pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Locally Affine-Invariant Shape Matching
Authors: Smit Marvaniya, Raj Gupta, Anurag Mittal
Date: 2015-04-25 00:00:00

Matching deformable objects using their shapes is an important problem in
computer vision since shape is perhaps the most distinguishable characteristic
of an object. The problem is difficult due to many factors such as intra-class
variations, local deformations, articulations, viewpoint changes and missed and
extraneous contour portions due to errors in shape extraction. While small
local deformations has been handled in the literature by allowing some leeway
in the matching of individual contour points via methods such as Chamfer
distance and Hausdorff distance, handling more severe deformations and
articulations has been done by applying local geometric corrections such as
similarity or affine. However, determining which portions of the shape should
be used for the geometric corrections is very hard, although some methods have
been tried. In this paper, we address this problem by an efficient search for
the group of contour segments to be clustered together for a geometric
correction using Dynamic Programming by essentially searching for the
segmentations of two shapes that lead to the best matching between them. At the
same time, we allow portions of the contours to remain unmatched to handle
missing and extraneous contour portions. Experiments indicate that our method
outperforms other algorithms, especially when the shapes to be matched are more
complex.
A Prior Distribution over Directed Acyclic Graphs for Sparse Bayesian Networks
Authors: Felix L. Rios, John M. Noble, Timo J. T. Koski
Date: 2015-04-25 00:00:00

The main contribution of this article is a new prior distribution over
directed acyclic graphs, which gives larger weight to sparse graphs. This
distribution is intended for structured Bayesian networks, where the structure
is given by an ordered block model. That is, the nodes of the graph are objects
which fall into categories (or blocks); the blocks have a natural ordering. The
presence of a relationship between two objects is denoted by an arrow, from the
object of lower category to the object of higher category. The models
considered here were introduced in Kemp et al. (2004) for relational data and
extended to multivariate data in Mansinghka et al. (2006). The prior over graph
structures presented here has an explicit formula. The number of nodes in each
layer of the graph follow a Hoppe Ewens urn model.
  We consider the situation where the nodes of the graph represent random
variables, whose joint probability distribution factorises along the DAG. We
describe Monte Carlo schemes for finding the optimal aposteriori structure
given a data matrix and compare the performance with Mansinghka et al. (2006)
and also with the uniform prior.
Preferential Multi-Context Systems
Authors: Kedian Mu, Kewen Wang, Lian Wen
Date: 2015-04-25 00:00:00
Tasks: Relation

Multi-context systems (MCS) presented by Brewka and Eiter can be considered
as a promising way to interlink decentralized and heterogeneous knowledge
contexts. In this paper, we propose preferential multi-context systems (PMCS),
which provide a framework for incorporating a total preorder relation over
contexts in a multi-context system. In a given PMCS, its contexts are divided
into several parts according to the total preorder relation over them,
moreover, only information flows from a context to ones of the same part or
less preferred parts are allowed to occur. As such, the first $l$ preferred
parts of an PMCS always fully capture the information exchange between contexts
of these parts, and then compose another meaningful PMCS, termed the
$l$-section of that PMCS. We generalize the equilibrium semantics for an MCS to
the (maximal) $l_{\leq}$-equilibrium which represents belief states at least
acceptable for the $l$-section of an PMCS. We also investigate inconsistency
analysis in PMCS and related computational complexity issues.
Online Convex Optimization Using Predictions
Authors: Niangjun Chen, Anish Agarwal, Adam Wierman, Siddharth Barman, Lachlan L. H. Andrew
Date: 2015-04-25 00:00:00
Tasks: Prediction

Making use of predictions is a crucial, but under-explored, area of online
algorithms. This paper studies a class of online optimization problems where we
have external noisy predictions available. We propose a stochastic prediction
error model that generalizes prior models in the learning and stochastic
control communities, incorporates correlation among prediction errors, and
captures the fact that predictions improve as time passes. We prove that
achieving sublinear regret and constant competitive ratio for online algorithms
requires the use of an unbounded prediction window in adversarial settings, but
that under more realistic stochastic prediction error models it is possible to
use Averaging Fixed Horizon Control (AFHC) to simultaneously achieve sublinear
regret and constant competitive ratio in expectation using only a
constant-sized prediction window. Furthermore, we show that the performance of
AFHC is tightly concentrated around its mean.
Differential Recurrent Neural Networks for Action Recognition
Authors: Vivek Veeriah, Naifan Zhuang, Guo-Jun Qi
Date: 2015-04-25 00:00:00
Proceeding: ICCV 2015 12
Tasks: Action Recognition, Temporal Action Localization, Time Series, Time Series Analysis

The long short-term memory (LSTM) neural network is capable of processing
complex sequential information since it utilizes special gating schemes for
learning representations from long input sequences. It has the potential to
model any sequential time-series data, where the current hidden state has to be
considered in the context of the past hidden states. This property makes LSTM
an ideal choice to learn the complex dynamics of various actions.
Unfortunately, the conventional LSTMs do not consider the impact of
spatio-temporal dynamics corresponding to the given salient motion patterns,
when they gate the information that ought to be memorized through time. To
address this problem, we propose a differential gating scheme for the LSTM
neural network, which emphasizes on the change in information gain caused by
the salient motions between the successive frames. This change in information
gain is quantified by Derivative of States (DoS), and thus the proposed LSTM
model is termed as differential Recurrent Neural Network (dRNN). We demonstrate
the effectiveness of the proposed model by automatically recognizing actions
from the real-world 2D and 3D human action datasets. Our study is one of the
first works towards demonstrating the potential of learning complex time-series
representations via high-order derivatives of states.
Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods
Authors: Arvind Neelakantan, Ming-Wei Chang
Date: 2015-04-24 00:00:00
Proceeding: HLT 2015 5
Tasks: Knowledge Base Completion, Relation Extraction

Most of previous work in knowledge base (KB) completion has focused on the
problem of relation extraction. In this work, we focus on the task of inferring
missing entity type instances in a KB, a fundamental task for KB competition
yet receives little attention. Due to the novelty of this task, we construct a
large-scale dataset and design an automatic evaluation methodology. Our
knowledge base completion method uses information within the existing KB and
external information from Wikipedia. We show that individual methods trained
with a global objective that considers unobserved cells from both the entity
and the type side gives consistently higher quality predictions compared to
baseline methods. We also perform manual evaluation on a small subset of the
data to verify the effectiveness of our knowledge base completion methods and
the correctness of our proposed automatic evaluation method.
Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space
Authors: Arvind Neelakantan, Jeevan Shankar, Alexandre Passos, Andrew McCallum
Date: 2015-04-24 00:00:00
Proceeding: EMNLP 2014 10
Tasks: Vocal Bursts Type Prediction, Word Embeddings, Word Similarity

There is rising interest in vector-space word embeddings and their use in
NLP, especially given recent methods for their fast estimation at very large
scale. Nearly all this work, however, assumes a single vector per word type
ignoring polysemy and thus jeopardizing their usefulness for downstream tasks.
We present an extension to the Skip-gram model that efficiently learns multiple
embeddings per word type. It differs from recent related work by jointly
performing word sense discrimination and embedding learning, by
non-parametrically estimating the number of senses per word type, and by its
efficiency and scalability. We present new state-of-the-art results in the word
similarity in context task and demonstrate its scalability by training with one
machine on a corpus of nearly 1 billion tokens in less than 6 hours.
Learning Dictionaries for Named Entity Recognition using Minimal Supervision
Authors: Arvind Neelakantan, Michael Collins
Date: 2015-04-24 00:00:00
Proceeding: EACL 2014 4
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

This paper describes an approach for automatic construction of dictionaries
for Named Entity Recognition (NER) using large amounts of unlabeled data and a
few seed examples. We use Canonical Correlation Analysis (CCA) to obtain lower
dimensional embeddings (representations) for candidate phrases and classify
these phrases using a small number of labeled examples. Our method achieves
16.5% and 11.3% F-1 score improvement over co-training on disease and virus NER
respectively. We also show that by adding candidate phrase embeddings as
features in a sequence tagger gives better performance compared to using word
embeddings.
Subjectivity, Bayesianism, and Causality
Authors: Pedro A. Ortega
Date: 2014-07-15 00:00:00

Bayesian probability theory is one of the most successful frameworks to model
reasoning under uncertainty. Its defining property is the interpretation of
probabilities as degrees of belief in propositions about the state of the world
relative to an inquiring subject. This essay examines the notion of
subjectivity by drawing parallels between Lacanian theory and Bayesian
probability theory, and concludes that the latter must be enriched with causal
interventions to model agency. The central contribution of this work is an
abstract model of the subject that accommodates causal interventions in a
measure-theoretic formalisation. This formalisation is obtained through a
game-theoretic Ansatz based on modelling the inside and outside of the subject
as an extensive-form game with imperfect information between two players.
Finally, I illustrate the expressiveness of this model with an example of
causal induction.
Object Level Deep Feature Pooling for Compact Image Representation
Authors: Konda Reddy Mopuri, R. Venkatesh Babu
Date: 2015-04-24 00:00:00
Tasks: Image Retrieval, Object, Retrieval, Translation

Convolutional Neural Network (CNN) features have been successfully employed
in recent works as an image descriptor for various vision tasks. But the
inability of the deep CNN features to exhibit invariance to geometric
transformations and object compositions poses a great challenge for image
search. In this work, we demonstrate the effectiveness of the objectness prior
over the deep CNN features of image regions for obtaining an invariant image
representation. The proposed approach represents the image as a vector of
pooled CNN features describing the underlying objects. This representation
provides robustness to spatial layout of the objects in the scene and achieves
invariance to general geometric transformations, such as translation, rotation
and scaling. The proposed approach also leads to a compact representation of
the scene, making each image occupy a smaller memory footprint. Experiments
show that the proposed representation achieves state of the art retrieval
results on a set of challenging benchmark image datasets, while maintaining a
compact representation.
Semantic Motion Segmentation Using Dense CRF Formulation
Authors: N. Dinesh Reddy, Prateek Singhal, K. Madhava Krishna
Date: 2015-04-24 00:00:00
Tasks: Motion Detection, Motion Segmentation, Object, Scene Understanding

While the literature has been fairly dense in the areas of scene
understanding and semantic labeling there have been few works that make use of
motion cues to embellish semantic performance and vice versa. In this paper, we
address the problem of semantic motion segmentation, and show how semantic and
motion priors augments performance. We pro- pose an algorithm that jointly
infers the semantic class and motion labels of an object. Integrating semantic,
geometric and optical ow based constraints into a dense CRF-model we infer both
the object class as well as motion class, for each pixel. We found improvement
in performance using a fully connected CRF as compared to a standard
clique-based CRFs. For inference, we use a Mean Field approximation based
algorithm. Our method outperforms recently pro- posed motion detection
algorithms and also improves the semantic labeling compared to the
state-of-the-art Automatic Labeling Environment algorithm on the challenging
KITTI dataset especially for object classes such as pedestrians and cars that
are critical to an outdoor robotic navigation scenario.
Cultural Event Recognition with Visual ConvNets and Temporal Models
Authors: Amaia Salvador, Matthias Zeppelzauer, Daniel Manchon-Vizuete, Andrea Calafell, Xavier Giro-i-Nieto
Date: 2015-04-24 00:00:00
Tasks: Classification, General Classification

This paper presents our contribution to the ChaLearn Challenge 2015 on
Cultural Event Classification. The challenge in this task is to automatically
classify images from 50 different cultural events. Our solution is based on the
combination of visual features extracted from convolutional neural networks
with temporal information using a hierarchical classifier scheme. We extract
visual features from the last three fully connected layers of both CaffeNet
(pretrained with ImageNet) and our fine tuned version for the ChaLearn
challenge. We propose a late fusion strategy that trains a separate low-level
SVM on each of the extracted neural codes. The class predictions of the
low-level SVMs form the input to a higher level SVM, which gives the final
event scores. We achieve our best result by adding a temporal refinement step
into our classification scheme, which is applied directly to the output of each
low-level SVM. Our approach penalizes high classification scores based on
visual features when their time stamp does not match well an event-specific
temporal distribution learned from the training and validation data. Our system
achieved the second best result in the ChaLearn Challenge 2015 on Cultural
Event Classification with a mean average precision of 0.767 on the test set.
Joint calibration of Ensemble of Exemplar SVMs
Authors: Davide Modolo, Alexander Vezhnevets, Olga Russakovsky, Vittorio Ferrari
Date: 2015-03-02 00:00:00
Proceeding: CVPR 2015 6
Tasks: object-detection, Object Detection

We present a method for calibrating the Ensemble of Exemplar SVMs model.
Unlike the standard approach, which calibrates each SVM independently, our
method optimizes their joint performance as an ensemble. We formulate joint
calibration as a constrained optimization problem and devise an efficient
optimization algorithm to find its global optimum. The algorithm dynamically
discards parts of the solution space that cannot contain the optimum early on,
making the optimization computationally feasible. We experiment with EE-SVM
trained on state-of-the-art CNN descriptors. Results on the ILSVRC 2014 and
PASCAL VOC 2007 datasets show that (i) our joint calibration procedure
outperforms independent calibration on the task of classifying windows as
belonging to an object class or not; and (ii) this improved window classifier
leads to better performance on the object detection task.
Optimum Statistical Estimation with Strategic Data Sources
Authors: Yang Cai, Constantinos Daskalakis, Christos H. Papadimitriou
Date: 2014-08-11 00:00:00
Tasks: regression

We propose an optimum mechanism for providing monetary incentives to the data
sources of a statistical estimator such as linear regression, so that high
quality data is provided at low cost, in the sense that the sum of payments and
estimation error is minimized. The mechanism applies to a broad range of
estimators, including linear and polynomial regression, kernel regression, and,
under some additional assumptions, ridge regression. It also generalizes to
several objectives, including minimizing estimation error subject to budget
constraints. Besides our concrete results for regression problems, we
contribute a mechanism design framework through which to design and analyze
statistical estimators whose examples are supplied by workers with cost for
labeling said examples.
A Bayesian approach for structure learning in oscillating regulatory networks
Authors: D Trejo, AJ Millar, G Sanguinetti
Date: 2015-04-24 00:00:00
Tasks: Time Series, Time Series Analysis

Oscillations lie at the core of many biological processes, from the cell
cycle, to circadian oscillations and developmental processes. Time-keeping
mechanisms are essential to enable organisms to adapt to varying conditions in
environmental cycles, from day/night to seasonal. Transcriptional regulatory
networks are one of the mechanisms behind these biological oscillations.
However, while identifying cyclically expressed genes from time series
measurements is relatively easy, determining the structure of the interaction
network underpinning the oscillation is a far more challenging problem. Here,
we explicitly leverage the oscillatory nature of the transcriptional signals
and present a method for reconstructing network interactions tailored to this
special but important class of genetic circuits. Our method is based on
projecting the signal onto a set of oscillatory basis functions using a
Discrete Fourier Transform. We build a Bayesian Hierarchical model within a
frequency domain linear model in order to enforce sparsity and incorporate
prior knowledge about the network structure. Experiments on real and simulated
data show that the method can lead to substantial improvements over competing
approaches if the oscillatory assumption is met, and remains competitive also
in cases it is not.
Articulated motion discovery using pairs of trajectories
Authors: Luca Del Pero, Susanna Ricco, Rahul Sukthankar, Vittorio Ferrari
Date: 2014-11-28 00:00:00
Proceeding: CVPR 2015 6

We propose an unsupervised approach for discovering characteristic motion
patterns in videos of highly articulated objects performing natural, unscripted
behaviors, such as tigers in the wild. We discover consistent patterns in a
bottom-up manner by analyzing the relative displacements of large numbers of
ordered trajectory pairs through time, such that each trajectory is attached to
a different moving part on the object. The pairs of trajectories descriptor
relies entirely on motion and is more discriminative than state-of-the-art
features that employ single trajectories. Our method generates temporal video
intervals, each automatically trimmed to one instance of the discovered
behavior, and clusters them by type (e.g., running, turning head, drinking
water). We present experiments on two datasets: dogs from YouTube-Objects and a
new dataset of National Geographic tiger videos. Results confirm that our
proposed descriptor outperforms existing appearance- and trajectory-based
descriptors (e.g., HOG and DTFs) on both datasets and enables us to segment
unconstrained animal video into intervals containing single behaviors.
Controlled Query Evaluation for Datalog and OWL 2 Profile Ontologies
Authors: Bernardo Cuenca Grau, Evgeny Kharlamov, Egor V. Kostylev, Dmitriy Zheleznyakov
Date: 2015-04-24 00:00:00

We study confidentiality enforcement in ontologies under the Controlled Query
Evaluation framework, where a policy specifies the sensitive information and a
censor ensures that query answers that may compromise the policy are not
returned. We focus on censors that ensure confidentiality while maximising
information access, and consider both Datalog and the OWL 2 profiles as
ontology languages.
Local Variation as a Statistical Hypothesis Test
Authors: Michael Baltaxe, Peter Meer, Michael Lindenbaum
Date: 2015-04-24 00:00:00
Tasks: Two-sample testing

The goal of image oversegmentation is to divide an image into several pieces,
each of which should ideally be part of an object. One of the simplest and yet
most effective oversegmentation algorithms is known as local variation (LV)
(Felzenszwalb and Huttenlocher 2004). In this work, we study this algorithm and
show that algorithms similar to LV can be devised by applying different
statistical models and decisions, thus providing further theoretical
justification and a well-founded explanation for the unexpected high
performance of the LV approach. Some of these algorithms are based on
statistics of natural images and on a hypothesis testing decision; we denote
these algorithms probabilistic local variation (pLV). The best pLV algorithm,
which relies on censored estimation, presents state-of-the-art results while
keeping the same computational complexity of the LV algorithm.
Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring
Authors: Konstantinos Georgatzis, Christopher K. I. Williams
Date: 2015-04-24 00:00:00

We present a Discriminative Switching Linear Dynamical System (DSLDS) applied
to patient monitoring in Intensive Care Units (ICUs). Our approach is based on
identifying the state-of-health of a patient given their observed vital signs
using a discriminative classifier, and then inferring their underlying
physiological values conditioned on this status. The work builds on the
Factorial Switching Linear Dynamical System (FSLDS) (Quinn et al., 2009) which
has been previously used in a similar setting. The FSLDS is a generative model,
whereas the DSLDS is a discriminative model. We demonstrate on two real-world
datasets that the DSLDS is able to outperform the FSLDS in most cases of
interest, and that an $\alpha$-mixture of the two models achieves higher
performance than either of the two models separately.
Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition
Authors: Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba, Ivan Oseledets, Victor Lempitsky
Date: 2014-12-19 00:00:00
Tasks: CPU, General Classification, Tensor Decomposition

We propose a simple two-step approach for speeding up convolution layers
within large convolutional neural networks based on tensor decomposition and
discriminative fine-tuning. Given a layer, we use non-linear least squares to
compute a low-rank CP-decomposition of the 4D convolution kernel tensor into a
sum of a small number of rank-one tensors. At the second step, this
decomposition is used to replace the original convolutional layer with a
sequence of four convolutional layers with small kernels. After such
replacement, the entire network is fine-tuned on the training data using
standard backpropagation process.
  We evaluate this approach on two CNNs and show that it is competitive with
previous approaches, leading to higher obtained CPU speedups at the cost of
lower accuracy drops for the smaller of the two networks. Thus, for the
36-class character classification CNN, our approach obtains a 8.5x CPU speedup
of the whole network with only minor accuracy drop (1% from 91% to 90%). For
the standard ImageNet architecture (AlexNet), the approach speeds up the second
convolution layer by a factor of 4x at the cost of $1\%$ increase of the
overall top-5 classification error.