeks, making it prohibitive.
Furthermore, as datasets grow, the representation learning potential of deep
networks grows as well by using more complex models. This synchronicity
triggers a sharp increase in the computational requirements and motivates us to
explore the scaling behaviour on petaflop scale supercomputers. In this paper
we will describe the challenges and novel solutions needed in order to train
ResNet-50 in this large scale environment. We demonstrate above 90\% scaling
efficiency and a training time of 28 minutes using up to 104K x86 cores. This
is supported by software tools from Intel's ecosystem. Moreover, we show that
with regular 90 - 120 epoch train runs we can achieve a top-1 accuracy as high
as 77\% for the unmodified ResNet-50 topology. We also introduce the novel
Collapsed Ensemble (CE) technique that allows us to obtain a 77.5\% top-1
accuracy, similar to that of a ResNet-152, while training a unmodified
ResNet-50 topology for the same fixed training budget. All ResNet-50 models as
well as the scripts needed to replicate them will be posted shortly.
Predicting vehicular travel times by modeling heterogeneous influences between arterial roads
Authors: Avinash Achar, Venkatesh Sarangan, R Rohith, Anand Sivasubramaniam
Date: 2017-11-15 00:00:00

Predicting travel times of vehicles in urban settings is a useful and
tangible quantity of interest in the context of intelligent transportation
systems. We address the problem of travel time prediction in arterial roads
using data sampled from probe vehicles. There is only a limited literature on
methods using data input from probe vehicles. The spatio-temporal dependencies
captured by existing data driven approaches are either too detailed or very
simplistic. We strike a balance of the existing data driven approaches to
account for varying degrees of influence a given road may experience from its
neighbors, while controlling the number of parameters to be learnt.
Specifically, we use a NoisyOR conditional probability distribution (CPD) in
conjunction with a dynamic bayesian network (DBN) to model state transitions of
various roads. We propose an efficient algorithm to learn model parameters. We
propose an algorithm for predicting travel times on trips of arbitrary
durations. Using synthetic and real world data traces we demonstrate the
superior performance of the proposed method under different traffic conditions.
Fast Predictive Simple Geodesic Regression
Authors: Zhipeng Ding, Greg Fleishman, Xiao Yang, Paul Thompson, Roland Kwitt, Marc Niethammer
Date: 2017-11-15 00:00:00
Tasks: GPU, Image Registration, Medical Image Analysis, regression

Deformable image registration and regression are important tasks in medical
image analysis. However, they are computationally expensive, especially when
analyzing large-scale datasets that contain thousands of images. Hence, cluster
computing is typically used, making the approaches dependent on such
computational infrastructure. Even larger computational resources are required
as study sizes increase. This limits the use of deformable image registration
and regression for clinical applications and as component algorithms for other
image analysis approaches. We therefore propose using a fast predictive
approach to perform image registrations. In particular, we employ these fast
registration predictions to approximate a simplified geodesic regression model
to capture longitudinal brain changes. The resulting method is orders of
magnitude faster than the standard optimization-based regression model and
hence facilitates large-scale analysis on a single graphics processing unit
(GPU). We evaluate our results on 3D brain magnetic resonance images (MRI) from
the ADNI datasets.
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
Authors: Mohammed Sadegh Norouzzadeh, Anh Nguyen, Margaret Kosmala, Ali Swanson, Meredith Palmer, Craig Packer, Jeff Clune
Date: 2017-03-16 00:00:00

Having accurate, detailed, and up-to-date information about the location and
behavior of animals in the wild would revolutionize our ability to study and
conserve ecosystems. We investigate the ability to automatically, accurately,
and inexpensively collect such data, which could transform many fields of
biology, ecology, and zoology into "big data" sciences. Motion sensor "camera
traps" enable collecting wildlife pictures inexpensively, unobtrusively, and
frequently. However, extracting information from these pictures remains an
expensive, time-consuming, manual task. We demonstrate that such information
can be automatically extracted by deep learning, a cutting-edge type of
artificial intelligence. We train deep convolutional neural networks to
identify, count, and describe the behaviors of 48 species in the
3.2-million-image Snapshot Serengeti dataset. Our deep neural networks
automatically identify animals with over 93.8% accuracy, and we expect that
number to improve rapidly in years to come. More importantly, if our system
classifies only images it is confident about, our system can automate animal
identification for 99.3% of the data while still performing at the same 96.6%
accuracy as that of crowdsourced teams of human volunteers, saving more than
8.4 years (at 40 hours per week) of human labeling effort (i.e. over 17,000
hours) on this 3.2-million-image dataset. Those efficiency gains immediately
highlight the importance of using deep neural networks to automate data
extraction from camera-trap images. Our results suggest that this technology
could enable the inexpensive, unobtrusive, high-volume, and even real-time
collection of a wealth of information about vast numbers of animals in the
wild.
Attending to All Mention Pairs for Full Abstract Biological Relation Extraction
Authors: Patrick Verga, Emma Strubell, Ofer Shai, Andrew McCallum
Date: 2017-10-23 00:00:00
Tasks: All, Prediction, Relation, Relation Extraction, Sentence

Most work in relation extraction forms a prediction by looking at a short
span of text within a single sentence containing a single entity pair mention.
However, many relation types, particularly in biomedical text, are expressed
across sentences or require a large context to disambiguate. We propose a model
to consider all mention and entity pairs simultaneously in order to make a
prediction. We encode full paper abstracts using an efficient self-attention
encoder and form pairwise predictions between all mentions with a bi-affine
operation. An entity-pair wise pooling aggregates mention pair scores to make a
final prediction while alleviating training noise by performing within document
multi-instance learning. We improve our model's performance by jointly training
the model to predict named entities and adding an additional corpus of weakly
labeled data. We demonstrate our model's effectiveness by achieving the state
of the art on the Biocreative V Chemical Disease Relation dataset for models
without KB resources, outperforming ensembles of models which use hand-crafted
features and additional linguistic resources.
Random gradient extrapolation for distributed and stochastic optimization
Authors: Guanghui Lan, Yi Zhou
Date: 2017-11-15 00:00:00
Tasks: Stochastic Optimization

In this paper, we consider a class of finite-sum convex optimization problems
defined over a distributed multiagent network with $m$ agents connected to a
central server. In particular, the objective function consists of the average
of $m$ ($\ge 1$) smooth components associated with each network agent together
with a strongly convex term. Our major contribution is to develop a new
randomized incremental gradient algorithm, namely random gradient extrapolation
method (RGEM), which does not require any exact gradient evaluation even for
the initial point, but can achieve the optimal ${\cal O}(\log(1/\epsilon))$
complexity bound in terms of the total number of gradient evaluations of
component functions to solve the finite-sum problems. Furthermore, we
demonstrate that for stochastic finite-sum optimization problems, RGEM
maintains the optimal ${\cal O}(1/\epsilon)$ complexity (up to a certain
logarithmic factor) in terms of the number of stochastic gradient computations,
but attains an ${\cal O}(\log(1/\epsilon))$ complexity in terms of
communication rounds (each round involves only one agent). It is worth noting
that the former bound is independent of the number of agents $m$, while the
latter one only linearly depends on $m$ or even $\sqrt m$ for ill-conditioned
problems. To the best of our knowledge, this is the first time that these
complexity bounds have been obtained for distributed and stochastic
optimization problems. Moreover, our algorithms were developed based on a novel
dual perspective of Nesterov's accelerated gradient method.
Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition
Authors: Chris Donahue, Bo Li, Rohit Prabhavalkar
Date: 2017-11-15 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Robust Speech Recognition, Speech Enhancement, speech-recognition, Speech Recognition

We investigate the effectiveness of generative adversarial networks (GANs)
for speech enhancement, in the context of improving noise robustness of
automatic speech recognition (ASR) systems. Prior work demonstrates that GANs
can effectively suppress additive noise in raw waveform speech signals,
improving perceptual quality metrics; however this technique was not justified
in the context of ASR. In this work, we conduct a detailed study to measure the
effectiveness of GANs in enhancing speech contaminated by both additive and
reverberant noise. Motivated by recent advances in image processing, we propose
operating GANs on log-Mel filterbank spectra instead of waveforms, which
requires less computation and is more robust to reverberant noise. While GAN
enhancement improves the performance of a clean-trained ASR system on noisy
speech, it falls short of the performance achieved by conventional multi-style
training (MTR). By appending the GAN-enhanced features to the noisy inputs and
retraining, we achieve a 7% WER improvement relative to the MTR system.
Markov Decision Processes with Continuous Side Information
Authors: Aditya Modi, Nan Jiang, Satinder Singh, Ambuj Tewari
Date: 2017-11-15 00:00:00
Tasks: PAC learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider a reinforcement learning (RL) setting in which the agent
interacts with a sequence of episodic MDPs. At the start of each episode the
agent has access to some side-information or context that determines the
dynamics of the MDP for that episode. Our setting is motivated by applications
in healthcare where baseline measurements of a patient at the start of a
treatment episode form the context that may provide information about how the
patient might respond to treatment decisions. We propose algorithms for
learning in such Contextual Markov Decision Processes (CMDPs) under an
assumption that the unobserved MDP parameters vary smoothly with the observed
context. We also give lower and upper PAC bounds under the smoothness
assumption. Because our lower bound has an exponential dependence on the
dimension, we consider a tractable linear setting where the context is used to
create linear combinations of a finite set of MDPs. For the linear setting, we
give a PAC learning algorithm based on KWIK learning techniques.