pwc-archive/papers-with-abstracts huggingface dataset:
Getting More Out Of Syntax with PropS
Authors: Gabriel Stanovsky, Jessica Ficler, Ido Dagan, Yoav Goldberg
Date: 2016-03-04 00:00:00
Tasks: Open Information Extraction

Semantic NLP applications often rely on dependency trees to recognize major
elements of the proposition structure of sentences. Yet, while much semantic
structure is indeed expressed by syntax, many phenomena are not easily read out
of dependency trees, often leading to further ad-hoc heuristic post-processing
or to information loss. To directly address the needs of semantic applications,
we present PropS -- an output representation designed to explicitly and
uniformly express much of the proposition structure which is implied from
syntax, and an associated tool for extracting it from dependency trees.
Depth Superresolution using Motion Adaptive Regularization
Authors: Ulugbek S. Kamilov, Petros T. Boufounos
Date: 2016-03-04 00:00:00

Spatial resolution of depth sensors is often significantly lower compared to
that of conventional optical cameras. Recent work has explored the idea of
improving the resolution of depth using higher resolution intensity as a side
information. In this paper, we demonstrate that further incorporating temporal
information in videos can significantly improve the results. In particular, we
propose a novel approach that improves depth resolution, exploiting the
space-time redundancy in the depth and intensity using motion-adaptive low-rank
regularization. Experiments confirm that the proposed approach substantially
improves the quality of the estimated high-resolution depth. Our approach can
be a first component in systems using vision techniques that rely on high
resolution depth information.
Towards Universal Paraphrastic Sentence Embeddings
Authors: John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu
Date: 2015-11-25 00:00:00
Tasks: General Classification, Sentence, Sentence Embeddings, Sentence Similarity, Sentiment Analysis, Sentiment Classification

We consider the problem of learning general-purpose, paraphrastic sentence
embeddings based on supervision from the Paraphrase Database (Ganitkevitch et
al., 2013). We compare six compositional architectures, evaluating them on
annotated textual similarity datasets drawn both from the same distribution as
the training data and from a wide range of other domains. We find that the most
complex architectures, such as long short-term memory (LSTM) recurrent neural
networks, perform best on the in-domain data. However, in out-of-domain
scenarios, simple architectures such as word averaging vastly outperform LSTMs.
Our simplest averaging model is even competitive with systems tuned for the
particular tasks while also being extremely efficient and easy to use.
  In order to better understand how these architectures compare, we conduct
further experiments on three supervised NLP tasks: sentence similarity,
entailment, and sentiment classification. We again find that the word averaging
models perform well for sentence similarity and entailment, outperforming
LSTMs. However, on sentiment classification, we find that the LSTM performs
very strongly-even recording new state-of-the-art performance on the Stanford
Sentiment Treebank.
  We then demonstrate how to combine our pretrained sentence embeddings with
these supervised tasks, using them both as a prior and as a black box feature
extractor. This leads to performance rivaling the state of the art on the SICK
similarity and entailment tasks. We release all of our resources to the
research community with the hope that they can serve as the new baseline for
further work on universal sentence embeddings.
Adversarial Manipulation of Deep Representations
Authors: Sara Sabour, Yanshuai Cao, Fartash Faghri, David J. Fleet
Date: 2015-11-16 00:00:00

We show that the representation of an image in a deep neural network (DNN)
can be manipulated to mimic those of other natural images, with only minor,
imperceptible perturbations to the original image. Previous methods for
generating adversarial images focused on image perturbations designed to
produce erroneous class labels, while we concentrate on the internal layers of
DNN representations. In this way our new class of adversarial images differs
qualitatively from others. While the adversary is perceptually similar to one
image, its internal representation appears remarkably similar to a different
image, one from a different class, bearing little if any apparent similarity to
the input; they appear generic and consistent with the space of natural images.
This phenomenon raises questions about DNN representations, as well as the
properties of natural images themselves.
Sentiment Analysis in Scholarly Book Reviews
Authors: Hussam Hamdan, Patrice Bellot, Frederic Bechet
Date: 2016-03-04 00:00:00
Tasks: Sentiment Analysis

So far different studies have tackled the sentiment analysis in several
domains such as restaurant and movie reviews. But, this problem has not been
studied in scholarly book reviews which is different in terms of review style
and size. In this paper, we propose to combine different features in order to
be presented to a supervised classifiers which extract the opinion target
expressions and detect their polarities in scholarly book reviews. We construct
a labeled corpus for training and evaluating our methods in French book
reviews. We also evaluate them on English restaurant reviews in order to
measure their robustness across the domains and languages. The evaluation shows
that our methods are enough robust for English restaurant reviews and French
book reviews.
Coordination Event Detection and Initiator Identification in Time Series Data
Authors: Chainarong Amornbunchornvej, Ivan Brugere, Ariana Strandburg-Peshkin, Damien Farine, Margaret C. Crofoot, Tanya Y. Berger-Wolf
Date: 2016-03-04 00:00:00
Tasks: Decision Making, Event Detection, Leadership Inference, Time Series, Time Series Analysis

Behavior initiation is a form of leadership and is an important aspect of social organization that affects the processes of group formation, dynamics, and decision-making in human societies and other social animal species. In this work, we formalize the "Coordination Initiator Inference Problem" and propose a simple yet powerful framework for extracting periods of coordinated activity and determining individuals who initiated this coordination, based solely on the activity of individuals within a group during those periods. The proposed approach, given arbitrary individual time series, automatically (1) identifies times of coordinated group activity, (2) determines the identities of initiators of those activities, and (3) classifies the likely mechanism by which the group coordination occurred, all of which are novel computational tasks. We demonstrate our framework on both simulated and real-world data: trajectories tracking of animals as well as stock market data. Our method is competitive with existing global leadership inference methods but provides the first approaches for local leadership and coordination mechanism classification. Our results are consistent with ground-truthed biological data and the framework finds many known events in financial data which are not otherwise reflected in the aggregate NASDAQ index. Our method is easily generalizable to any coordinated time-series data from interacting entities.
Normalized Hierarchical SVM
Authors: Heejin Choi, Yutaka Sasaki, Nathan Srebro
Date: 2015-08-11 00:00:00
Tasks: General Classification

We present improved methods of using structured SVMs in a large-scale
hierarchical classification problem, that is when labels are leaves, or sets of
leaves, in a tree or a DAG. We examine the need to normalize both the
regularization and the margin and show how doing so significantly improves
performance, including allowing achieving state-of-the-art results where
unnormalized structured SVMs do not perform better than flat models. We also
describe a further extension of hierarchical SVMs that highlight the connection
between hierarchical SVMs and matrix factorization models.
Recurrent Neural Networks Hardware Implementation on FPGA
Authors: Andre Xian Ming Chang, Berin Martini, Eugenio Culurciello
Date: 2015-11-17 00:00:00
Tasks: CPU, Language Modeling, Language Modelling

Recurrent Neural Networks (RNNs) have the ability to retain memory and learn
data sequences. Due to the recurrent nature of RNNs, it is sometimes hard to
parallelize all its computations on conventional hardware. CPUs do not
currently offer large parallelism, while GPUs offer limited parallelism due to
sequential components of RNN models. In this paper we present a hardware
implementation of Long-Short Term Memory (LSTM) recurrent network on the
programmable logic Zynq 7020 FPGA from Xilinx. We implemented a RNN with $2$
layers and $128$ hidden units in hardware and it has been tested using a
character level language model. The implementation is more than $21\times$
faster than the ARM CPU embedded on the Zynq 7020 FPGA. This work can
potentially evolve to a RNN co-processor for future mobile devices.
Parallel Texts in the Hebrew Bible, New Methods and Visualizations
Authors: Martijn Naaijer, Dirk Roorda
Date: 2016-03-04 00:00:00

In this article we develop an algorithm to detect parallel texts in the
Masoretic Text of the Hebrew Bible. The results are presented online and
chapters in the Hebrew Bible containing parallel passages can be inspected
synoptically. Differences between parallel passages are highlighted. In a
similar way the MT of Isaiah is presented synoptically with 1QIsaa. We also
investigate how one can investigate the degree of similarity between parallel
passages with the help of a case study of 2 Kings 19-25 and its parallels in
Isaiah, Jeremiah and 2 Chronicles.
Keypoint Encoding for Improved Feature Extraction from Compressed Video at Low Bitrates
Authors: Jianshu Chao, Eckehard Steinbach
Date: 2015-06-27 00:00:00
Tasks: Image Retrieval, Keypoint Detection, Retrieval, Video Compression

In many mobile visual analysis applications, compressed video is transmitted
over a communication network and analyzed by a server. Typical processing steps
performed at the server include keypoint detection, descriptor calculation, and
feature matching. Video compression has been shown to have an adverse effect on
feature-matching performance. The negative impact of compression can be reduced
by using the keypoints extracted from the uncompressed video to calculate
descriptors from the compressed video. Based on this observation, we propose to
provide these keypoints to the server as side information and to extract only
the descriptors from the compressed video. First, we introduce four different
frame types for keypoint encoding to address different types of changes in
video content. These frame types represent a new scene, the same scene, a
slowly changing scene, or a rapidly moving scene and are determined by
comparing features between successive video frames. Then, we propose Intra,
Skip and Inter modes of encoding the keypoints for different frame types. For
example, keypoints for new scenes are encoded using the Intra mode, and
keypoints for unchanged scenes are skipped. As a result, the bitrate of the
side information related to keypoint encoding is significantly reduced.
Finally, we present pairwise matching and image retrieval experiments conducted
to evaluate the performance of the proposed approach using the Stanford mobile
augmented reality dataset and 720p format videos. The results show that the
proposed approach offers significantly improved feature matching and image
retrieval performance at a given bitrate.
A Bayesian Model of Multilingual Unsupervised Semantic Role Induction
Authors: Nikhil Garg, James Henderson
Date: 2016-03-04 00:00:00

We propose a Bayesian model of unsupervised semantic role induction in
multiple languages, and use it to explore the usefulness of parallel corpora
for this task. Our joint Bayesian model consists of individual models for each
language plus additional latent variables that capture alignments between roles
across languages. Because it is a generative Bayesian model, we can do
evaluations in a variety of scenarios just by varying the inference procedure,
without changing the model, thereby comparing the scenarios directly. We
compare using only monolingual data, using a parallel corpus, using a parallel
corpus with annotations in the other language, and using small amounts of
annotation in the target language. We find that the biggest impact of adding a
parallel corpus to training is actually the increase in mono-lingual data, with
the alignments to another language resulting in small improvements, even with
labeled data for the other language.
Clustering by Hierarchical Nearest Neighbor Descent (H-NND)
Authors: Teng Qiu, Yong-Jie Li
Date: 2015-09-09 00:00:00
Tasks: Clustering

Previously in 2014, we proposed the Nearest Descent (ND) method, capable of
generating an efficient Graph, called the in-tree (IT). Due to some beautiful
and effective features, this IT structure proves well suited for data
clustering. Although there exist some redundant edges in IT, they usually have
salient features and thus it is not hard to remove them.
  Subsequently, in order to prevent the seemingly redundant edges from
occurring, we proposed the Nearest Neighbor Descent (NND) by adding the
"Neighborhood" constraint on ND. Consequently, clusters automatically emerged,
without the additional requirement of removing the redundant edges. However,
NND proved still not perfect, since it brought in a new yet worse problem, the
"over-partitioning" problem.
  Now, in this paper, we propose a method, called the Hierarchical Nearest
Neighbor Descent (H-NND), which overcomes the over-partitioning problem of NND
via using the hierarchical strategy. Specifically, H-NND uses ND to effectively
merge the over-segmented sub-graphs or clusters that NND produces. Like ND,
H-NND also generates the IT structure, in which the redundant edges once again
appear. This seemingly comes back to the situation that ND faces. However,
compared with ND, the redundant edges in the IT structure generated by H-NND
generally become more salient, thus being much easier and more reliable to be
identified even by the simplest edge-removing method which takes the edge
length as the only measure. In other words, the IT structure constructed by
H-NND becomes more fitted for data clustering. We prove this on several
clustering datasets of varying shapes, dimensions and attributes. Besides,
compared with ND, H-NND generally takes less computation time to construct the
IT data structure for the input data.
Online Adaptive Hidden Markov Model for Multi-Tracker Fusion
Authors: Tomas Vojir, Jiri Matas, Jana Noskova
Date: 2015-04-23 00:00:00
Tasks: Object Tracking, Visual Object Tracking

In this paper, we propose a novel method for visual object tracking called
HMMTxD. The method fuses observations from complementary out-of-the box
trackers and a detector by utilizing a hidden Markov model whose latent states
correspond to a binary vector expressing the failure of individual trackers.
The Markov model is trained in an unsupervised way, relying on an online
learned detector to provide a source of tracker-independent information for a
modified Baum- Welch algorithm that updates the model w.r.t. the partially
annotated data.
  We show the effectiveness of the proposed method on combination of two and
three tracking algorithms. The performance of HMMTxD is evaluated on two
standard benchmarks (CVPR2013 and VOT) and on a rich collection of 77 publicly
available sequences. The HMMTxD outperforms the state-of-the-art, often
significantly, on all datasets in almost all criteria.
Censoring Representations with an Adversary
Authors: Harrison Edwards, Amos Storkey
Date: 2015-11-18 00:00:00
Tasks: Fairness

In practice, there are often explicit constraints on what representations or
decisions are acceptable in an application of machine learning. For example it
may be a legal requirement that a decision must not favour a particular group.
Alternatively it can be that that representation of data must not have
identifying information. We address these two related issues by learning
flexible representations that minimize the capability of an adversarial critic.
This adversary is trying to predict the relevant sensitive variable from the
representation, and so minimizing the performance of the adversary ensures
there is little or no information in the representation about the sensitive
variable. We demonstrate this adversarial approach on two problems: making
decisions free from discrimination and removing private information from
images. We formulate the adversarial model as a minimax problem, and optimize
that minimax objective using a stochastic gradient alternate min-max optimizer.
We demonstrate the ability to provide discriminant free representations for
standard test problems, and compare with previous state of the art methods for
fairness, showing statistically significant improvement across most cases. The
flexibility of this method is shown via a novel problem: removing annotations
from images, from unaligned training examples of annotated and unannotated
images, and with no a priori knowledge of the form of annotation provided to
the model.
Right Ideals of a Ring and Sublanguages of Science
Authors: Javier Arias Navarro
Date: 2016-03-03 00:00:00

Among Zellig Harris's numerous contributions to linguistics his theory of the
sublanguages of science probably ranks among the most underrated. However, not
only has this theory led to some exhaustive and meaningful applications in the
study of the grammar of immunology language and its changes over time, but it
also illustrates the nature of mathematical relations between chunks or subsets
of a grammar and the language as a whole. This becomes most clear when dealing
with the connection between metalanguage and language, as well as when
reflecting on operators.
  This paper tries to justify the claim that the sublanguages of science stand
in a particular algebraic relation to the rest of the language they are
embedded in, namely, that of right ideals in a ring.
Dynamic Memory Networks for Visual and Textual Question Answering
Authors: Caiming Xiong, Stephen Merity, Richard Socher
Date: 2016-03-04 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Neural network architectures with memory and attention mechanisms exhibit
certain reasoning capabilities required for question answering. One such
architecture, the dynamic memory network (DMN), obtained high accuracy on a
variety of language tasks. However, it was not shown whether the architecture
achieves strong results for question answering when supporting facts are not
marked during training or whether it could be applied to other modalities such
as images. Based on an analysis of the DMN, we propose several improvements to
its memory and input modules. Together with these changes we introduce a novel
input module for images in order to be able to answer visual questions. Our new
DMN+ model improves the state of the art on both the Visual Question Answering
dataset and the \babi-10k text question-answering dataset without supporting
fact supervision.
Lasso estimation for GEFCom2014 probabilistic electric load forecasting
Authors: Florian Ziel, Bidong Liu
Date: 2016-03-04 00:00:00
Tasks: Load Forecasting

We present a methodology for probabilistic load forecasting that is based on
lasso (least absolute shrinkage and selection operator) estimation. The model
considered can be regarded as a bivariate time-varying threshold
autoregressive(AR) process for the hourly electric load and temperature. The
joint modeling approach incorporates the temperature effects directly, and
reflects daily, weekly, and annual seasonal patterns and public holiday
effects. We provide two empirical studies, one based on the probabilistic load
forecasting track of the Global Energy Forecasting Competition 2014
(GEFCom2014-L), and the other based on another recent probabilistic load
forecasting competition that follows a setup similar to that of GEFCom2014-L.
In both empirical case studies, the proposed methodology outperforms two
multiple linear regression based benchmarks from among the top eight entries to
GEFCom2014-L.
A Unified View of Localized Kernel Learning
Authors: John Moeller, Sarathkrishna Swaminathan, Suresh Venkatasubramanian
Date: 2016-03-04 00:00:00

Multiple Kernel Learning, or MKL, extends (kernelized) SVM by attempting to
learn not only a classifier/regressor but also the best kernel for the training
task, usually from a combination of existing kernel functions. Most MKL methods
seek the combined kernel that performs best over every training example,
sacrificing performance in some areas to seek a global optimum. Localized
kernel learning (LKL) overcomes this limitation by allowing the training
algorithm to match a component kernel to the examples that can exploit it best.
Several approaches to the localized kernel learning problem have been explored
in the last several years. We unify many of these approaches under one simple
system and design a new algorithm with improved performance. We also develop
enhanced versions of existing algorithms, with an eye on scalability and
performance.
Gradient Descent Converges to Minimizers
Authors: Jason D. Lee, Max Simchowitz, Michael. I. Jordan, Benjamin Recht
Date: 2016-02-16 00:00:00

We show that gradient descent converges to a local minimizer, almost surely
with random initialization. This is proved by applying the Stable Manifold
Theorem from dynamical systems theory.
Learning deep representation of multityped objects and tasks
Authors: Truyen Tran, Dinh Phung, Svetha Venkatesh
Date: 2016-03-04 00:00:00
Tasks: Image Retrieval, Retrieval

We introduce a deep multitask architecture to integrate multityped
representations of multimodal objects. This multitype exposition is less
abstract than the multimodal characterization, but more machine-friendly, and
thus is more precise to model. For example, an image can be described by
multiple visual views, which can be in the forms of bag-of-words (counts) or
color/texture histograms (real-valued). At the same time, the image may have
several social tags, which are best described using a sparse binary vector. Our
deep model takes as input multiple type-specific features, narrows the
cross-modality semantic gaps, learns cross-type correlation, and produces a
high-level homogeneous representation. At the same time, the model supports
heterogeneously typed tasks. We demonstrate the capacity of the model on two
applications: social image retrieval and multiple concept prediction. The deep
architecture produces more compact representation, naturally integrates
multiviews and multimodalities, exploits better side information, and most
importantly, performs competitively against baselines.
Joint Learning Templates and Slots for Event Schema Induction
Authors: Lei Sha, Sujian Li, Baobao Chang, Zhifang Sui
Date: 2016-03-04 00:00:00
Proceeding: NAACL 2016 6
Tasks: Image Segmentation, Semantic Segmentation, Sentence

Automatic event schema induction (AESI) means to extract meta-event from raw
text, in other words, to find out what types (templates) of event may exist in
the raw text and what roles (slots) may exist in each event type. In this
paper, we propose a joint entity-driven model to learn templates and slots
simultaneously based on the constraints of templates and slots in the same
sentence. In addition, the entities' semantic information is also considered
for the inner connectivity of the entities. We borrow the normalized cut
criteria in image segmentation to divide the entities into more accurate
template clusters and slot clusters. The experiment shows that our model gains
a relatively higher result than previous work.