pwc-archive/papers-with-abstracts huggingface dataset:
Jointly Learning Multiple Measures of Similarities from Triplet Comparisons
Authors: Liwen Zhang, Subhransu Maji, Ryota Tomioka
Date: 2015-03-05 00:00:00
Tasks: Metric Learning, Triplet

Similarity between objects is multi-faceted and it can be easier for human
annotators to measure it when the focus is on a specific aspect. We consider
the problem of mapping objects into view-specific embeddings where the distance
between them is consistent with the similarity comparisons of the form "from
the t-th view, object A is more similar to B than to C". Our framework jointly
learns view-specific embeddings exploiting correlations between views.
Experiments on a number of datasets, including one of multi-view crowdsourced
comparison on bird images, show the proposed method achieves lower triplet
generalization error when compared to both learning embeddings independently
for each view and all views pooled into one view. Our method can also be used
to learn multiple measures of similarity over input features taking class
labels into account and compares favorably to existing approaches for
multi-task metric learning on the ISOLET dataset.
A Quorum Sensing Inspired Algorithm for Dynamic Clustering
Authors: Feng Tan, Jean-Jacques Slotine
Date: 2013-03-16 00:00:00
Tasks: Clustering, Community Detection, Image Segmentation, Semantic Segmentation

Quorum sensing is a decentralized biological process, through which a
community of cells with no global awareness coordinate their functional
behaviors based solely on cell-medium interactions and local decisions. This
paper draws inspirations from quorum sensing and colony competition to derive a
new algorithm for data clustering. The algorithm treats each data as a single
cell, and uses knowledge of local connectivity to cluster cells into multiple
colonies simultaneously. It simulates auto-inducers secretion in quorum sensing
to tune the influence radius for each cell. At the same time, sparsely
distributed core cells spread their influences to form colonies, and
interactions between colonies eventually determine each cell's identity. The
algorithm has the flexibility to analyze not only static but also time-varying
data, which surpasses the capacity of many existing algorithms. Its stability
and convergence properties are established. The algorithm is tested on several
applications, including both synthetic and real benchmarks data sets, alleles
clustering, community detection, image segmentation. In particular, the
algorithm's distinctive capability to deal with time-varying data allows us to
experiment it on novel applications such as robotic swarms grouping and
switching model identification. We believe that the algorithm's promising
performance would stimulate many more exciting applications.
Isometric sketching of any set via the Restricted Isometry Property
Authors: Samet Oymak, Benjamin Recht, Mahdi Soltanolkotabi
Date: 2015-06-11 00:00:00
Tasks: Dimensionality Reduction

In this paper we show that for the purposes of dimensionality reduction
certain class of structured random matrices behave similarly to random Gaussian
matrices. This class includes several matrices for which matrix-vector multiply
can be computed in log-linear time, providing efficient dimensionality
reduction of general sets. In particular, we show that using such matrices any
set from high dimensions can be embedded into lower dimensions with near
optimal distortion. We obtain our results by connecting dimensionality
reduction of any set to dimensionality reduction of sparse vectors via a
chaining argument.
Structured Transforms for Small-Footprint Deep Learning
Authors: Vikas Sindhwani, Tara N. Sainath, Sanjiv Kumar
Date: 2015-10-06 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Deep Learning, Keyword Spotting, speech-recognition, Speech Recognition

We consider the task of building compact deep learning pipelines suitable for
deployment on storage and power constrained mobile devices. We propose a
unified framework to learn a broad family of structured parameter matrices that
are characterized by the notion of low displacement rank. Our structured
transforms admit fast function and gradient evaluation, and span a rich range
of parameter sharing configurations whose statistical modeling capacity can be
explicitly tuned along a continuum from structured to unstructured.
Experimental results show that these transforms can significantly accelerate
inference and forward/backward passes during training, and offer superior
accuracy-compactness-speed tradeoffs in comparison to a number of existing
techniques. In keyword spotting applications in mobile speech recognition, our
methods are much more effective than standard linear low-rank bottleneck layers
and nearly retain the performance of state of the art models, while providing
more than 3.5-fold compression.
Language Segmentation
Authors: David Alfter
Date: 2015-10-06 00:00:00
Tasks: Clustering, Language Modeling, Language Modelling, Segmentation, Sentence

Language segmentation consists in finding the boundaries where one language
ends and another language begins in a text written in more than one language.
This is important for all natural language processing tasks. The problem can be
solved by training language models on language data. However, in the case of
low- or no-resource languages, this is problematic. I therefore investigate
whether unsupervised methods perform better than supervised methods when it is
difficult or impossible to train supervised approaches. A special focus is
given to difficult texts, i.e. texts that are rather short (one sentence),
containing abbreviations, low-resource languages and non-standard language. I
compare three approaches: supervised n-gram language models, unsupervised
clustering and weakly supervised n-gram language model induction. I devised the
weakly supervised approach in order to deal with difficult text specifically.
In order to test the approach, I compiled a small corpus of different text
types, ranging from one-sentence texts to texts of about 300 words. The weakly
supervised language model induction approach works well on short and difficult
texts, outperforming the clustering algorithm and reaching scores in the
vicinity of the supervised approach. The results look promising, but there is
room for improvement and a more thorough investigation should be undertaken.
Unsupervised Learning of Visual Representations using Videos
Authors: Xiaolong Wang, Abhinav Gupta
Date: 2015-05-04 00:00:00
Proceeding: ICCV 2015 12
Tasks: Surface Normal Estimation, Triplet, Visual Tracking

Is strong supervision necessary for learning a good visual representation? Do
we really need millions of semantically-labeled images to train a Convolutional
Neural Network (CNN)? In this paper, we present a simple yet surprisingly
powerful approach for unsupervised learning of CNN. Specifically, we use
hundreds of thousands of unlabeled videos from the web to learn visual
representations. Our key idea is that visual tracking provides the supervision.
That is, two patches connected by a track should have similar visual
representation in deep feature space since they probably belong to the same
object or object part. We design a Siamese-triplet network with a ranking loss
function to train this CNN representation. Without using a single image from
ImageNet, just using 100K unlabeled videos and the VOC 2012 dataset, we train
an ensemble of unsupervised networks that achieves 52% mAP (no bounding box
regression). This performance comes tantalizingly close to its
ImageNet-supervised counterpart, an ensemble which achieves a mAP of 54.4%. We
also show that our unsupervised network can perform competitively in other
tasks such as surface-normal estimation.
Euclidean Auto Calibration of Camera Networks: Baseline Constraint Removes Scale Ambiguity
Authors: Kiran Kumar Vupparaboina, Kamala Raghavan, Soumya Jana
Date: 2015-10-06 00:00:00
Tasks: 3D Reconstruction

Metric auto calibration of a camera network from multiple views has been
reported by several authors. Resulting 3D reconstruction recovers shape
faithfully, but not scale. However, preservation of scale becomes critical in
applications, such as multi-party telepresence, where multiple 3D scenes need
to be fused into a single coordinate system. In this context, we propose a
camera network configuration that includes a stereo pair with known baseline
separation, and analytically demonstrate Euclidean auto calibration of such
network under mild conditions. Further, we experimentally validate our theory
using a four-camera network. Importantly, our method not only recovers scale,
but also compares favorably with the well known Zhang and Pollefeys methods in
terms of shape recovery.
Improved Spectral Clustering via Embedded Label Propagation
Authors: Xiaojun Chang, Feiping Nie, Yi Yang, Heng Huang
Date: 2014-11-23 00:00:00
Tasks: Clustering

Spectral clustering is a key research topic in the field of machine learning
and data mining. Most of the existing spectral clustering algorithms are built
upon Gaussian Laplacian matrices, which are sensitive to parameters. We propose
a novel parameter free, distance consistent Locally Linear Embedding. The
proposed distance consistent LLE promises that edges between closer data points
have greater weight.Furthermore, we propose a novel improved spectral
clustering via embedded label propagation. Our algorithm is built upon two
advancements of the state of the art:1) label propagation,which propagates a
node\'s labels to neighboring nodes according to their proximity; and 2)
manifold learning, which has been widely used in its capacity to leverage the
manifold structure of data points. First we perform standard spectral
clustering on original data and assign each cluster to k nearest data points.
Next, we propagate labels through dense, unlabeled data regions. Extensive
experiments with various datasets validate the superiority of the proposed
algorithm compared to current state of the art spectral algorithms.
DKP-AOM: results for OAEI 2015
Authors: Muhammad Fahad
Date: 2015-10-06 00:00:00
Tasks: Anatomy

In this paper, we present the results obtained by our DKP-AOM system within
the OAEI 2015 campaign. DKP-AOM is an ontology merging tool designed to merge
heterogeneous ontologies. In OAEI, we have participated with its ontology
mapping component which serves as a basic module capable of matching large
scale ontologies before their merging. This is our first successful
participation in the Conference, OA4QA and Anatomy track of OAEI. DKP-AOM is
participating with two versions (DKP-AOM and DKP-AOM_lite), DKP-AOM performs
coherence analysis. In OA4QA track, DKPAOM out-performed in the evaluation and
generated accurate alignments allowed to answer all the queries of the
evaluation. We can also see its competitive results for the conference track in
the evaluation initiative among other reputed systems. In the anatomy track, it
has produced alignments within an allocated time and appeared in the list of
systems which produce coherent results. Finally, we discuss some future work
towards the development of DKP-AOM.
A Latent Source Model for Patch-Based Image Segmentation
Authors: George Chen, Devavrat Shah, Polina Golland
Date: 2015-10-06 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Despite the popularity and empirical success of patch-based nearest-neighbor
and weighted majority voting approaches to medical image segmentation, there
has been no theoretical development on when, why, and how well these
nonparametric methods work. We bridge this gap by providing a theoretical
performance guarantee for nearest-neighbor and weighted majority voting
segmentation under a new probabilistic model for patch-based image
segmentation. Our analysis relies on a new local property for how similar
nearby patches are, and fuses existing lines of work on modeling natural
imagery patches and theory for nonparametric classification. We use the model
to derive a new patch-based segmentation algorithm that iterates between
inferring local label patches and merging these local segmentations to produce
a globally consistent image segmentation. Many existing patch-based algorithms
arise as special cases of the new algorithm.
Large-scale subspace clustering using sketching and validation
Authors: Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis
Date: 2015-10-06 00:00:00
Tasks: Clustering

The nowadays massive amounts of generated and communicated data present major
challenges in their processing. While capable of successfully classifying
nonlinearly separable objects in various settings, subspace clustering (SC)
methods incur prohibitively high computational complexity when processing
large-scale data. Inspired by the random sampling and consensus (RANSAC)
approach to robust regression, the present paper introduces a randomized scheme
for SC, termed sketching and validation (SkeVa-)SC, tailored for large-scale
data. At the heart of SkeVa-SC lies a randomized scheme for approximating the
underlying probability density function of the observed data by kernel
smoothing arguments. Sparsity in data representations is also exploited to
reduce the computational burden of SC, while achieving high clustering
accuracy. Performance analysis as well as extensive numerical tests on
synthetic and real data corroborate the potential of SkeVa-SC and its
competitive performance relative to state-of-the-art scalable SC approaches.
Keywords: Subspace clustering, big data, kernel smoothing, randomization,
sketching, validation, sparsity.
Disjunctive Answer Set Solvers via Templates
Authors: Remi Brochenin, Yuliya Lierler, Marco Maratea
Date: 2015-10-06 00:00:00

Answer set programming is a declarative programming paradigm oriented towards
difficult combinatorial search problems. A fundamental task in answer set
programming is to compute stable models, i.e., solutions of logic programs.
Answer set solvers are the programs that perform this task. The problem of
deciding whether a disjunctive program has a stable model is
$\Sigma^P_2$-complete. The high complexity of reasoning within disjunctive
logic programming is responsible for few solvers capable of dealing with such
programs, namely DLV, GnT, Cmodels, CLASP and WASP. In this paper we show that
transition systems introduced by Nieuwenhuis, Oliveras, and Tinelli to model
and analyze satisfiability solvers can be adapted for disjunctive answer set
solvers. Transition systems give a unifying perspective and bring clarity in
the description and comparison of solvers. They can be effectively used for
analyzing, comparing and proving correctness of search algorithms as well as
inspiring new ideas in the design of disjunctive answer set solvers. In this
light, we introduce a general template, which accounts for major techniques
implemented in disjunctive solvers. We then illustrate how this general
template captures solvers DLV, GnT and Cmodels. We also show how this framework
provides a convenient tool for designing new solving algorithms by means of
combinations of techniques employed in different solvers.
Predicting Daily Activities From Egocentric Images Using Deep Learning
Authors: Daniel Castro, Steven Hickson, Vinay Bettadapura, Edison Thomaz, Gregory Abowd, Henrik Christensen, Irfan Essa
Date: 2015-10-06 00:00:00
Tasks: Classification, Deep Learning, General Classification

We present a method to analyze images taken from a passive egocentric
wearable camera along with the contextual information, such as time and day of
week, to learn and predict everyday activities of an individual. We collected a
dataset of 40,103 egocentric images over a 6 month period with 19 activity
classes and demonstrate the benefit of state-of-the-art deep learning
techniques for learning and predicting daily activities. Classification is
conducted using a Convolutional Neural Network (CNN) with a classification
method we introduce called a late fusion ensemble. This late fusion ensemble
incorporates relevant contextual information and increases our classification
accuracy. Our technique achieves an overall accuracy of 83.07% in predicting a
person's activity across the 19 activity classes. We also demonstrate some
promising results from two additional users by fine-tuning the classifier with
one day of training data.
Analyzer and generator for Pali
Authors: David Alfter
Date: 2015-10-06 00:00:00
Tasks: Morphological Analysis

This work describes a system that performs morphological analysis and
generation of Pali words. The system works with regular inflectional paradigms
and a lexical database. The generator is used to build a collection of
inflected and derived words, which in turn is used by the analyzer. Generating
and storing morphological forms along with the corresponding morphological
information allows for efficient and simple look up by the analyzer. Indeed, by
looking up a word and extracting the attached morphological information, the
analyzer does not have to compute this information. As we must, however, assume
the lexical database to be incomplete, the system can also work without the
dictionary component, using a rule-based approach.
Parameterized Neural Network Language Models for Information Retrieval
Authors: Benjamin Piwowarski, Sylvain Lamprier, Nicolas Despres
Date: 2015-10-06 00:00:00
Tasks: Information Retrieval, Language Modeling, Language Modelling, Retrieval

Information Retrieval (IR) models need to deal with two difficult issues,
vocabulary mismatch and term dependencies. Vocabulary mismatch corresponds to
the difficulty of retrieving relevant documents that do not contain exact query
terms but semantically related terms. Term dependencies refers to the need of
considering the relationship between the words of the query when estimating the
relevance of a document. A multitude of solutions has been proposed to solve
each of these two problems, but no principled model solve both. In parallel, in
the last few years, language models based on neural networks have been used to
cope with complex natural language processing tasks like emotion and paraphrase
detection. Although they present good abilities to cope with both term
dependencies and vocabulary mismatch problems, thanks to the distributed
representation of words they are based upon, such models could not be used
readily in IR, where the estimation of one language model per document (or
query) is required. This is both computationally unfeasible and prone to
over-fitting. Based on a recent work that proposed to learn a generic language
model that can be modified through a set of document-specific parameters, we
explore use of new neural network models that are adapted to ad-hoc IR tasks.
Within the language model IR framework, we propose and study the use of a
generic language model as well as a document-specific language model. Both can
be used as a smoothing component, but the latter is more adapted to the
document at hand and has the potential of being used as a full document
language model. We experiment with such models and analyze their results on
TREC-1 to 8 datasets.
RAID: A Relation-Augmented Image Descriptor
Authors: Paul Guerrero, Niloy J. Mitra, Peter Wonka
Date: 2015-10-05 00:00:00
Tasks: Relation

As humans, we regularly interpret images based on the relations between image
regions. For example, a person riding object X, or a plank bridging two
objects. Current methods provide limited support to search for images based on
such relations. We present RAID, a relation-augmented image descriptor that
supports queries based on inter-region relations. The key idea of our
descriptor is to capture the spatial distribution of simple point-to-region
relationships to describe more complex relationships between two image regions.
We evaluate the proposed descriptor by querying into a large subset of the
Microsoft COCO database and successfully extract nontrivial images
demonstrating complex inter-region relations, which are easily missed or
erroneously classified by existing methods.
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
Authors: Dan Xu, Elisa Ricci, Yan Yan, Jingkuan Song, Nicu Sebe
Date: 2015-10-06 00:00:00
Tasks: Anomaly Detection, Denoising, Event Detection

We present a novel unsupervised deep learning framework for anomalous event
detection in complex video scenes. While most existing works merely use
hand-crafted appearance and motion features, we propose Appearance and Motion
DeepNet (AMDN) which utilizes deep neural networks to automatically learn
feature representations. To exploit the complementary information of both
appearance and motion patterns, we introduce a novel double fusion framework,
combining both the benefits of traditional early fusion and late fusion
strategies. Specifically, stacked denoising autoencoders are proposed to
separately learn both appearance and motion features as well as a joint
representation (early fusion). Based on the learned representations, multiple
one-class SVM models are used to predict the anomaly scores of each input,
which are then integrated with a late fusion strategy for final anomaly
detection. We evaluate the proposed method on two publicly available video
surveillance datasets, showing competitive performance with respect to state of
the art approaches.
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks
Authors: Efstratios Gavves, Thomas Mensink, Tatiana Tommasi, Cees G. M. Snoek, Tinne Tuytelaars
Date: 2015-10-06 00:00:00
Proceeding: ICCV 2015 12
Tasks: Active Learning, General Classification, image-classification, Image Classification, Transfer Learning

How can we reuse existing knowledge, in the form of available datasets, when
solving a new and apparently unrelated target task from a set of unlabeled
data? In this work we make a first contribution to answer this question in the
context of image classification. We frame this quest as an active learning
problem and use zero-shot classifiers to guide the learning process by linking
the new task to the existing classifiers. By revisiting the dual formulation of
adaptive SVM, we reveal two basic conditions to choose greedily only the most
relevant samples to be annotated. On this basis we propose an effective active
learning algorithm which learns the best possible target classification model
with minimum human labeling effort. Extensive experiments on two challenging
datasets show the value of our approach compared to the state-of-the-art active
learning methodologies, as well as its potential to reuse past datasets with
minimal effort for future tasks.
DC Decomposition of Nonconvex Polynomials with Algebraic Techniques
Authors: Amir Ali Ahmadi, Georgina Hall
Date: 2015-10-06 00:00:00
Tasks: valid

We consider the problem of decomposing a multivariate polynomial as the
difference of two convex polynomials. We introduce algebraic techniques which
reduce this task to linear, second order cone, and semidefinite programming.
This allows us to optimize over subsets of valid difference of convex
decompositions (dcds) and find ones that speed up the convex-concave procedure
(CCP). We prove, however, that optimizing over the entire set of dcds is
NP-hard.