pwc-archive/papers-with-abstracts huggingface dataset:
Towards Optimal Algorithms for Prediction with Expert Advice
Authors: Nick Gravin, Yuval Peres, Balasubramanian Sivan
Date: 2014-09-10 00:00:00
Tasks: Prediction, Thompson Sampling

We study the classical problem of prediction with expert advice in the
adversarial setting with a geometric stopping time. In 1965, Cover gave the
optimal algorithm for the case of 2 experts. In this paper, we design the
optimal algorithm, adversary and regret for the case of 3 experts. Further, we
show that the optimal algorithm for $2$ and $3$ experts is a probability
matching algorithm (analogous to Thompson sampling) against a particular
randomized adversary. Remarkably, our proof shows that the probability matching
algorithm is not only optimal against this particular randomized adversary, but
also minimax optimal.
  Our analysis develops upper and lower bounds simultaneously, analogous to the
primal-dual method. Our analysis of the optimal adversary goes through delicate
asymptotics of the random walk of a particle between multiple walls. We use the
connection we develop to random walks to derive an improved algorithm and
regret bound for the case of $4$ experts, and, provide a general framework for
designing the optimal algorithm and adversary for an arbitrary number of
experts.
Efficient Activity Detection in Untrimmed Video with Max-Subgraph Search
Authors: Chao-Yeh Chen, Kristen Grauman
Date: 2016-07-11 00:00:00
Tasks: Action Detection, Activity Detection

We propose an efficient approach for activity detection in video that unifies
activity categorization with space-time localization. The main idea is to pose
activity detection as a maximum-weight connected subgraph problem. Offline, we
learn a binary classifier for an activity category using positive video
exemplars that are "trimmed" in time to the activity of interest. Then, given a
novel \emph{untrimmed} video sequence, we decompose it into a 3D array of
space-time nodes, which are weighted based on the extent to which their
component features support the learned activity model. To perform detection, we
then directly localize instances of the activity by solving for the
maximum-weight connected subgraph in the test video's space-time graph. We show
that this detection strategy permits an efficient branch-and-cut solution for
the best-scoring---and possibly non-cubically shaped---portion of the video for
a given activity classifier. The upshot is a fast method that can search a
broader space of space-time region candidates than was previously practical,
which we find often leads to more accurate detection. We demonstrate the
proposed algorithm on four datasets, and we show its speed and accuracy
advantages over multiple existing search strategies.
Mapping distributional to model-theoretic semantic spaces: a baseline
Authors: Franck Dernoncourt
Date: 2016-07-11 00:00:00
Tasks: Dependency Parsing, Question Answering, regression, Word Embeddings

Word embeddings have been shown to be useful across state-of-the-art systems
in many natural language processing tasks, ranging from question answering
systems to dependency parsing. (Herbelot and Vecchi, 2015) explored word
embeddings and their utility for modeling language semantics. In particular,
they presented an approach to automatically map a standard distributional
semantic space onto a set-theoretic model using partial least squares
regression. We show in this paper that a simple baseline achieves a +51%
relative improvement compared to their model on one of the two datasets they
used, and yields competitive results on the second dataset.
Syntactic Phylogenetic Trees
Authors: Kevin Shu, Sharjeel Aziz, Vy-Luan Huynh, David Warrick, Matilde Marcolli
Date: 2016-07-10 00:00:00

In this paper we identify several serious problems that arise in the use of
syntactic data from the SSWL database for the purpose of computational
phylogenetic reconstruction. We show that the most naive approach fails to
produce reliable linguistic phylogenetic trees. We identify some of the sources
of the observed problems and we discuss how they may be, at least partly,
corrected by using additional information, such as prior subdivision into
language families and subfamilies, and a better use of the information about
ancient languages. We also describe how the use of phylogenetic algebraic
geometry can help in estimating to what extent the probability distribution at
the leaves of the phylogenetic tree obtained from the SSWL data can be
considered reliable, by testing it on phylogenetic trees established by other
forms of linguistic analysis. In simple examples, we find that, after
restricting to smaller language subfamilies and considering only those SSWL
parameters that are fully mapped for the whole subfamily, the SSWL data match
extremely well reliable phylogenetic trees, according to the evaluation of
phylogenetic invariants. This is a promising sign for the use of SSWL data for
linguistic phylogenetics.
Charagram: Embedding Words and Sentences via Character n-grams
Authors: John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu
Date: 2016-07-10 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Part-Of-Speech Tagging, Sentence, Sentence Similarity, Word Similarity

We present Charagram embeddings, a simple approach for learning
character-based compositional models to embed textual sequences. A word or
sentence is represented using a character n-gram count vector, followed by a
single nonlinear transformation to yield a low-dimensional embedding. We use
three tasks for evaluation: word similarity, sentence similarity, and
part-of-speech tagging. We demonstrate that Charagram embeddings outperform
more complex architectures based on character-level recurrent and convolutional
neural networks, achieving new state-of-the-art performance on several
similarity tasks.
Open Information Extraction
Authors: Duc-Thuan Vo, Ebrahim Bagheri
Date: 2016-07-10 00:00:00
Tasks: Open Information Extraction, POS, Relation

Open Information Extraction (Open IE) systems aim to obtain relation tuples
with highly scalable extraction in portable across domain by identifying a
variety of relation phrases and their arguments in arbitrary sentences. The
first generation of Open IE learns linear chain models based on unlexicalized
features such as Part-of-Speech (POS) or shallow tags to label the intermediate
words between pair of potential arguments for identifying extractable
relations. Open IE currently is developed in the second generation that is able
to extract instances of the most frequently observed relation types such as
Verb, Noun and Prep, Verb and Prep, and Infinitive with deep linguistic
analysis. They expose simple yet principled ways in which verbs express
relationships in linguistics such as verb phrase-based extraction or
clause-based extraction. They obtain a significantly higher performance over
previous systems in the first generation. In this paper, we describe an
overview of two Open IE generations including strengths, weaknesses and
application areas.
Friction from Reflectance: Deep Reflectance Codes for Predicting Physical Surface Properties from One-Shot In-Field Reflectance
Authors: Hang Zhang, Kristin Dana, Ko Nishino
Date: 2016-03-25 00:00:00
Tasks: Friction, Scene Understanding

Images are the standard input for vision algorithms, but one-shot infield
reflectance measurements are creating new opportunities for recognition and
scene understanding. In this work, we address the question of what reflectance
can reveal about materials in an efficient manner. We go beyond the question of
recognition and labeling and ask the question: What intrinsic physical
properties of the surface can be estimated using reflectance? We introduce a
framework that enables prediction of actual friction values for surfaces using
one-shot reflectance measurements. This work is a first of its kind
vision-based friction estimation. We develop a novel representation for
reflectance disks that capture partial BRDF measurements instantaneously. Our
method of deep reflectance codes combines CNN features and fisher vector
pooling with optimal binary embedding to create codes that have sufficient
discriminatory power and have important properties of illumination and spatial
invariance. The experimental results demonstrate that reflectance can play a
new role in deciphering the underlying physical properties of real-world
scenes.
Annotation Methodologies for Vision and Language Dataset Creation
Authors: Gitit Kehat, James Pustejovsky
Date: 2016-07-10 00:00:00
Tasks: Action Recognition, Image Description, Question Answering, Temporal Action Localization, Visual Question Answering, Visual Question Answering (VQA)

Annotated datasets are commonly used in the training and evaluation of tasks
involving natural language and vision (image description generation, action
recognition and visual question answering). However, many of the existing
datasets reflect problems that emerge in the process of data selection and
annotation. Here we point out some of the difficulties and problems one
confronts when creating and validating annotated vision and language datasets.
KOGNAC: Efficient Encoding of Large Knowledge Graphs
Authors: Jacopo Urbani, Sourav Dutta, Sairam Gurajada, Gerhard Weikum
Date: 2016-04-16 00:00:00
Tasks: Knowledge Graphs

Many Web applications require efficient querying of large Knowledge Graphs
(KGs). We propose KOGNAC, a dictionary-encoding algorithm designed to improve
SPARQL querying with a judicious combination of statistical and semantic
techniques. In KOGNAC, frequent terms are detected with a frequency
approximation algorithm and encoded to maximise compression. Infrequent terms
are semantically grouped into ontological classes and encoded to increase data
locality. We evaluated KOGNAC in combination with state-of-the-art RDF engines,
and observed that it significantly improves SPARQL querying on KGs with up to
1B edges.
How to Allocate Resources For Features Acquisition?
Authors: Oran Richman, Shie Mannor
Date: 2016-07-10 00:00:00
Tasks: General Classification

We study classification problems where features are corrupted by noise and
where the magnitude of the noise in each feature is influenced by the resources
allocated to its acquisition. This is the case, for example, when multiple
sensors share a common resource (power, bandwidth, attention, etc.). We develop
a method for computing the optimal resource allocation for a variety of
scenarios and derive theoretical bounds concerning the benefit that may arise
by non-uniform allocation. We further demonstrate the effectiveness of the
developed method in simulations.
The BioDynaMo Project
Authors: Roman Bauer, Lukas Breitwieser, Alberto Di Meglio, Leonard Johard, Marcus Kaiser, Marco Manca, Manuel Mazzara, Max Talanov
Date: 2016-07-10 00:00:00
Tasks: Cloud Computing

Computer simulations have become a very powerful tool for scientific
research. Given the vast complexity that comes with many open scientific
questions, a purely analytical or experimental approach is often not viable.
For example, biological systems (such as the human brain) comprise an extremely
complex organization and heterogeneous interactions across different spatial
and temporal scales. In order to facilitate research on such problems, the
BioDynaMo project (\url{https://biodynamo.web.cern.ch/}) aims at a general
platform for computer simulations for biological research. Since the scientific
investigations require extensive computer resources, this platform should be
executable on hybrid cloud computing systems, allowing for the efficient use of
state-of-the-art computing technology. This paper describes challenges during
the early stages of the software development process. In particular, we
describe issues regarding the implementation and the highly interdisciplinary
as well as international nature of the collaboration. Moreover, we explain the
methodologies, the approach, and the lessons learnt by the team during these
first stages.
Dealing with Class Imbalance using Thresholding
Authors: Charmgil Hong, Rumi Ghosh, Soundar Srinivasan
Date: 2016-07-10 00:00:00
Tasks: Classification, General Classification, imbalanced classification, Outlier Detection

We propose thresholding as an approach to deal with class imbalance. We
define the concept of thresholding as a process of determining a decision
boundary in the presence of a tunable parameter. The threshold is the maximum
value of this tunable parameter where the conditions of a certain decision are
satisfied. We show that thresholding is applicable not only for linear
classifiers but also for non-linear classifiers. We show that this is the
implicit assumption for many approaches to deal with class imbalance in linear
classifiers. We then extend this paradigm beyond linear classification and show
how non-linear classification can be dealt with under this umbrella framework
of thresholding. The proposed method can be used for outlier detection in many
real-life scenarios like in manufacturing. In advanced manufacturing units,
where the manufacturing process has matured over time, the number of instances
(or parts) of the product that need to be rejected (based on a strict regime of
quality tests) becomes relatively rare and are defined as outliers. How to
detect these rare parts or outliers beforehand? How to detect combination of
conditions leading to these outliers? These are the questions motivating our
research. This paper focuses on prediction of outliers and conditions leading
to outliers using classification. We address the problem of outlier detection
using classification. The classes are good parts (those passing the quality
tests) and bad parts (those failing the quality tests and can be considered as
outliers). The rarity of outliers transforms this problem into a
class-imbalanced classification problem.
Extending Weakly-Sticky Datalog+/-: Query-Answering Tractability and Optimizations
Authors: Mostafa Milani, Leopoldo Bertossi
Date: 2016-07-10 00:00:00

Weakly-sticky (WS) Datalog+/- is an expressive member of the family of
Datalog+/- programs that is based on the syntactic notions of stickiness and
weak-acyclicity. Query answering over the WS programs has been investigated,
but there is still much work to do on the design and implementation of
practical query answering (QA) algorithms and their optimizations. Here, we
study sticky and WS programs from the point of view of the behavior of the
chase procedure, extending the stickiness property of the chase to that of
generalized stickiness of the chase (gsch-property). With this property we
specify the semantic class of GSCh programs, which includes sticky and WS
programs, and other syntactic subclasses that we identify. In particular, we
introduce joint-weakly-sticky (JWS) programs, that include WS programs. We also
propose a bottom-up QA algorithm for a range of subclasses of GSCh. The
algorithm runs in polynomial time (in data) for JWS programs. Unlike the WS
class, JWS is closed under a general magic-sets rewriting procedure for the
optimization of programs with existential rules. We apply the magic-sets
rewriting in combination with the proposed QA algorithm for the optimization of
QA over JWS programs.
Towards an "In-the-Wild" Emotion Dataset Using a Game-based Framework
Authors: Wei Li, Farnaz Abtahi, Christina Tsangouri, Zhigang Zhu
Date: 2016-07-10 00:00:00

In order to create an "in-the-wild" dataset of facial emotions with large
number of balanced samples, this paper proposes a game-based data collection
framework. The framework mainly include three components: a game engine, a game
interface, and a data collection and evaluation module. We use a deep learning
approach to build an emotion classifier as the game engine. Then a emotion web
game to allow gamers to enjoy the games, while the data collection module
obtains automatically-labelled emotion images. Using our game, we have
collected more than 15,000 images within a month of the test run and built an
emotion dataset "GaMo". To evaluate the dataset, we compared the performance of
two deep learning models trained on both GaMo and CIFE. The results of our
experiments show that because of being large and balanced, GaMo can be used to
build a more robust emotion detector than the emotion detector trained on CIFE,
which was used in the game engine to collect the face images.
Bayesian quantile additive regression trees
Authors: Bereket P. Kindo, Hao Wang, Timothy Hanson, Edsel A. Peña
Date: 2016-07-10 00:00:00
Tasks: Binary Classification, General Classification, quantile regression, regression

Ensemble of regression trees have become popular statistical tools for the
estimation of conditional mean given a set of predictors. However, quantile
regression trees and their ensembles have not yet garnered much attention
despite the increasing popularity of the linear quantile regression model. This
work proposes a Bayesian quantile additive regression trees model that shows
very good predictive performance illustrated using simulation studies and real
data applications. Further extension to tackle binary classification problems
is also considered.
Sparse additive Gaussian process with soft interactions
Authors: Garret Vo, Debdeep Pati
Date: 2016-07-09 00:00:00
Tasks: regression, Variable Selection

Additive nonparametric regression models provide an attractive tool for
variable selection in high dimensions when the relationship between the
response and predictors is complex. They offer greater flexibility compared to
parametric non-linear regression models and better interpretability and
scalability than the non-parametric regression models. However, achieving
sparsity simultaneously in the number of nonparametric components as well as in
the variables within each nonparametric component poses a stiff computational
challenge. In this article, we develop a novel Bayesian additive regression
model using a combination of hard and soft shrinkages to separately control the
number of additive components and the variables within each component. An
efficient algorithm is developed to select the importance variables and
estimate the interaction network. Excellent performance is obtained in
simulated and real data examples.
Augmenting Supervised Emotion Recognition with Rule-Based Decision Model
Authors: Amol Patwardhan, Gerald Knapp
Date: 2016-07-09 00:00:00
Tasks: Emotion Recognition

The aim of this research is development of rule based decision model for
emotion recognition. This research also proposes using the rules for augmenting
inter-corporal recognition accuracy in multimodal systems that use supervised
learning techniques. The classifiers for such learning based recognition
systems are susceptible to over fitting and only perform well on intra-corporal
data. To overcome the limitation this research proposes using rule based model
as an additional modality. The rules were developed using raw feature data from
visual channel, based on human annotator agreement and existing studies that
have attributed movement and postures to emotions. The outcome of the rule
evaluations was combined during the decision phase of emotion recognition
system. The results indicate rule based emotion recognition augment recognition
accuracy of learning based systems and also provide better recognition rate
across inter corpus emotion test data.
Multimodal Affect Recognition using Kinect
Authors: Amol Patwardhan, Gerald Knapp
Date: 2016-07-09 00:00:00
Tasks: Emotion Recognition

Affect (emotion) recognition has gained significant attention from
researchers in the past decade. Emotion-aware computer systems and devices have
many applications ranging from interactive robots, intelligent online tutor to
emotion based navigation assistant. In this research data from multiple
modalities such as face, head, hand, body and speech was utilized for affect
recognition. The research used color and depth sensing device such as Kinect
for facial feature extraction and tracking human body joints. Temporal features
across multiple frames were used for affect recognition. Event driven decision
level fusion was used to combine the results from each individual modality
using majority voting to recognize the emotions. The study also implemented
affect recognition by matching the features to the rule based emotion templates
per modality. Experiments showed that multimodal affect recognition rates using
combination of emotion templates and supervised learning were better compared
to recognition rates based on supervised learning alone. Recognition rates
obtained using temporal feature were higher compared to recognition rates
obtained using position based features only.
Hierarchical Deep Temporal Models for Group Activity Recognition
Authors: Mostafa S. Ibrahim, Srikanth Muralidharan, Zhiwei Deng, Arash Vahdat, Greg Mori
Date: 2016-07-09 00:00:00
Tasks: Activity Recognition, Group Activity Recognition

In this paper we present an approach for classifying the activity performed
by a group of people in a video sequence. This problem of group activity
recognition can be addressed by examining individual person actions and their
relations. Temporal dynamics exist both at the level of individual person
actions as well as at the level of group activity. Given a video sequence as
input, methods can be developed to capture these dynamics at both person-level
and group-level detail. We build a deep model to capture these dynamics based
on LSTM (long short-term memory) models. In order to model both person-level
and group-level dynamics, we present a 2-stage deep temporal model for the
group activity recognition problem. In our approach, one LSTM model is designed
to represent action dynamics of individual people in a video sequence and
another LSTM model is designed to aggregate person-level information for group
activity recognition. We collected a new dataset consisting of volleyball
videos labeled with individual and group activities in order to evaluate our
method. Experimental results on this new Volleyball Dataset and the standard
benchmark Collective Activity Dataset demonstrate the efficacy of the proposed
models.
Beating level-set methods for 3D seismic data interpolation: a primal-dual alternating approach
Authors: Rajiv Kumar, Oscar López, Damek Davis, Aleksandr Y. Aravkin, Felix J. Herrmann
Date: 2016-07-09 00:00:00

Acquisition cost is a crucial bottleneck for seismic workflows, and low-rank
formulations for data interpolation allow practitioners to `fill in' data
volumes from critically subsampled data acquired in the field. Tremendous size
of seismic data volumes required for seismic processing remains a major
challenge for these techniques.
  We propose a new approach to solve residual constrained formulations for
interpolation. We represent the data volume using matrix factors, and build a
block-coordinate algorithm with constrained convex subproblems that are solved
with a primal-dual splitting scheme. The new approach is competitive with state
of the art level-set algorithms that interchange the role of objectives with
constraints. We use the new algorithm to successfully interpolate a large scale
5D seismic data volume, generated from the geologically complex synthetic 3D
Compass velocity model, where 80% of the data has been removed.