pwc-archive/papers-with-abstracts huggingface dataset:
Transition-Based Dependency Parsing with Stack Long Short-Term Memory
Authors: Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, Noah A. Smith
Date: 2015-05-29 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Dependency Parsing, Transition-Based Dependency Parsing

We propose a technique for learning representations of parser states in
transition-based dependency parsers. Our primary innovation is a new control
structure for sequence-to-sequence neural networks---the stack LSTM. Like the
conventional stack data structures used in transition-based parsing, elements
can be pushed to or popped from the top of the stack in constant time, but, in
addition, an LSTM maintains a continuous space embedding of the stack contents.
This lets us formulate an efficient parsing model that captures three facets of
a parser's state: (i) unbounded look-ahead into the buffer of incoming words,
(ii) the complete history of actions taken by the parser, and (iii) the
complete contents of the stack of partially built tree fragments, including
their internal structures. Standard backpropagation techniques are used for
training and yield state-of-the-art parsing performance.
Signal Recovery on Graphs: Variation Minimization
Authors: Siheng Chen, Aliaksei Sandryhaila, José M. F. Moura, Jelena Kovačević
Date: 2014-11-26 00:00:00
Tasks: Anomaly Detection, General Classification, Matrix Completion, Recommendation Systems

We consider the problem of signal recovery on graphs as graphs model data
with complex structure as signals on a graph. Graph signal recovery implies
recovery of one or multiple smooth graph signals from noisy, corrupted, or
incomplete measurements. We propose a graph signal model and formulate signal
recovery as a corresponding optimization problem. We provide a general solution
by using the alternating direction methods of multipliers. We next show how
signal inpainting, matrix completion, robust principal component analysis, and
anomaly detection all relate to graph signal recovery, and provide
corresponding specific solutions and theoretical analysis. Finally, we validate
the proposed methods on real-world recovery problems, including online blog
classification, bridge condition identification, temperature estimation,
recommender system, and expert opinion combination of online blog
classification.
Geometry of Graph Edit Distance Spaces
Authors: Brijnesh J. Jain
Date: 2015-05-29 00:00:00

In this paper we study the geometry of graph spaces endowed with a special
class of graph edit distances. The focus is on geometrical results useful for
statistical pattern recognition. The main result is the Graph Representation
Theorem. It states that a graph is a point in some geometrical space, called
orbit space. Orbit spaces are well investigated and easier to explore than the
original graph space. We derive a number of geometrical results from the orbit
space representation, translate them to the graph space, and indicate their
significance and usefulness in statistical pattern recognition.
General Deformations of Point Configurations Viewed By a Pinhole Model Camera
Authors: Yirmeyahu Kaminski, Michael Werman
Date: 2015-05-29 00:00:00

This paper is a theoretical study of the following Non-Rigid Structure from Motion problem. What can be computed from a monocular view of a parametrically deforming set of points? We treat various variations of this problem for affine and polynomial deformations with calibrated and uncalibrated cameras. We show that in general at least three images with quasi-identical two deformations are needed in order to have a finite set of solutions of the points' structure and calculate some simple examples.
Signal Recovery on Graphs: Random versus Experimentally Designed Sampling
Authors: Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovačević
Date: 2015-04-21 00:00:00

We study signal recovery on graphs based on two sampling strategies: random
sampling and experimentally designed sampling. We propose a new class of smooth
graph signals, called approximately bandlimited, which generalizes the
bandlimited class and is similar to the globally smooth class. We then propose
two recovery strategies based on random sampling and experimentally designed
sampling. The proposed recovery strategy based on experimentally designed
sampling is similar to the leverage scores used in the matrix approximation. We
show that while both strategies are unbiased estimators for the low-frequency
components, the convergence rate of experimentally designed sampling is much
faster than that of random sampling when a graph is irregular. We validate the
proposed recovery strategies on three specific graphs: a ring graph, an
Erd\H{o}s-R\'enyi graph, and a star graph. The simulation results support the
theoretical analysis.
Research on the fast Fourier transform of image based on GPU
Authors: Feifei Shen, Zhenjian Song, Congrui Wu, Jiaqi Geng, Qingyun Wang
Date: 2015-05-29 00:00:00
Tasks: CPU, GPU

Study of general purpose computation by GPU (Graphics Processing Unit) can
improve the image processing capability of micro-computer system. This paper
studies the parallelism of the different stages of decimation in time radix 2
FFT algorithm, designs the butterfly and scramble kernels and implements 2D FFT
on GPU. The experiment result demonstrates the validity and advantage over
general CPU, especially in the condition of large input size. The approach can
also be generalized to other transforms alike.
Unsupervised Feature Learning with C-SVDDNet
Authors: Dong Wang, Xiaoyang Tan
Date: 2014-12-23 00:00:00
Tasks: Image Classification, Object Recognition

In this paper, we investigate the problem of learning feature representation
from unlabeled data using a single-layer K-means network. A K-means network
maps the input data into a feature representation by finding the nearest
centroid for each input point, which has attracted researchers' great attention
recently due to its simplicity, effectiveness, and scalability. However, one
drawback of this feature mapping is that it tends to be unreliable when the
training data contains noise. To address this issue, we propose a SVDD based
feature learning algorithm that describes the density and distribution of each
cluster from K-means with an SVDD ball for more robust feature representation.
For this purpose, we present a new SVDD algorithm called C-SVDD that centers
the SVDD ball towards the mode of local density of each cluster, and we show
that the objective of C-SVDD can be solved very efficiently as a linear
programming problem. Additionally, traditional unsupervised feature learning
methods usually take an average or sum of local representations to obtain
global representation which ignore spatial relationship among them. To use
spatial information we propose a global representation with a variant of SIFT
descriptor. The architecture is also extended with multiple receptive field
scales and multiple pooling sizes. Extensive experiments on several popular
object recognition benchmarks, such as STL-10, MINST, Holiday and Copydays
shows that the proposed C-SVDDNet method yields comparable or better
performance than that of the previous state of the art methods.
Symbolic Segmentation Using Algorithm Selection
Authors: Martin Lukac, Kamila Abdiyeva, Michitaka Kameyama
Date: 2015-05-29 00:00:00
Tasks: Segmentation

In this paper we present an alternative approach to symbolic segmentation;
instead of implementing a new method we approach symbolic segmentation as an
algorithm selection problem. That is, let there be $n$ available algorithms for
symbolic segmentation, a selection mechanism forms a set of input features and
image attributes and selects on a case by case basis the best algorithm. The
selection mechanism is demonstrated from within an algorithm framework where
the selection is done in a set of various algorithm networks. Two sets of
experiments are performed and in both cases we demonstrate that the algorithm
selection allows to increase the result of the symbolic segmentation by a
considerable amount.
Supervised Fine Tuning for Word Embedding with Integrated Knowledge
Authors: Xuefeng Yang, Kezhi Mao
Date: 2015-05-29 00:00:00
Tasks: Sentence, Sentence Completion, Sentiment Analysis

Learning vector representation for words is an important research field which
may benefit many natural language processing tasks. Two limitations exist in
nearly all available models, which are the bias caused by the context
definition and the lack of knowledge utilization. They are difficult to tackle
because these algorithms are essentially unsupervised learning approaches.
Inspired by deep learning, the authors propose a supervised framework for
learning vector representation of words to provide additional supervised fine
tuning after unsupervised learning. The framework is knowledge rich approacher
and compatible with any numerical vectors word representation. The authors
perform both intrinsic evaluation like attributional and relational similarity
prediction and extrinsic evaluations like the sentence completion and sentiment
analysis. Experiments results on 6 embeddings and 4 tasks with 10 datasets show
that the proposed fine tuning framework may significantly improve the quality
of the vector representation of words.
Salient Object Detection via Augmented Hypotheses
Authors: Tam V. Nguyen, Jose Sepulveda
Date: 2015-05-29 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

In this paper, we propose using \textit{augmented hypotheses} which consider
objectness, foreground and compactness for salient object detection. Our
algorithm consists of four basic steps. First, our method generates the
objectness map via objectness hypotheses. Based on the objectness map, we
estimate the foreground margin and compute the corresponding foreground map
which prefers the foreground objects. From the objectness map and the
foreground map, the compactness map is formed to favor the compact objects. We
then derive a saliency measure that produces a pixel-accurate saliency map
which uniformly covers the objects of interest and consistently separates fore-
and background. We finally evaluate the proposed framework on two challenging
datasets, MSRA-1000 and iCoSeg. Our extensive experimental results show that
our method outperforms state-of-the-art approaches.
On the Computational Complexity of High-Dimensional Bayesian Variable Selection
Authors: Yun Yang, Martin J. Wainwright, Michael. I. Jordan
Date: 2015-05-29 00:00:00
Tasks: Variable Selection, Vocal Bursts Intensity Prediction

We study the computational complexity of Markov chain Monte Carlo (MCMC)
methods for high-dimensional Bayesian linear regression under sparsity
constraints. We first show that a Bayesian approach can achieve
variable-selection consistency under relatively mild conditions on the design
matrix. We then demonstrate that the statistical criterion of posterior
concentration need not imply the computational desideratum of rapid mixing of
the MCMC algorithm. By introducing a truncated sparsity prior for variable
selection, we provide a set of conditions that guarantee both
variable-selection consistency and rapid mixing of a particular
Metropolis-Hastings algorithm. The mixing time is linear in the number of
covariates up to a logarithmic factor. Our proof controls the spectral gap of
the Markov chain by constructing a canonical path ensemble that is inspired by
the steps taken by greedy algorithms for variable selection.
Fast Computation of PERCLOS and Saccadic Ratio
Authors: Anirban Dasgupta, Aurobinda Routray
Date: 2015-05-29 00:00:00

This thesis describes the development of fast algorithms for the computation
of PERcentage CLOSure of eyes (PERCLOS) and Saccadic Ratio (SR). PERCLOS and SR
are two ocular parameters reported to be measures of alertness levels in human
beings. PERCLOS is the percentage of time in which at least 80% of the eyelid
remains closed over the pupil. Saccades are fast and simultaneous movement of
both the eyes in the same direction. SR is the ratio of peak saccadic velocity
to the saccadic duration. This thesis addresses the issues of image based
estimation of PERCLOS and SR, prevailing in the literature such as illumination
variation, poor illumination conditions, head rotations etc. In this work,
algorithms for real-time PERCLOS computation has been developed and implemented
on an embedded platform. The platform has been used as a case study for
assessment of loss of attention in automotive drivers. The SR estimation has
been carried out offline as real-time implementation requires high frame rates
of processing which is difficult to achieve due to hardware limitations. The
accuracy in estimation of the loss of attention using PERCLOS and SR has been
validated using brain signals, which are reported to be an authentic cue for
estimating the state of alertness in human beings. The major contributions of
this thesis include database creation, design and implementation of fast
algorithms for estimating PERCLOS and SR on embedded computing platforms.
Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network
Authors: Junshi Huang, Rogerio S. Feris, Qiang Chen, Shuicheng Yan
Date: 2015-05-29 00:00:00
Proceeding: ICCV 2015 12
Tasks: Attribute, Image Retrieval, Learning-To-Rank, Retrieval, Triplet

We address the problem of cross-domain image retrieval, considering the
following practical application: given a user photo depicting a clothing image,
our goal is to retrieve the same or attribute-similar clothing items from
online shopping stores. This is a challenging problem due to the large
discrepancy between online shopping images, usually taken in ideal
lighting/pose/background conditions, and user photos captured in uncontrolled
conditions. To address this problem, we propose a Dual Attribute-aware Ranking
Network (DARN) for retrieval feature learning. More specifically, DARN consists
of two sub-networks, one for each domain, whose retrieval feature
representations are driven by semantic attribute learning. We show that this
attribute-guided learning is a key factor for retrieval accuracy improvement.
In addition, to further align with the nature of the retrieval problem, we
impose a triplet visual similarity constraint for learning to rank across the
two sub-networks. Another contribution of our work is a large-scale dataset
which makes the network learning feasible. We exploit customer review websites
to crawl a large set of online shopping images and corresponding offline user
photos with fine-grained clothing attributes, i.e., around 450,000 online
shopping images and about 90,000 exact offline counterpart images of those
online ones. All these images are collected from real-world consumer websites
reflecting the diversity of the data modality, which makes this dataset unique
and rare in the academic community. We extensively evaluate the retrieval
performance of networks in different configurations. The top-20 retrieval
accuracy is doubled when using the proposed DARN other than the current popular
solution using pre-trained CNN features only (0.570 vs. 0.268).
Towards combinatorial clustering: preliminary research survey
Authors: Mark Sh. Levin
Date: 2015-05-28 00:00:00
Tasks: Clustering, Combinatorial Optimization, Multiple-choice, Survey

The paper describes clustering problems from the combinatorial viewpoint. A
brief systemic survey is presented including the following: (i) basic
clustering problems (e.g., classification, clustering, sorting, clustering with
an order over cluster), (ii) basic approaches to assessment of objects and
object proximities (i.e., scales, comparison, aggregation issues), (iii) basic
approaches to evaluation of local quality characteristics for clusters and
total quality characteristics for clustering solutions, (iv) clustering as
multicriteria optimization problem, (v) generalized modular clustering
framework, (vi) basic clustering models/methods (e.g., hierarchical clustering,
k-means clustering, minimum spanning tree based clustering, clustering as
assignment, detection of clisue/quasi-clique based clustering, correlation
clustering, network communities based clustering), Special attention is
targeted to formulation of clustering as multicriteria optimization models.
Combinatorial optimization models are used as auxiliary problems (e.g.,
assignment, partitioning, knapsack problem, multiple choice problem,
morphological clique problem, searching for consensus/median for structures).
Numerical examples illustrate problem formulations, solving methods, and
applications. The material can be used as follows: (a) a research survey, (b) a
fundamental for designing the structure/architecture of composite modular
clustering software, (c) a bibliography reference collection, and (d) a
tutorial.
Coordinate Descent with Arbitrary Sampling II: Expected Separable Overapproximation
Authors: Zheng Qu, Peter Richtárik
Date: 2014-12-27 00:00:00

The design and complexity analysis of randomized coordinate descent methods,
and in particular of variants which update a random subset (sampling) of
coordinates in each iteration, depends on the notion of expected separable
overapproximation (ESO). This refers to an inequality involving the objective
function and the sampling, capturing in a compact way certain smoothness
properties of the function in a random subspace spanned by the sampled
coordinates. ESO inequalities were previously established for special classes
of samplings only, almost invariably for uniform samplings. In this paper we
develop a systematic technique for deriving these inequalities for a large
class of functions and for arbitrary samplings. We demonstrate that one can
recover existing ESO results using our general approach, which is based on the
study of eigenvalues associated with samplings and the data describing the
function.
Reply to Garcia et al.: Common mistakes in measuring frequency dependent word characteristics
Authors: P. S. Dodds, E. M. Clark, S. Desu, M. R. Frank, A. J. Reagan, J. R. Williams, L. Mitchell, K. D. Harris, I. M. Kloumann, J. P. Bagrow, K. Megerdoomian, M. T. McMahon, B. F. Tivnan, C. M. Danforth
Date: 2015-05-25 00:00:00
Tasks: Misconceptions

We demonstrate that the concerns expressed by Garcia et al. are misplaced,
due to (1) a misreading of our findings in [1]; (2) a widespread failure to
examine and present words in support of asserted summary quantities based on
word usage frequencies; and (3) a range of misconceptions about word usage
frequency, word rank, and expert-constructed word lists. In particular, we show
that the English component of our study compares well statistically with two
related surveys, that no survey design influence is apparent, and that
estimates of measurement error do not explain the positivity biases reported in
our work and that of others. We further demonstrate that for the frequency
dependence of positivity---of which we explored the nuances in great detail in
[1]---Garcia et al. did not perform a reanalysis of our data---they instead
carried out an analysis of a different, statistically improper data set and
introduced a nonlinearity before performing linear regression.
Query by String word spotting based on character bi-gram indexing
Authors: Suman K. Ghosh, Ernest Valveny
Date: 2015-05-28 00:00:00
Tasks: Attribute, Re-Ranking, Retrieval, Segmentation

In this paper we propose a segmentation-free query by string word spotting
method. Both the documents and query strings are encoded using a recently
proposed word representa- tion that projects images and strings into a common
atribute space based on a pyramidal histogram of characters(PHOC). These
attribute models are learned using linear SVMs over the Fisher Vector
representation of the images along with the PHOC labels of the corresponding
strings. In order to search through the whole page, document regions are
indexed per character bi- gram using a similar attribute representation. On top
of that, we propose an integral image representation of the document using a
simplified version of the attribute model for efficient computation. Finally we
introduce a re-ranking step in order to boost retrieval performance. We show
state-of-the-art results for segmentation-free query by string word spotting in
single-writer and multi-writer standard datasets
A Data-Driven Approach for Tag Refinement and Localization in Web Videos
Authors: Lamberto Ballan, Marco Bertini, Giuseppe Serra, Alberto del Bimbo
Date: 2014-07-02 00:00:00
Tasks: TAG

Tagging of visual content is becoming more and more widespread as web-based
services and social networks have popularized tagging functionalities among
their users. These user-generated tags are used to ease browsing and
exploration of media collections, e.g. using tag clouds, or to retrieve
multimedia content. However, not all media are equally tagged by users. Using
the current systems is easy to tag a single photo, and even tagging a part of a
photo, like a face, has become common in sites like Flickr and Facebook. On the
other hand, tagging a video sequence is more complicated and time consuming, so
that users just tag the overall content of a video. In this paper we present a
method for automatic video annotation that increases the number of tags
originally provided by users, and localizes them temporally, associating tags
to keyframes. Our approach exploits collective knowledge embedded in
user-generated tags and web sources, and visual similarity of keyframes and
images uploaded to social sites like YouTube and Flickr, as well as web sources
like Google and Bing. Given a keyframe, our method is able to select on the fly
from these visual sources the training exemplars that should be the most
relevant for this test sample, and proceeds to transfer labels across similar
images. Compared to existing video tagging approaches that require training
classifiers for each tag, our system has few parameters, is easy to implement
and can deal with an open vocabulary scenario. We demonstrate the approach on
tag refinement and localization on DUT-WEBV, a large dataset of web videos, and
show state-of-the-art results.
Human Social Interaction Modeling Using Temporal Deep Networks
Authors: Mohamed R. Amer, Behjat Siddiquie, Amir Tamrakar, David A. Salter, Brian Lande, Darius Mehri, Ajay Divakaran
Date: 2015-05-06 00:00:00

We present a novel approach to computational modeling of social interactions
based on modeling of essential social interaction predicates (ESIPs) such as
joint attention and entrainment. Based on sound social psychological theory and
methodology, we collect a new "Tower Game" dataset consisting of audio-visual
capture of dyadic interactions labeled with the ESIPs. We expect this dataset
to provide a new avenue for research in computational social interaction
modeling. We propose a novel joint Discriminative Conditional Restricted
Boltzmann Machine (DCRBM) model that combines a discriminative component with
the generative power of CRBMs. Such a combination enables us to uncover
actionable constituents of the ESIPs in two steps. First, we train the DCRBM
model on the labeled data and get accurate (76\%-49\% across various ESIPs)
detection of the predicates. Second, we exploit the generative capability of
DCRBMs to activate the trained model so as to generate the lower-level data
corresponding to the specific ESIP that closely matches the actual training
data (with mean square error 0.01-0.1 for generating 100 frames). We are thus
able to decompose the ESIPs into their constituent actionable behaviors. Such a
purely computational determination of how to establish an ESIP such as
engagement is unprecedented.