pwc-archive/papers-with-abstracts huggingface dataset:
Creating Causal Embeddings for Question Answering with Minimal Supervision
Authors: Rebecca Sharp, Mihai Surdeanu, Peter Jansen, Peter Clark, Michael Hammond
Date: 2016-09-26 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Question Answering, Reranking, Word Embeddings

A common model for question answering (QA) is that a good answer is one that
is closely related to the question, where relatedness is often determined using
general-purpose lexical models such as word embeddings. We argue that a better
approach is to look for answers that are related to the question in a relevant
way, according to the information need of the question, which may be determined
through task-specific embeddings. With causality as a use case, we implement
this insight in three steps. First, we generate causal embeddings
cost-effectively by bootstrapping cause-effect pairs extracted from free text
using a small set of seed patterns. Second, we train dedicated embeddings over
this data, by using task-specific contexts, i.e., the context of a cause is its
effect. Finally, we extend a state-of-the-art reranking approach for QA to
incorporate these causal embeddings. We evaluate the causal embedding models
both directly with a casual implication task, and indirectly, in a downstream
causal QA task using data from Yahoo! Answers. We show that explicitly modeling
causality improves performance in both tasks. In the QA task our best model
achieves 37.3% P@1, significantly outperforming a strong baseline by 7.7%
(relative).
Toward Socially-Infused Information Extraction: Embedding Authors, Mentions, and Entities
Authors: Yi Yang, Ming-Wei Chang, Jacob Eisenstein
Date: 2016-09-26 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Entity Linking, Structured Prediction

Entity linking is the task of identifying mentions of entities in text, and
linking them to entries in a knowledge base. This task is especially difficult
in microblogs, as there is little additional text to provide disambiguating
context; rather, authors rely on an implicit common ground of shared knowledge
with their readers. In this paper, we attempt to capture some of this implicit
context by exploiting the social network structure in microblogs. We build on
the theory of homophily, which implies that socially linked individuals share
interests, and are therefore likely to mention the same sorts of entities. We
implement this idea by encoding authors, mentions, and entities in a continuous
vector space, which is constructed so that socially-connected authors have
similar vector representations. These vectors are incorporated into a neural
structured prediction model, which captures structural constraints that are
inherent in the entity linking task. Together, these design decisions yield F1
improvements of 1%-5% on benchmark datasets, as compared to the previous
state-of-the-art.
Swipe Mosaics from Video
Authors: Malcolm Reynolds, Tom S. F. Haines, Gabriel J. Brostow
Date: 2016-09-26 00:00:00
Tasks: Visual Odometry

A panoramic image mosaic is an attractive visualization for viewing many
overlapping photos, but its images must be both captured and processed
correctly to produce an acceptable composite. We propose Swipe Mosaics, an
interactive visualization that places the individual video frames on a 2D
planar map that represents the layout of the physical scene. Compared to
traditional panoramic mosaics, our capture is easier because the user can both
translate the camera center and film moving subjects. Processing and display
degrade gracefully if the footage lacks distinct, overlapping, non-repeating
texture. Our proposed visual odometry algorithm produces a distribution over
(x,y) translations for image pairs. Inferring a distribution of possible camera
motions allows us to better cope with parallax, lack of texture, dynamic
scenes, and other phenomena that hurt deterministic reconstruction techniques.
Robustness is obtained by training on synthetic scenes with known camera
motions. We show that Swipe Mosaics are easy to generate, support a wide range
of difficult scenes, and are useful for documenting a scene for closer
inspection.
S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking
Authors: Yi Yang, Ming-Wei Chang
Date: 2016-09-26 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Entity Linking

Non-linear models recently receive a lot of attention as people are starting
to discover the power of statistical and embedding features. However,
tree-based models are seldom studied in the context of structured learning
despite their recent success on various classification and ranking tasks. In
this paper, we propose S-MART, a tree-based structured learning framework based
on multiple additive regression trees. S-MART is especially suitable for
handling tasks with dense features, and can be used to learn many different
structures under various loss functions.
  We apply S-MART to the task of tweet entity linking --- a core component of
tweet information extraction, which aims to identify and link name mentions to
entities in a knowledge base. A novel inference algorithm is proposed to handle
the special structure of the task. The experimental results show that S-MART
significantly outperforms state-of-the-art tweet entity linking systems.
Constrained Cohort Intelligence using Static and Dynamic Penalty Function Approach for Mechanical Components Design
Authors: Omkar Kulkarni, Ninad Kulkarni, Anand J Kulkarni, Ganesh Kakandikar
Date: 2016-09-26 00:00:00

Most of the metaheuristics can efficiently solve unconstrained problems;
however, their performance may degenerate if the constraints are involved. This
paper proposes two constraint handling approaches for an emerging metaheuristic
of Cohort Intelligence (CI). More specifically CI with static penalty function
approach (SCI) and CI with dynamic penalty function approach (DCI) are
proposed. The approaches have been tested by solving several constrained test
problems. The performance of the SCI and DCI have been compared with algorithms
like GA, PSO, ABC, d-Ds. In addition, as well as three real world problems from
mechanical engineering domain with improved solutions. The results were
satisfactory and validated the applicability of CI methodology for solving real
world problems.
Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification
Authors: Rahul Rama Varior, Mrinal Haloi, Gang Wang
Date: 2016-07-28 00:00:00
Tasks: Person Re-Identification

Matching pedestrians across multiple camera views, known as human
re-identification, is a challenging research problem that has numerous
applications in visual surveillance. With the resurgence of Convolutional
Neural Networks (CNNs), several end-to-end deep Siamese CNN architectures have
been proposed for human re-identification with the objective of projecting the
images of similar pairs (i.e. same identity) to be closer to each other and
those of dissimilar pairs to be distant from each other. However, current
networks extract fixed representations for each image regardless of other
images which are paired with it and the comparison with other images is done
only at the final level. In this setting, the network is at risk of failing to
extract finer local patterns that may be essential to distinguish positive
pairs from hard negative pairs. In this paper, we propose a gating function to
selectively emphasize such fine common local patterns by comparing the
mid-level features across pairs of images. This produces flexible
representations for the same image according to the images they are paired
with. We conduct experiments on the CUHK03, Market-1501 and VIPeR datasets and
demonstrate improved performance compared to a baseline Siamese CNN
architecture.
Diversity Enhancement for Micro-Differential Evolution
Authors: Hojjat Salehinejad, Shahryar Rahnamayan, Hamid. R. Tizhoosh
Date: 2015-12-25 00:00:00
Tasks: Diversity

The differential evolution (DE) algorithm suffers from high computational
time due to slow nature of evaluation. In contrast, micro-DE (MDE) algorithms
employ a very small population size, which can converge faster to a reasonable
solution. However, these algorithms are vulnerable to a premature convergence
as well as to high risk of stagnation. In this paper, MDE algorithm with
vectorized random mutation factor (MDEVM) is proposed, which utilizes the small
size population benefit while empowers the exploration ability of mutation
factor through randomizing it in the decision variable level. The idea is
supported by analyzing mutation factor using Monte-Carlo based simulations. To
facilitate the usage of MDE algorithms with very-small population sizes, new
mutation schemes for population sizes less than four are also proposed.
Furthermore, comprehensive comparative simulations and analysis on performance
of the MDE algorithms over various mutation schemes, population sizes, problem
types (i.e. uni-modal, multi-modal, and composite), problem dimensionalities,
and mutation factor ranges are conducted by considering population diversity
analysis for stagnation and trapping in local optimum situations. The studies
are conducted on 28 benchmark functions provided for the IEEE CEC-2013
competition. Experimental results demonstrate high performance and convergence
speed of the proposed MDEVM algorithm.
Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions
Authors: Arijit Ray, Gordon Christie, Mohit Bansal, Dhruv Batra, Devi Parikh
Date: 2016-06-21 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Question Answering, Question Similarity, Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) is the task of answering natural-language
questions about images. We introduce the novel problem of determining the
relevance of questions to images in VQA. Current VQA models do not reason about
whether a question is even related to the given image (e.g. What is the capital
of Argentina?) or if it requires information from external resources to answer
correctly. This can break the continuity of a dialogue in human-machine
interaction. Our approaches for determining relevance are composed of two
stages. Given an image and a question, (1) we first determine whether the
question is visual or not, (2) if visual, we determine whether the question is
relevant to the given image or not. Our approaches, based on LSTM-RNNs, VQA
model uncertainty, and caption-question similarity, are able to outperform
strong baselines on both relevance tasks. We also present human studies showing
that VQA models augmented with such question relevance reasoning are perceived
as more intelligent, reasonable, and human-like.
A computer program for simulating time travel and a possible 'solution' for the grandfather paradox
Authors: Doron Friedman
Date: 2016-09-26 00:00:00

While the possibility of time travel in physics is still debated, the
explosive growth of virtual-reality simulations opens up new possibilities to
rigorously explore such time travel and its consequences in the digital domain.
Here we provide a computational model of time travel and a computer program
that allows exploring digital time travel. In order to explain our method we
formalize a simplified version of the famous grandfather paradox, show how the
system can allow the participant to go back in time, try to kill their
ancestors before they were born, and experience the consequences. The system
has even come up with scenarios that can be considered consistent "solutions"
of the grandfather paradox. We discuss the conditions for digital time travel,
which indicate that it has a large number of practical applications.
Grounding object perception in a naive agent's sensorimotor experience
Authors: Alban Laflaquière, Nikolas Hemion
Date: 2016-09-26 00:00:00
Tasks: Object

Artificial object perception usually relies on a priori defined models and
feature extraction algorithms. We study how the concept of object can be
grounded in the sensorimotor experience of a naive agent. Without any knowledge
about itself or the world it is immersed in, the agent explores its
sensorimotor space and identifies objects as consistent networks of
sensorimotor transitions, independent from their context. A fundamental drive
for prediction is assumed to explain the emergence of such networks from a
developmental standpoint. An algorithm is proposed and tested to illustrate the
approach.
Effective Blind Source Separation Based on the Adam Algorithm
Authors: Michele Scarpiniti, Simone Scardapane, Danilo Comminiello, Raffaele Parisi, Aurelio Uncini
Date: 2016-05-25 00:00:00
Tasks: blind source separation, Stochastic Optimization

In this paper, we derive a modified InfoMax algorithm for the solution of
Blind Signal Separation (BSS) problems by using advanced stochastic methods.
The proposed approach is based on a novel stochastic optimization approach
known as the Adaptive Moment Estimation (Adam) algorithm. The proposed BSS
solution can benefit from the excellent properties of the Adam approach. In
order to derive the new learning rule, the Adam algorithm is introduced in the
derivation of the cost function maximization in the standard InfoMax algorithm.
The natural gradient adaptation is also considered. Finally, some experimental
results show the effectiveness of the proposed approach.
Reasoning About Pragmatics with Neural Listeners and Speakers
Authors: Jacob Andreas, Dan Klein
Date: 2016-04-02 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Referring Expression, Text Generation

We present a model for pragmatically describing scenes, in which contrastive
behavior results from a combination of inference-driven pragmatics and learned
semantics. Like previous learned approaches to language generation, our model
uses a simple feature-driven architecture (here a pair of neural "listener" and
"speaker" models) to ground language in the world. Like inference-driven
approaches to pragmatics, our model actively reasons about listener behavior
when selecting utterances. For training, our approach requires only ordinary
captions, annotated _without_ demonstration of the pragmatic behavior the model
ultimately exhibits. In human evaluations on a referring expression game, our
approach succeeds 81% of the time, compared to a 69% success rate using
existing techniques.
Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
Authors: Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Date: 2016-09-05 00:00:00
Tasks: Bayesian Optimization

This work presents PESMOC, Predictive Entropy Search for Multi-objective
Bayesian Optimization with Constraints, an information-based strategy for the
simultaneous optimization of multiple expensive-to-evaluate black-box functions
under the presence of several constraints. PESMOC can hence be used to solve a
wide range of optimization problems. Iteratively, PESMOC chooses an input
location on which to evaluate the objective functions and the constraints so as
to maximally reduce the entropy of the Pareto set of the corresponding
optimization problem. The constraints considered in PESMOC are assumed to have
similar properties to those of the objective functions in typical Bayesian
optimization problems. That is, they do not have a known expression (which
prevents gradient computation), their evaluation is considered to be very
expensive, and the resulting observations may be corrupted by noise. These
constraints arise in a plethora of expensive black-box optimization problems.
We carry out synthetic experiments to illustrate the effectiveness of PESMOC,
where we sample both the objectives and the constraints from a Gaussian process
prior. The results obtained show that PESMOC is able to provide better
recommendations with a smaller number of evaluations than a strategy based on
random search.
Distributed Iterative Learning Control for a Team of Quadrotors
Authors: Andreas Hock, Angela P. Schoellig
Date: 2016-03-18 00:00:00

The goal of this work is to enable a team of quadrotors to learn how to
accurately track a desired trajectory while holding a given formation. We solve
this problem in a distributed manner, where each vehicle has only access to the
information of its neighbors. The desired trajectory is only available to one
(or few) vehicles. We present a distributed iterative learning control (ILC)
approach where each vehicle learns from the experience of its own and its
neighbors' previous task repetitions, and adapts its feedforward input to
improve performance. Existing algorithms are extended in theory to make them
more applicable to real-world experiments. In particular, we prove stability
for any causal learning function with gains chosen according to a simple scalar
condition. Previous proofs were restricted to a specific learning function that
only depends on the tracking error derivative (D-type ILC). Our extension
provides more degrees of freedom in the ILC design and, as a result, better
performance can be achieved. We also show that stability is not affected by a
linear dynamic coupling between neighbors. This allows us to use an additional
consensus feedback controller to compensate for non-repetitive disturbances.
Experiments with two quadrotors attest the effectiveness of the proposed
distributed multi-agent ILC approach. This is the first work to show
distributed ILC in experiment.
3D Face Reconstruction by Learning from Synthetic Data
Authors: Elad Richardson, Matan Sela, Ron Kimmel
Date: 2016-09-14 00:00:00
Tasks: 3D Face Reconstruction, Face Reconstruction

Fast and robust three-dimensional reconstruction of facial geometric
structure from a single image is a challenging task with numerous applications.
Here, we introduce a learning-based approach for reconstructing a
three-dimensional face from a single image. Recent face recovery methods rely
on accurate localization of key characteristic points. In contrast, the
proposed approach is based on a Convolutional-Neural-Network (CNN) which
extracts the face geometry directly from its image. Although such deep
architectures outperform other models in complex computer vision problems,
training them properly requires a large dataset of annotated examples. In the
case of three-dimensional faces, currently, there are no large volume data
sets, while acquiring such big-data is a tedious task. As an alternative, we
propose to generate random, yet nearly photo-realistic, facial images for which
the geometric form is known. The suggested model successfully recovers facial
shapes from real images, even for faces with extreme expressions and under
various lighting conditions.
Convolutional Two-Stream Network Fusion for Video Action Recognition
Authors: Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman
Date: 2016-04-22 00:00:00
Proceeding: CVPR 2016 6
Tasks: Action Recognition, Action Recognition In Videos, Temporal Action Localization, Vocal Bursts Valence Prediction

Recent applications of Convolutional Neural Networks (ConvNets) for human
action recognition in videos have proposed different solutions for
incorporating the appearance and motion information. We study a number of ways
of fusing ConvNet towers both spatially and temporally in order to best take
advantage of this spatio-temporal information. We make the following findings:
(i) that rather than fusing at the softmax layer, a spatial and temporal
network can be fused at a convolution layer without loss of performance, but
with a substantial saving in parameters; (ii) that it is better to fuse such
networks spatially at the last convolutional layer than earlier, and that
additionally fusing at the class prediction layer can boost accuracy; finally
(iii) that pooling of abstract convolutional features over spatiotemporal
neighbourhoods further boosts performance. Based on these studies we propose a
new ConvNet architecture for spatiotemporal fusion of video snippets, and
evaluate its performance on standard benchmarks where this architecture
achieves state-of-the-art results.
Feature Selection: A Data Perspective
Authors: Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P. Trevino, Jiliang Tang, Huan Liu
Date: 2016-01-29 00:00:00
Tasks: feature selection, Sparse Learning, Survey

Feature selection, as a data preprocessing strategy, has been proven to be
effective and efficient in preparing data (especially high-dimensional data)
for various data mining and machine learning problems. The objectives of
feature selection include: building simpler and more comprehensible models,
improving data mining performance, and preparing clean, understandable data.
The recent proliferation of big data has presented some substantial challenges
and opportunities to feature selection. In this survey, we provide a
comprehensive and structured overview of recent advances in feature selection
research. Motivated by current challenges and opportunities in the era of big
data, we revisit feature selection research from a data perspective and review
representative feature selection algorithms for conventional data, structured
data, heterogeneous data and streaming data. Methodologically, to emphasize the
differences and similarities of most existing feature selection algorithms for
conventional data, we categorize them into four main groups: similarity based,
information theoretical based, sparse learning based and statistical based
methods. To facilitate and promote the research in this community, we also
present an open-source feature selection repository that consists of most of
the popular feature selection algorithms
(\url{http://featureselection.asu.edu/}). Also, we use it as an example to show
how to evaluate feature selection algorithms. At the end of the survey, we
present a discussion about some open problems and challenges that require more
attention in future research.
Visual Stability Prediction and Its Application to Manipulation
Authors: Wenbin Li, Aleš Leonardis, Mario Fritz
Date: 2016-09-15 00:00:00
Tasks: Prediction

Understanding physical phenomena is a key competence that enables humans and
animals to act and interact under uncertain perception in previously unseen
environments containing novel objects and their configurations. Developmental
psychology has shown that such skills are acquired by infants from observations
at a very early stage.
  In this paper, we contrast a more traditional approach of taking a
model-based route with explicit 3D representations and physical simulation by
an {\em end-to-end} approach that directly predicts stability from appearance.
We ask the question if and to what extent and quality such a skill can directly
be acquired in a data-driven way---bypassing the need for an explicit
simulation at run-time.
  We present a learning-based approach based on simulated data that predicts
stability of towers comprised of wooden blocks under different conditions and
quantities related to the potential fall of the towers. We first evaluate the
approach on synthetic data and compared the results to human judgments on the
same stimuli. Further, we extend this approach to reason about future states of
such towers that in turn enables successful stacking.