pwc-archive/papers-with-abstracts huggingface dataset:
Question Answering via Integer Programming over Semi-Structured Knowledge
Authors: Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Peter Clark, Oren Etzioni, Dan Roth
Date: 2016-04-20 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval

Answering science questions posed in natural language is an important AI
challenge. Answering such questions often requires non-trivial inference and
knowledge that goes beyond factoid retrieval. Yet, most systems for this task
are based on relatively shallow Information Retrieval (IR) and statistical
correlation techniques operating on large unstructured corpora. We propose a
structured inference system for this task, formulated as an Integer Linear
Program (ILP), that answers natural language questions using a semi-structured
knowledge base derived from text, including questions requiring multi-step
inference and a combination of multiple facts. On a dataset of real, unseen
science questions, our system significantly outperforms (+14%) the best
previous attempt at structured reasoning for this task, which used Markov Logic
Networks (MLNs). It also improves upon a previous ILP formulation by 17.7%.
When combined with unstructured inference methods, the ILP system significantly
boosts overall performance (+10%). Finally, we show our approach is
substantially more robust to a simple answer perturbation compared to
statistical correlation methods.
ARSENAL: Automatic Requirements Specification Extraction from Natural Language
Authors: Shalini Ghosh, Daniel Elenius, Wenchao Li, Patrick Lincoln, Natarajan Shankar, Wilfried Steiner
Date: 2014-03-13 00:00:00

Requirements are informal and semi-formal descriptions of the expected
behavior of a complex system from the viewpoints of its stakeholders
(customers, users, operators, designers, and engineers). However, for the
purpose of design, testing, and verification for critical systems, we can
transform requirements into formal models that can be analyzed automatically.
ARSENAL is a framework and methodology for systematically transforming natural
language (NL) requirements into analyzable formal models and logic
specifications. These models can be analyzed for consistency and
implementability. The ARSENAL methodology is specialized to individual domains,
but the approach is general enough to be adapted to new domains.
Random Projection Estimation of Discrete-Choice Models with Large Choice Sets
Authors: Khai X. Chiong, Matthew Shum
Date: 2016-04-20 00:00:00
Tasks: Data Compression, Dimensionality Reduction, Discrete Choice Models, LEMMA

We introduce sparse random projection, an important dimension-reduction tool
from machine learning, for the estimation of discrete-choice models with
high-dimensional choice sets. Initially, high-dimensional data are compressed
into a lower-dimensional Euclidean space using random projections.
Subsequently, estimation proceeds using cyclic monotonicity moment inequalities
implied by the multinomial choice model; the estimation procedure is
semi-parametric and does not require explicit distributional assumptions to be
made regarding the random utility errors. The random projection procedure is
justified via the Johnson-Lindenstrauss Lemma -- the pairwise distances between
data points are preserved during data compression, which we exploit to show
convergence of our estimator. The estimator works well in simulations and in an
application to a supermarket scanner dataset.
Constructive Preference Elicitation by Setwise Max-margin Learning
Authors: Stefano Teso, Andrea Passerini, Paolo Viappiani
Date: 2016-04-20 00:00:00

In this paper we propose an approach to preference elicitation that is
suitable to large configuration spaces beyond the reach of existing
state-of-the-art approaches. Our setwise max-margin method can be viewed as a
generalization of max-margin learning to sets, and can produce a set of
"diverse" items that can be used to ask informative queries to the user.
Moreover, the approach can encourage sparsity in the parameter space, in order
to favor the assessment of utility towards combinations of weights that
concentrate on just few features. We present a mixed integer linear programming
formulation and show how our approach compares favourably with Bayesian
preference elicitation alternatives and easily scales to realistic datasets.
Greedy Criterion in Orthogonal Greedy Learning
Authors: Lin Xu, Shao-Bo Lin, Jinshan Zeng, Xia Liu, Zongben Xu
Date: 2016-04-20 00:00:00

Orthogonal greedy learning (OGL) is a stepwise learning scheme that starts
with selecting a new atom from a specified dictionary via the steepest gradient
descent (SGD) and then builds the estimator through orthogonal projection. In
this paper, we find that SGD is not the unique greedy criterion and introduce a
new greedy criterion, called "$\delta$-greedy threshold" for learning. Based on
the new greedy criterion, we derive an adaptive termination rule for OGL. Our
theoretical study shows that the new learning scheme can achieve the existing
(almost) optimal learning rate of OGL. Plenty of numerical experiments are
provided to support that the new scheme can achieve almost optimal
generalization performance, while requiring less computation than OGL.
Computational Drug Repositioning Using Continuous Self-controlled Case Series
Authors: Zhaobin Kuang, James Thomson, Michael Caldwell, Peggy Peissig, Ron Stewart, David Page
Date: 2016-04-20 00:00:00

Computational Drug Repositioning (CDR) is the task of discovering potential
new indications for existing drugs by mining large-scale heterogeneous
drug-related data sources. Leveraging the patient-level temporal ordering
information between numeric physiological measurements and various drug
prescriptions provided in Electronic Health Records (EHRs), we propose a
Continuous Self-controlled Case Series (CSCCS) model for CDR. As an initial
evaluation, we look for drugs that can control Fasting Blood Glucose (FBG)
level in our experiments. Applying CSCCS to the Marshfield Clinic EHR,
well-known drugs that are indicated for controlling blood glucose level are
rediscovered. Furthermore, some drugs with recent literature support for the
potential effect of blood glucose level control are also identified.
Multiplayer Games for Learning Multirobot Coordination Algorithms
Authors: Arash Tavakoli, Haig Nalbandian, Nora Ayanian
Date: 2016-04-20 00:00:00
Tasks: Decision Making

Humans have an impressive ability to solve complex coordination problems in a
fully distributed manner. This ability, if learned as a set of distributed
multirobot coordination strategies, can enable programming large groups of
robots to collaborate towards complex coordination objectives in a way similar
to humans. Such strategies would offer robustness, adaptability,
fault-tolerance, and, importantly, distributed decision-making. To that end, we
have designed a networked gaming platform to investigate human group behavior,
specifically in solving complex collaborative coordinated tasks. Through this
platform, we are able to limit the communication, sensing, and actuation
capabilities provided to the players. With the aim of learning coordination
algorithms for robots in mind, we define these capabilities to mimic those of a
simple ground robot.
Parametric Object Motion from Blur
Authors: Jochen Gast, Anita Sellent, Stefan Roth
Date: 2016-04-20 00:00:00
Proceeding: CVPR 2016 6
Tasks: Deblurring, Motion Segmentation, Object, Optical Flow Estimation, Segmentation

Motion blur can adversely affect a number of vision tasks, hence it is
generally considered a nuisance. We instead treat motion blur as a useful
signal that allows to compute the motion of objects from a single image.
Drawing on the success of joint segmentation and parametric motion models in
the context of optical flow estimation, we propose a parametric object motion
model combined with a segmentation mask to exploit localized, non-uniform
motion blur. Our parametric image formation model is differentiable w.r.t. the
motion parameters, which enables us to generalize marginal-likelihood
techniques from uniform blind deblurring to localized, non-uniform blur. A
two-stage pipeline, first in derivative space and then in image space, allows
to estimate both parametric object motion as well as a motion segmentation from
a single image alone. Our experiments demonstrate its ability to cope with very
challenging cases of object motion blur.
A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion
Authors: Johannes Welbl, Guillaume Bouchard, Sebastian Riedel
Date: 2016-04-20 00:00:00
Proceeding: WS 2016 6
Tasks: Knowledge Base Completion

Embedding-based Knowledge Base Completion models have so far mostly combined
distributed representations of individual entities or relations to compute
truth scores of missing links. Facts can however also be represented using
pairwise embeddings, i.e. embeddings for pairs of entities and relations. In
this paper we explore such bigram embeddings with a flexible Factorization
Machine model and several ablations from it. We investigate the relevance of
various bigram types on the fb15k237 dataset and find relative improvements
compared to a compositional model.
Distributed Entity Disambiguation with Per-Mention Learning
Authors: Tiep Mai, Bichen Shi, Patrick K. Nicholson, Deepak Ajwani, Alessandra Sala
Date: 2016-04-20 00:00:00
Tasks: Entity Disambiguation

Entity disambiguation, or mapping a phrase to its canonical representation in
a knowledge base, is a fundamental step in many natural language processing
applications. Existing techniques based on global ranking models fail to
capture the individual peculiarities of the words and hence, either struggle to
meet the accuracy requirements of many real-world applications or they are too
complex to satisfy real-time constraints of applications.
  In this paper, we propose a new disambiguation system that learns specialized
features and models for disambiguating each ambiguous phrase in the English
language. To train and validate the hundreds of thousands of learning models
for this purpose, we use a Wikipedia hyperlink dataset with more than 170
million labelled annotations. We provide an extensive experimental evaluation
to show that the accuracy of our approach compares favourably with respect to
many state-of-the-art disambiguation systems. The training required for our
approach can be easily distributed over a cluster. Furthermore, updating our
system for new entities or calibrating it for special ones is a computationally
fast process, that does not affect the disambiguation of the other entities.
Scene Parsing with Integration of Parametric and Non-parametric Models
Authors: Bing Shuai, Zhen Zuo, Gang Wang, Bing Wang
Date: 2016-04-20 00:00:00
Tasks: Metric Learning, Scene Parsing

We adopt Convolutional Neural Networks (CNNs) to be our parametric model to
learn discriminative features and classifiers for local patch classification.
Based on the occurrence frequency distribution of classes, an ensemble of CNNs
(CNN-Ensemble) are learned, in which each CNN component focuses on learning
different and complementary visual patterns. The local beliefs of pixels are
output by CNN-Ensemble. Considering that visually similar pixels are
indistinguishable under local context, we leverage the global scene semantics
to alleviate the local ambiguity. The global scene constraint is mathematically
achieved by adding a global energy term to the labeling energy function, and it
is practically estimated in a non-parametric framework. A large margin based
CNN metric learning method is also proposed for better global belief
estimation. In the end, the integration of local and global beliefs gives rise
to the class likelihood of pixels, based on which maximum marginal inference is
performed to generate the label prediction maps. Even without any
post-processing, we achieve state-of-the-art results on the challenging
SiftFlow and Barcelona benchmarks.
Trading-Off Cost of Deployment Versus Accuracy in Learning Predictive Models
Authors: Daniel P. Robinson, Suchi Saria
Date: 2016-04-20 00:00:00
Tasks: Prediction

Predictive models are finding an increasing number of applications in many
industries. As a result, a practical means for trading-off the cost of
deploying a model versus its effectiveness is needed. Our work is motivated by
risk prediction problems in healthcare. Cost-structures in domains such as
healthcare are quite complex, posing a significant challenge to existing
approaches. We propose a novel framework for designing cost-sensitive
structured regularizers that is suitable for problems with complex cost
dependencies. We draw upon a surprising connection to boolean circuits. In
particular, we represent the problem costs as a multi-layer boolean circuit,
and then use properties of boolean circuits to define an extended feature
vector and a group regularizer that exactly captures the underlying cost
structure. The resulting regularizer may then be combined with a fidelity
function to perform model prediction, for example. For the challenging
real-world application of risk prediction for sepsis in intensive care units,
the use of our regularizer leads to models that are in harmony with the
underlying cost structure and thus provide an excellent prediction accuracy
versus cost tradeoff.
Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient Regularization
Authors: Hongyang Xue, Shengming Zhang, Deng Cai
Date: 2016-04-20 00:00:00
Tasks: Image Inpainting, Low-Rank Matrix Completion, Matrix Completion

We consider the case of inpainting single depth images. Without corresponding
color images, previous or next frames, depth image inpainting is quite
challenging. One natural solution is to regard the image as a matrix and adopt
the low rank regularization just as inpainting color images. However, the low
rank assumption does not make full use of the properties of depth images.
  A shallow observation may inspire us to penalize the non-zero gradients by
sparse gradient regularization. However, statistics show that though most
pixels have zero gradients, there is still a non-ignorable part of pixels whose
gradients are equal to 1. Based on this specific property of depth images , we
propose a low gradient regularization method in which we reduce the penalty for
gradient 1 while penalizing the non-zero gradients to allow for gradual depth
changes. The proposed low gradient regularization is integrated with the low
rank regularization into the low rank low gradient approach for depth image
inpainting. We compare our proposed low gradient regularization with sparse
gradient regularization. The experimental results show the effectiveness of our
proposed approach.
Sherlock: Sparse Hierarchical Embeddings for Visually-aware One-class Collaborative Filtering
Authors: Ruining He, Chunbin Lin, Jianguo Wang, Julian McAuley
Date: 2016-04-20 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Building successful recommender systems requires uncovering the underlying
dimensions that describe the properties of items as well as users' preferences
toward them. In domains like clothing recommendation, explaining users'
preferences requires modeling the visual appearance of the items in question.
This makes recommendation especially challenging, due to both the complexity
and subtlety of people's 'visual preferences,' as well as the scale and
dimensionality of the data and features involved. Ultimately, a successful
model should be capable of capturing considerable variance across different
categories and styles, while still modeling the commonalities explained by
`global' structures in order to combat the sparsity (e.g. cold-start),
variability, and scale of real-world datasets. Here, we address these
challenges by building such structures to model the visual dimensions across
different product categories. With a novel hierarchical embedding architecture,
our method accounts for both high-level (colorfulness, darkness, etc.) and
subtle (e.g. casualness) visual characteristics simultaneously.
Multi-agent evolutionary systems for the generation of complex virtual worlds
Authors: Jan Kruse, Andy M. Connor
Date: 2016-04-20 00:00:00

Modern films, games and virtual reality applications are dependent on
convincing computer graphics. Highly complex models are a requirement for the
successful delivery of many scenes and environments. While workflows such as
rendering, compositing and animation have been streamlined to accommodate
increasing demands, modelling complex models is still a laborious task. This
paper introduces the computational benefits of an Interactive Genetic Algorithm
(IGA) to computer graphics modelling while compensating the effects of user
fatigue, a common issue with Interactive Evolutionary Computation. An
intelligent agent is used in conjunction with an IGA that offers the potential
to reduce the effects of user fatigue by learning from the choices made by the
human designer and directing the search accordingly. This workflow accelerates
the layout and distribution of basic elements to form complex models. It
captures the designer's intent through interaction, and encourages playful
discovery.
Procedural urban environments for FPS games
Authors: Jan Kruse, Ricardo Sosa, Andy M. Connor
Date: 2016-04-20 00:00:00
Tasks: BIG-bench Machine Learning, FPS Games

This paper presents a novel approach to procedural generation of urban maps
for First Person Shooter (FPS) games. A multi-agent evolutionary system is
employed to place streets, buildings and other items inside the Unity3D game
engine, resulting in playable video game levels. A computational agent is
trained using machine learning techniques to capture the intent of the game
designer as part of the multi-agent system, and to enable a semi-automated
aesthetic selection for the underlying genetic algorithm.
Evaluation of Deep Learning based Pose Estimation for Sign Language Recognition
Authors: Srujana Gattupalli, Amir Ghaderi, Vassilis Athitsos
Date: 2016-02-29 00:00:00
Tasks: Deep Learning, Hand Detection, Pose Estimation, Sign Language Recognition, Transfer Learning

Human body pose estimation and hand detection are two important tasks for
systems that perform computer vision-based sign language recognition(SLR).
However, both tasks are challenging, especially when the input is color videos,
with no depth information. Many algorithms have been proposed in the literature
for these tasks, and some of the most successful recent algorithms are based on
deep learning. In this paper, we introduce a dataset for human pose estimation
for SLR domain. We evaluate the performance of two deep learning based pose
estimation methods, by performing user-independent experiments on our dataset.
We also perform transfer learning, and we obtain results that demonstrate that
transfer learning can improve pose estimation accuracy. The dataset and results
from these methods can create a useful baseline for future works.
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
Authors: Krishna Kumar Singh, Fanyi Xiao, Yong Jae Lee
Date: 2016-04-19 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, object-detection, Object Detection, Weakly Supervised Object Detection

The status quo approach to training object detectors requires expensive
bounding box annotations. Our framework takes a markedly different direction:
we transfer tracked object boxes from weakly-labeled videos to weakly-labeled
images to automatically generate pseudo ground-truth boxes, which replace
manually annotated bounding boxes. We first mine discriminative regions in the
weakly-labeled image collection that frequently/rarely appear in the
positive/negative images. We then match those regions to videos and retrieve
the corresponding tracked object boxes. Finally, we design a hough transform
algorithm to vote for the best box to serve as the pseudo GT for each image,
and use them to train an object detector. Together, these lead to
state-of-the-art weakly-supervised detection results on the PASCAL 2007 and
2010 datasets.
Sketching and Neural Networks
Authors: Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar
Date: 2016-04-19 00:00:00

High-dimensional sparse data present computational and statistical challenges
for supervised learning. We propose compact linear sketches for reducing the
dimensionality of the input, followed by a single layer neural network. We show
that any sparse polynomial function can be computed, on nearly all sparse
binary vectors, by a single layer neural network that takes a compact sketch of
the vector as input. Consequently, when a set of sparse binary vectors is
approximately separable using a sparse polynomial, there exists a single-layer
neural network that takes a short sketch as input and correctly classifies
nearly all the points. Previous work has proposed using sketches to reduce
dimensionality while preserving the hypothesis class. However, the sketch size
has an exponential dependence on the degree in the case of polynomial
classifiers. In stark contrast, our approach of using improper learning, using
a larger hypothesis class allows the sketch size to have a logarithmic
dependence on the degree. Even in the linear case, our approach allows us to
improve on the pesky $O({1}/{{\gamma}^2})$ dependence of random projections, on
the margin $\gamma$. We empirically show that our approach leads to more
compact neural networks than related methods such as feature hashing at equal
or better performance.
Syntactic and semantic classification of verb arguments using dependency-based and rich semantic features
Authors: Francesco Elia
Date: 2016-04-19 00:00:00
Tasks: General Classification, Word Embeddings

Corpus Pattern Analysis (CPA) has been the topic of Semeval 2015 Task 15,
aimed at producing a system that can aid lexicographers in their efforts to
build a dictionary of meanings for English verbs using the CPA annotation
process. CPA parsing is one of the subtasks which this annotation process is
made of and it is the focus of this report. A supervised machine-learning
approach has been implemented, in which syntactic features derived from parse
trees and semantic features derived from WordNet and word embeddings are used.
It is shown that this approach performs well, even with the data sparsity
issues that characterize the dataset, and can obtain better results than other
system by a margin of about 4% f-score.
Yin and Yang: Balancing and Answering Binary Visual Questions
Authors: Peng Zhang, Yash Goyal, Douglas Summers-Stay, Dhruv Batra, Devi Parikh
Date: 2015-11-16 00:00:00
Proceeding: CVPR 2016 6
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

The complex compositional structure of language makes problems at the
intersection of vision and language challenging. But language also provides a
strong prior that can result in good superficial performance, without the
underlying models truly understanding the visual content. This can hinder
progress in pushing state of art in the computer vision aspects of multi-modal
AI. In this paper, we address binary Visual Question Answering (VQA) on
abstract scenes. We formulate this problem as visual verification of concepts
inquired in the questions. Specifically, we convert the question to a tuple
that concisely summarizes the visual concept to be detected in the image. If
the concept can be found in the image, the answer to the question is "yes", and
otherwise "no". Abstract scenes play two roles (1) They allow us to focus on
the high-level semantics of the VQA task as opposed to the low-level
recognition problems, and perhaps more importantly, (2) They provide us the
modality to balance the dataset such that language priors are controlled, and
the role of vision is essential. In particular, we collect fine-grained pairs
of scenes for every question, such that the answer to the question is "yes" for
one scene, and "no" for the other for the exact same question. Indeed, language
priors alone do not perform better than chance on our balanced dataset.
Moreover, our proposed approach matches the performance of a state-of-the-art
VQA approach on the unbalanced dataset, and outperforms it on the balanced
dataset.