pwc-archive/papers-with-abstracts huggingface dataset:
Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation
Authors: Boyuan Pan, Hao Li, Zhou Zhao, Deng Cai, Xiaofei He
Date: 2017-11-01 00:00:00
Tasks: Machine Reading Comprehension, Machine Translation, Reading Comprehension, Sentence, TriviaQA

In this paper, we consider the problem of machine reading task when the
questions are in the form of keywords, rather than natural language. In recent
years, researchers have achieved significant success on machine reading
comprehension tasks, such as SQuAD and TriviaQA. These datasets provide a
natural language question sentence and a pre-selected passage, and the goal is
to answer the question according to the passage. However, in the situation of
interacting with machines by means of text, people are more likely to raise a
query in form of several keywords rather than a complete sentence. The
keyword-based query comprehension is a new challenge, because small variations
to a question may completely change its semantical information, thus yield
different answers. In this paper, we propose a novel neural network system that
consists a Demand Optimization Model based on a passage-attention neural
machine translation and a Reader Model that can find the answer given the
optimized question. The Demand Optimization Model optimizes the original query
and output multiple reconstructed questions, then the Reader Model takes the
new questions as input and locate the answers from the passage. To make
predictions robust, an evaluation mechanism will score the reconstructed
questions so the final answer strike a good balance between the quality of both
the Demand Optimization Model and the Reader Model. Experimental results on
several datasets show that our framework significantly improves multiple strong
baselines on this challenging task.
Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation
Authors: Guan-Horng Liu, Avinash Siravuru, Sai Prabhakar, Manuela Veloso, George Kantor
Date: 2017-05-30 00:00:00
Tasks: Autonomous Navigation, Sensor Fusion, State Estimation

Multisensory polices are known to enhance both state estimation and target
tracking. However, in the space of end-to-end sensorimotor control, this
multi-sensor outlook has received limited attention. Moreover, systematic ways
to make policies robust to partial sensor failure are not well explored. In
this work, we propose a specific customization of Dropout, called
\textit{Sensor Dropout}, to improve multisensory policy robustness and handle
partial failure in the sensor-set. We also introduce an additional auxiliary
loss on the policy network in order to reduce variance in the band of potential
multi- and uni-sensory policies to reduce jerks during policy switching
triggered by an abrupt sensor failure or deactivation/activation. Finally,
through the visualization of gradients, we show that the learned policies are
conditioned on the same latent states representation despite having diverse
observations spaces - a hallmark of true sensor-fusion. Simulation results of
the multisensory policy, as visualized in TORCS racing game, can be seen here:
https://youtu.be/QAK2lcXjNZc.
Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples
Authors: Pavlos Vougiouklis, Hady Elsahar, Lucie-Aimée Kaffee, Christoph Gravier, Frederique Laforest, Jonathon Hare, Elena Simperl
Date: 2017-11-01 00:00:00

Most people do not interact with Semantic Web data directly. Unless they have
the expertise to understand the underlying technology, they need textual or
visual interfaces to help them make sense of it. We explore the problem of
generating natural language summaries for Semantic Web data. This is
non-trivial, especially in an open-domain context. To address this problem, we
explore the use of neural networks. Our system encodes the information from a
set of triples into a vector of fixed dimensionality and generates a textual
summary by conditioning the output on the encoded vector. We train and evaluate
our models on two corpora of loosely aligned Wikipedia snippets and DBpedia and
Wikidata triples with promising results.
Erratum: Link prediction in drug-target interactions network using similarity indices
Authors: Yiding Lu, Yufan Guo, Anna Korhonen
Date: 2017-11-01 00:00:00
Tasks: Link Prediction, Prediction

Background: In silico drug-target interaction (DTI) prediction plays an
integral role in drug repositioning: the discovery of new uses for existing
drugs. One popular method of drug repositioning is network-based DTI
prediction, which uses complex network theory to predict DTIs from a
drug-target network. Currently, most network-based DTI prediction is based on
machine learning methods such as Restricted Boltzmann Machines (RBM) or Support
Vector Machines (SVM). These methods require additional information about the
characteristics of drugs, targets and DTIs, such as chemical structure, genome
sequence, binding types, causes of interactions, etc., and do not perform
satisfactorily when such information is unavailable. We propose a new,
alternative method for DTI prediction that makes use of only network topology
information attempting to solve this problem.
  Results: We compare our method for DTI prediction against the well-known RBM
approach. We show that when applied to the MATADOR database, our approach based
on node neighborhoods yield higher precision for high-ranking predictions than
RBM when no information regarding DTI types is available.
  Conclusion: This demonstrates that approaches purely based on network
topology provide a more suitable approach to DTI prediction in the many
real-life situations where little or no prior knowledge is available about the
characteristics of drugs, targets, or their interactions.
Phase Conductor on Multi-layered Attentions for Machine Comprehension
Authors: Rui Liu, Wei Wei, Weiguang Mao, Maria Chikina
Date: 2017-10-28 00:00:00
Proceeding: ICLR 2018 1
Tasks: Question Answering, Reading Comprehension

Attention models have been intensively studied to improve NLP tasks such as
machine comprehension via both question-aware passage attention model and
self-matching attention model. Our research proposes phase conductor
(PhaseCond) for attention models in two meaningful ways. First, PhaseCond, an
architecture of multi-layered attention models, consists of multiple phases
each implementing a stack of attention layers producing passage representations
and a stack of inner or outer fusion layers regulating the information flow.
Second, we extend and improve the dot-product attention function for PhaseCond
by simultaneously encoding multiple question and passage embedding layers from
different perspectives. We demonstrate the effectiveness of our proposed model
PhaseCond on the SQuAD dataset, showing that our model significantly
outperforms both state-of-the-art single-layered and multiple-layered attention
models. We deepen our results with new findings via both detailed qualitative
analysis and visualized examples showing the dynamic changes through
multi-layered attention models.
A multitask deep learning model for real-time deployment in embedded systems
Authors: Miquel Martí, Atsuto Maki
Date: 2017-10-31 00:00:00
Tasks: object-detection, Object Detection, Semantic Segmentation

We propose an approach to Multitask Learning (MTL) to make deep learning
models faster and lighter for applications in which multiple tasks need to be
solved simultaneously, which is particularly useful in embedded, real-time
systems. We develop a multitask model for both Object Detection and Semantic
Segmentation and analyze the challenges that appear during its training. Our
multitask network is 1.6x faster, lighter and uses less memory than deploying
the single-task models in parallel. We conclude that MTL has the potential to
give superior performance in exchange of a more complex training process that
introduces challenges not present in single-task models.
Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxation
Authors: Abolfazl Hashemi, Rasoul Shafipour, Haris Vikalo, Gonzalo Mateos
Date: 2017-10-31 00:00:00

We study the problem of sampling a bandlimited graph signal in the presence
of noise, where the objective is to select a node subset of prescribed
cardinality that minimizes the signal reconstruction mean squared error (MSE).
To that end, we formulate the task at hand as the minimization of MSE subject
to binary constraints, and approximate the resulting NP-hard problem via
semidefinite programming (SDP) relaxation. Moreover, we provide an alternative
formulation based on maximizing a monotone weak submodular function and propose
a randomized-greedy algorithm to find a sub-optimal subset. We then derive a
worst-case performance guarantee on the MSE returned by the randomized greedy
algorithm for general non-stationary graph signals. The efficacy of the
proposed methods is illustrated through numerical simulations on synthetic and
real-world graphs. Notably, the randomized greedy algorithm yields an
order-of-magnitude speedup over state-of-the-art greedy sampling schemes, while
incurring only a marginal MSE performance loss.
Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation
Authors: Min Tang, Zichen Zhang, Dana Cobzas, Martin Jagersand, Jacob L. Jaremko
Date: 2017-10-31 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Volumetric Medical Image Segmentation

We propose an attention mechanism for 3D medical image segmentation. The
method, named segmentation-by-detection, is a cascade of a detection module
followed by a segmentation module. The detection module enables a region of
interest to come to attention and produces a set of object region candidates
which are further used as an attention model. Rather than dealing with the
entire volume, the segmentation module distills the information from the
potential region. This scheme is an efficient solution for volumetric data as
it reduces the influence of the surrounding noise which is especially important
for medical data with low signal-to-noise ratio. Experimental results on 3D
ultrasound data of the femoral head shows superiority of the proposed method
when compared with a standard fully convolutional network like the U-Net.
Accelerated Sparse Subspace Clustering
Authors: Abolfazl Hashemi, Haris Vikalo
Date: 2017-10-31 00:00:00
Tasks: Clustering

State-of-the-art algorithms for sparse subspace clustering perform spectral
clustering on a similarity matrix typically obtained by representing each data
point as a sparse combination of other points using either basis pursuit (BP)
or orthogonal matching pursuit (OMP). BP-based methods are often prohibitive in
practice while the performance of OMP-based schemes are unsatisfactory,
especially in settings where data points are highly similar. In this paper, we
propose a novel algorithm that exploits an accelerated variant of orthogonal
least-squares to efficiently find the underlying subspaces. We show that under
certain conditions the proposed algorithm returns a subspace-preserving
solution. Simulation results illustrate that the proposed method compares
favorably with BP-based method in terms of running time while being
significantly more accurate than OMP-based schemes.
OctNetFusion: Learning Depth Fusion from Data
Authors: Gernot Riegler, Ali Osman Ulusoy, Horst Bischof, Andreas Geiger
Date: 2017-04-04 00:00:00
Tasks: 3D Reconstruction

In this paper, we present a learning based approach to depth fusion, i.e.,
dense 3D reconstruction from multiple depth images. The most common approach to
depth fusion is based on averaging truncated signed distance functions, which
was originally proposed by Curless and Levoy in 1996. While this method is
simple and provides great results, it is not able to reconstruct (partially)
occluded surfaces and requires a large number frames to filter out sensor noise
and outliers. Motivated by the availability of large 3D model repositories and
recent advances in deep learning, we present a novel 3D CNN architecture that
learns to predict an implicit surface representation from the input depth maps.
Our learning based method significantly outperforms the traditional volumetric
fusion approach in terms of noise reduction and outlier suppression. By
learning the structure of real world 3D objects and scenes, our approach is
further able to reconstruct occluded regions and to fill in gaps in the
reconstruction. We demonstrate that our learning based approach outperforms
both vanilla TSDF fusion as well as TV-L1 fusion on the task of volumetric
fusion. Further, we demonstrate state-of-the-art 3D shape completion results.
OhioState at IJCNLP-2017 Task 4: Exploring Neural Architectures for Multilingual Customer Feedback Analysis
Authors: Dushyanta Dhyani
Date: 2017-10-18 00:00:00
Proceeding: IJCNLP 2017 12

This paper describes our systems for IJCNLP 2017 Shared Task on Customer
Feedback Analysis. We experimented with simple neural architectures that gave
competitive performance on certain tasks. This includes shallow CNN and
Bi-Directional LSTM architectures with Facebook's Fasttext as a baseline model.
Our best performing model was in the Top 5 systems using the Exact-Accuracy and
Micro-Average-F1 metrics for the Spanish (85.28% for both) and French (70% and
73.17% respectively) task, and outperformed all the other models on comment
(87.28%) and meaningless (51.85%) tags using Micro Average F1 by Tags metric
for the French task.
Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living
Authors: Ivan Miguel Pires, Nuno M. Garcia, Nuno Pombo, Francisco Flórez-Revuelta, Susanna Spinsante
Date: 2017-10-31 00:00:00
Tasks: L2 Regularization

Several types of sensors have been available in off-the-shelf mobile devices,
including motion, magnetic, vision, acoustic, and location sensors. This paper
focuses on the fusion of the data acquired from motion and magnetic sensors,
i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of
Activities of Daily Living (ADL) using pattern recognition techniques. The
system developed in this study includes data acquisition, data processing, data
fusion, and artificial intelligence methods. Artificial Neural Networks (ANN)
are included in artificial intelligence methods, which are used in this study
for the recognition of ADL. The purpose of this study is the creation of a new
method using ANN for the identification of ADL, comparing three types of ANN,
in order to achieve results with a reliable accuracy. The best accuracy was
obtained with Deep Learning, which, after the application of the L2
regularization and normalization techniques on the sensors data, reports an
accuracy of 89.51%.
How intelligent are convolutional neural networks?
Authors: Zhennan Yan, Xiang Sean Zhou
Date: 2017-09-18 00:00:00

Motivated by the Gestalt pattern theory, and the Winograd Challenge for
language understanding, we design synthetic experiments to investigate a deep
learning algorithm's ability to infer simple (at least for human) visual
concepts, such as symmetry, from examples. A visual concept is represented by
randomly generated, positive as well as negative, example images. We then test
the ability and speed of algorithms (and humans) to learn the concept from
these images. The training and testing are performed progressively in multiple
rounds, with each subsequent round deliberately designed to be more complex and
confusing than the previous round(s), especially if the concept was not grasped
by the learner. However, if the concept was understood, all the deliberate
tests would become trivially easy. Our experiments show that humans can often
infer a semantic concept quickly after looking at only a very small number of
examples (this is often referred to as an "aha moment": a moment of sudden
realization), and performs perfectly during all testing rounds (except for
careless mistakes). On the contrary, deep convolutional neural networks (DCNN)
could approximate some concepts statistically, but only after seeing many
(x10^4) more examples. And it will still make obvious mistakes, especially
during deliberate testing rounds or on samples outside the training
distributions. This signals a lack of true "understanding", or a failure to
reach the right "formula" for the semantics. We did find that some concepts are
easier for DCNN than others. For example, simple "counting" is more learnable
than "symmetry", while "uniformity" or "conformance" are much more difficult
for DCNN to learn. To conclude, we propose an "Aha Challenge" for visual
perception, calling for focused and quantitative research on Gestalt-style
machine intelligence using limited training examples.
Summarizing Dialogic Arguments from Social Media
Authors: Amita Misra, Shereen Oraby, Shubhangi Tandon, Sharath TS, Pranav Anand, Marilyn Walker
Date: 2017-10-31 00:00:00

Online argumentative dialog is a rich source of information on popular
beliefs and opinions that could be useful to companies as well as governmental
or public policy agencies. Compact, easy to read, summaries of these dialogues
would thus be highly valuable. A priori, it is not even clear what form such a
summary should take. Previous work on summarization has primarily focused on
summarizing written texts, where the notion of an abstract of the text is well
defined. We collect gold standard training data consisting of five human
summaries for each of 161 dialogues on the topics of Gay Marriage, Gun Control
and Abortion. We present several different computational models aimed at
identifying segments of the dialogues whose content should be used for the
summary, using linguistic features and Word2vec features with both SVMs and
Bidirectional LSTMs. We show that we can identify the most important arguments
by using the dialog context with a best F-measure of 0.74 for gun control, 0.71
for gay marriage, and 0.67 for abortion.
Semantic Image Retrieval via Active Grounding of Visual Situations
Authors: Max H. Quinn, Erik Conser, Jordan M. Witte, Melanie Mitchell
Date: 2017-10-31 00:00:00
Tasks: Image Retrieval, Retrieval

We describe a novel architecture for semantic image retrieval---in
particular, retrieval of instances of visual situations. Visual situations are
concepts such as "a boxing match," "walking the dog," "a crowd waiting for a
bus," or "a game of ping-pong," whose instantiations in images are linked more
by their common spatial and semantic structure than by low-level visual
similarity. Given a query situation description, our architecture---called
Situate---learns models capturing the visual features of expected objects as
well the expected spatial configuration of relationships among objects. Given a
new image, Situate uses these models in an attempt to ground (i.e., to create a
bounding box locating) each expected component of the situation in the image
via an active search procedure. Situate uses the resulting grounding to compute
a score indicating the degree to which the new image is judged to contain an
instance of the situation. Such scores can be used to rank images in a
collection as part of a retrieval system. In the preliminary study described
here, we demonstrate the promise of this system by comparing Situate's
performance with that of two baseline methods, as well as with a related
semantic image-retrieval system based on "scene graphs."
A Goal-Based Movement Model for Continuous Multi-Agent Tasks
Authors: Shariq Iqbal, John Pearson
Date: 2017-02-23 00:00:00
Tasks: Reinforcement Learning, Time Series, Time Series Analysis

Despite increasing attention paid to the need for fast, scalable methods to
analyze next-generation neuroscience data, comparatively little attention has
been paid to the development of similar methods for behavioral analysis. Just
as the volume and complexity of brain data have grown, behavioral paradigms in
systems neuroscience have likewise become more naturalistic and less
constrained, necessitating an increase in the flexibility and scalability of
the models used to study them. In particular, key assumptions made in the
analysis of typical decision paradigms --- optimality; analytic tractability;
discrete, low-dimensional action spaces --- may be untenable in richer tasks.
Here, using the case of a two-player, real-time, continuous strategic game as
an example, we show how the use of modern machine learning methods allows us to
relax each of these assumptions. Following an inverse reinforcement learning
approach, we are able to succinctly characterize the joint distribution over
players' actions via a generative model that allows us to simulate realistic
game play. We compare simulated play from a number of generative time series
models and show that ours successfully resists mode collapse while generating
trajectories with the rich variability of real behavior. Together, these
methods offer a rich class of models for the analysis of continuous action
tasks at the single-trial level.
Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset
Authors: Alejandro Schuler, Ken Jung, Robert Tibshirani, Trevor Hastie, Nigam Shah
Date: 2017-10-31 00:00:00
Tasks: Causal Inference

Many decisions in healthcare, business, and other policy domains are made
without the support of rigorous evidence due to the cost and complexity of
performing randomized experiments. Using observational data to answer causal
questions is risky: subjects who receive different treatments also differ in
other ways that affect outcomes. Many causal inference methods have been
developed to mitigate these biases. However, there is no way to know which
method might produce the best estimate of a treatment effect in a given study.
In analogy to cross-validation, which estimates the prediction error of
predictive models applied to a given dataset, we propose synth-validation, a
procedure that estimates the estimation error of causal inference methods
applied to a given dataset. In synth-validation, we use the observed data to
estimate generative distributions with known treatment effects. We apply each
causal inference method to datasets sampled from these distributions and
compare the effect estimates with the known effects to estimate error. Using
simulations, we show that using synth-validation to select a causal inference
method for each study lowers the expected estimation error relative to
consistently using any single method.
Calibration for Stratified Classification Models
Authors: Chandler Zuo
Date: 2017-10-31 00:00:00
Tasks: Classification, General Classification

In classification problems, sampling bias between training data and testing
data is critical to the ranking performance of classification scores. Such bias
can be both unintentionally introduced by data collection and intentionally
introduced by the algorithm, such as under-sampling or weighting techniques
applied to imbalanced data. When such sampling bias exists, using the raw
classification score to rank observations in the testing data can lead to
suboptimal results. In this paper, I investigate the optimal calibration
strategy in general settings, and develop a practical solution for one specific
sampling bias case, where the sampling bias is introduced by stratified
sampling. The optimal solution is developed by analytically solving the problem
of optimizing the ROC curve. For practical data, I propose a ranking algorithm
for general classification models with stratified data. Numerical experiments
demonstrate that the proposed algorithm effectively addresses the stratified
sampling bias issue. Interestingly, the proposed method shows its potential
applicability in two other machine learning areas: unsupervised learning and
model ensembling, which can be future research topics.