pwc-archive/papers-with-abstracts huggingface dataset:
RankMerging: A supervised learning-to-rank framework to predict links in large social network
Authors: Lionel Tabourier, Daniel Faria Bernardes, Anne-Sophie Libert, Renaud Lambiotte
Date: 2014-07-09 00:00:00
Tasks: feature selection, Learning-To-Rank, parameter estimation

Uncovering unknown or missing links in social networks is a difficult task because of their sparsity and because links may represent different types of relationships, characterized by different structural patterns. In this paper, we define a simple yet efficient supervised learning-to-rank framework, called RankMerging, which aims at combining information provided by various unsupervised rankings. We illustrate our method on three different kinds of social networks and show that it substantially improves the performances of unsupervised metrics of ranking. We also compare it to other combination strategies based on standard methods. Finally, we explore various aspects of RankMerging, such as feature selection and parameter estimation and discuss its area of relevance: the prediction of an adjustable number of links on large networks.
Surrogate Losses in Passive and Active Learning
Authors: Steve Hanneke, Liu Yang
Date: 2012-07-16 00:00:00
Tasks: Active Learning

Active learning is a type of sequential design for supervised machine learning, in which the learning algorithm sequentially requests the labels of selected instances from a large pool of unlabeled data points. The objective is to produce a classifier of relatively low risk, as measured under the 0-1 loss, ideally using fewer label requests than the number of random labeled data points sufficient to achieve the same. This work investigates the potential uses of surrogate loss functions in the context of active learning. Specifically, it presents an active learning algorithm based on an arbitrary classification-calibrated surrogate loss function, along with an analysis of the number of label requests sufficient for the classifier returned by the algorithm to achieve a given risk under the 0-1 loss. Interestingly, these results cannot be obtained by simply optimizing the surrogate risk via active learning to an extent sufficient to provide a guarantee on the 0-1 loss, as is common practice in the analysis of surrogate losses for passive learning. Some of the results have additional implications for the use of surrogate losses in passive learning.
Content-Based Bird Retrieval using Shape context, Color moments and Bag of Features
Authors: Bahri Abdelkhalak, Hamid Zouaki
Date: 2015-03-14 00:00:00
Tasks: Retrieval

In this paper we propose a new descriptor for birds search. First, our work
was carried on the choice of a descriptor. This choice is usually driven by the
application requirements such as robustness to noise, stability with respect to
bias, the invariance to geometrical transformations or tolerance to occlusions.
In this context, we introduce a descriptor which combines the shape and color
descriptors to have an effectiveness description of birds. The proposed
descriptor is an adaptation of a descriptor based on the contours defined in
article Belongie et al. [5] combined with color moments [19]. Specifically,
points of interest are extracted from each image and information's in the
region in the vicinity of these points are represented by descriptors of shape
context concatenated with color moments. Thus, the approach bag of visual words
is applied to the latter. The experimental results show the effectiveness of
our descriptor for the bird search by content.
LiSens --- A Scalable Architecture for Video Compressive Sensing
Authors: Jian Wang, Mohit Gupta, Aswin C. Sankaranarayanan
Date: 2015-03-14 00:00:00
Tasks: Compressive Sensing, Video Compressive Sensing

The measurement rate of cameras that take spatially multiplexed measurements
by using spatial light modulators (SLM) is often limited by the switching speed
of the SLMs. This is especially true for single-pixel cameras where the
photodetector operates at a rate that is many orders-of-magnitude greater than
the SLM. We study the factors that determine the measurement rate for such
spatial multiplexing cameras (SMC) and show that increasing the number of
pixels in the device improves the measurement rate, but there is an optimum
number of pixels (typically, few thousands) beyond which the measurement rate
does not increase. This motivates the design of LiSens, a novel imaging
architecture, that replaces the photodetector in the single-pixel camera with a
1D linear array or a line-sensor. We illustrate the optical architecture
underlying LiSens, build a prototype, and demonstrate results of a range of
indoor and outdoor scenes. LiSens delivers on the promise of SMCs: imaging at a
megapixel resolution, at video rate, using an inexpensive low-resolution
sensor.
A Dictionary-based Approach for Estimating Shape and Spatially-Varying Reflectance
Authors: Zhuo Hui, Aswin C. Sankaranarayanan
Date: 2015-03-14 00:00:00
Tasks: BRDF estimation, Object

We present a technique for estimating the shape and reflectance of an object
in terms of its surface normals and spatially-varying BRDF. We assume that
multiple images of the object are obtained under fixed view-point and varying
illumination, i.e, the setting of photometric stereo. Assuming that the BRDF at
each pixel lies in the non-negative span of a known BRDF dictionary, we derive
a per-pixel surface normal and BRDF estimation framework that requires neither
iterative optimization techniques nor careful initialization, both of which are
endemic to most state-of-the-art techniques. We showcase the performance of our
technique on a wide range of simulated and real scenes where we outperform
competing methods.
Quantum Structure of Negation and Conjunction in Human Thought
Authors: Diederik Aerts, Sandro Sozzo, Tomas Veloz
Date: 2015-03-14 00:00:00
Tasks: Logical Reasoning, Negation

We analyse in this paper the data collected in a set of experiments performed
on human subjects on the combination of natural concepts. We investigate the
mutual influence of conceptual conjunction and negation by measuring the
membership weights of a list of exemplars with respect to two concepts, e.g.,
'Fruits' and 'Vegetables', and their conjunction 'Fruits And Vegetables', but
also their conjunction when one or both concepts are negated, namely, 'Fruits
And Not Vegetables', 'Not Fruits And Vegetables' and 'Not Fruits And Not
Vegetables'. Our findings sharpen existing analysis on conceptual combinations,
revealing systematic and remarkable deviations from classical (fuzzy set) logic
and probability theory. And, more important, our results give further
considerable evidence to the validity of our quantum-theoretic framework for
the combination of two concepts. Indeed, the representation of conceptual
negation naturally arises from the general assumptions of our two-sector Fock
space model, and this representation faithfully agrees with the collected data.
In addition, we find a further significant deviation and a priori unexpected
from classicality, which can exactly be explained by assuming that human
reasoning is the superposition of an 'emergent reasoning' and a 'logical
reasoning', and that these two processes can be successfully represented in a
Fock space algebraic structure.
Statistical Limits of Convex Relaxations
Authors: Zhaoran Wang, Quanquan Gu, Han Liu
Date: 2015-03-04 00:00:00
Tasks: Sparse Learning, Stochastic Block Model

Many high dimensional sparse learning problems are formulated as nonconvex
optimization. A popular approach to solve these nonconvex optimization problems
is through convex relaxations such as linear and semidefinite programming. In
this paper, we study the statistical limits of convex relaxations.
Particularly, we consider two problems: Mean estimation for sparse principal
submatrix and edge probability estimation for stochastic block model. We
exploit the sum-of-squares relaxation hierarchy to sharply characterize the
limits of a broad class of convex relaxations. Our result shows statistical
optimality needs to be compromised for achieving computational tractability
using convex relaxations. Compared with existing results on computational lower
bounds for statistical problems, which consider general polynomial-time
algorithms and rely on computational hardness hypotheses on problems like
planted clique detection, our theory focuses on a broad class of convex
relaxations and does not rely on unproven hypotheses.
The YLI-MED Corpus: Characteristics, Procedures, and Plans
Authors: Julia Bernd, Damian Borth, Benjamin Elizalde, Gerald Friedland, Heather Gallagher, Luke Gottlieb, Adam Janin, Sara Karabashlieva, Jocelyn Takahashi, Jennifer Won
Date: 2015-03-13 00:00:00
Tasks: Descriptive, Event Detection

The YLI Multimedia Event Detection corpus is a public-domain index of videos
with annotations and computed features, specialized for research in multimedia
event detection (MED), i.e., automatically identifying what's happening in a
video by analyzing the audio and visual content. The videos indexed in the
YLI-MED corpus are a subset of the larger YLI feature corpus, which is being
developed by the International Computer Science Institute and Lawrence
Livermore National Laboratory based on the Yahoo Flickr Creative Commons 100
Million (YFCC100M) dataset. The videos in YLI-MED are categorized as depicting
one of ten target events, or no target event, and are annotated for additional
attributes like language spoken and whether the video has a musical score. The
annotations also include degree of annotator agreement and average annotator
confidence scores for the event categorization of each video. Version 1.0 of
YLI-MED includes 1823 "positive" videos that depict the target events and
48,138 "negative" videos, as well as 177 supplementary videos that are similar
to event videos but are not positive examples. Our goal in producing YLI-MED is
to be as open about our data and procedures as possible. This report describes
the procedures used to collect the corpus; gives detailed descriptive
statistics about the corpus makeup (and how video attributes affected
annotators' judgments); discusses possible biases in the corpus introduced by
our procedural choices and compares it with the most similar existing dataset,
TRECVID MED's HAVIC corpus; and gives an overview of our future plans for
expanding the annotation effort.
Fuzzy Mixed Integer Linear Programming for Air Vehicles Operations Optimization
Authors: Arindam Chaudhuri, Dipak Chatterjee, Ritesh Rajput
Date: 2015-03-13 00:00:00
Tasks: Scheduling

Multiple Air Vehicles (AVs) to prosecute geographically dispersed targets is
an important optimization problem. Associated multiple tasks viz., target
classification, attack and verification are successively performed on each
target. The optimal minimum time performance of these tasks requires
cooperation among vehicles such that critical time constraints are satisfied
i.e. target must be classified before it can be attacked and AV is sent to
target area to verify its destruction after target has been attacked. Here,
optimal task scheduling problem from Indian Air Force is formulated as Fuzzy
Mixed Integer Linear Programming (FMILP) problem. The solution assigns all
tasks to vehicles and performs scheduling in an optimal manner including
scheduled staged departure times. Coupled tasks involving time and task order
constraints are addressed. When AVs have sufficient endurance, existence of
optimal solution is guaranteed. The solution developed can serve as an
effective heuristic for different categories of AV optimization problems.
Fuzzy Mixed Integer Optimization Model for Regression Approach
Authors: Arindam Chaudhuri, Dipak Chatterjee
Date: 2015-03-13 00:00:00
Tasks: model, regression

Mixed Integer Optimization has been a topic of active research in past
decades. It has been used to solve Statistical problems of classification and
regression involving massive data. However, there is an inherent degree of
vagueness present in huge real life data. This impreciseness is handled by
Fuzzy Sets. In this Paper, Fuzzy Mixed Integer Optimization Method (FMIOM) is
used to find solution to Regression problem. The methodology exploits discrete
character of problem. In this way large scale problems are solved within
practical limits. The data points are separated into different polyhedral
regions and each region has its own distinct regression coefficients. In this
attempt, an attention is drawn to Statistics and Data Mining community that
Integer Optimization can be significantly used to revisit different Statistical
problems. Computational experimentations with generated and real data sets show
that FMIOM is comparable to and often outperforms current leading methods. The
results illustrate potential for significant impact of Fuzzy Integer
Optimization methods on Computational Statistics and Data Mining.
Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices
Authors: Yudong Chen, Jiaming Xu
Date: 2014-02-06 00:00:00
Tasks: Clustering, Community Detection, Stochastic Block Model

We consider two closely related problems: planted clustering and submatrix
localization. The planted clustering problem assumes that a random graph is
generated based on some underlying clusters of the nodes; the task is to
recover these clusters given the graph. The submatrix localization problem
concerns locating hidden submatrices with elevated means inside a large
real-valued random matrix. Of particular interest is the setting where the
number of clusters/submatrices is allowed to grow unbounded with the problem
size. These formulations cover several classical models such as planted clique,
planted densest subgraph, planted partition, planted coloring, and stochastic
block model, which are widely used for studying community detection and
clustering/bi-clustering.
  For both problems, we show that the space of the model parameters
(cluster/submatrix size, cluster density, and submatrix mean) can be
partitioned into four disjoint regions corresponding to decreasing statistical
and computational complexities: (1) the \emph{impossible} regime, where all
algorithms fail; (2) the \emph{hard} regime, where the computationally
expensive Maximum Likelihood Estimator (MLE) succeeds; (3) the \emph{easy}
regime, where the polynomial-time convexified MLE succeeds; (4) the
\emph{simple} regime, where a simple counting/thresholding procedure succeeds.
Moreover, we show that each of these algorithms provably fails in the previous
harder regimes.
  Our theorems establish the minimax recovery limit, which are tight up to
constants and hold with a growing number of clusters/submatrices, and provide a
stronger performance guarantee than previously known for polynomial-time
algorithms. Our study demonstrates the tradeoffs between statistical and
computational considerations, and suggests that the minimax recovery limit may
not be achievable by polynomial-time algorithms.
A Minimal Active Inference Agent
Authors: Simon McGregor, Manuel Baltieri, Christopher L. Buckley
Date: 2015-03-13 00:00:00

Research on the so-called "free-energy principle'' (FEP) in cognitive
neuroscience is becoming increasingly high-profile. To date, introductions to
this theory have proved difficult for many readers to follow, but it depends
mainly upon two relatively simple ideas: firstly that normative or teleological
values can be expressed as probability distributions (active inference), and
secondly that approximate Bayesian reasoning can be effectively performed by
gradient descent on model parameters (the free-energy principle). The notion of
active inference is of great interest for a number of disciplines including
cognitive science and artificial intelligence, as well as cognitive
neuroscience, and deserves to be more widely known.
  This paper attempts to provide an accessible introduction to active inference
and informational free-energy, for readers from a range of scientific
backgrounds. In this work introduce an agent-based model with an agent trying
to make predictions about its position in a one-dimensional discretized world
using methods from the FEP.
What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision
Authors: Jonathan Malmaud, Jonathan Huang, Vivek Rathod, Nick Johnston, Andrew Rabinovich, Kevin Murphy
Date: 2015-03-05 00:00:00
Tasks: Keyword Spotting

We present a novel method for aligning a sequence of instructions to a video
of someone carrying out a task. In particular, we focus on the cooking domain,
where the instructions correspond to the recipe. Our technique relies on an HMM
to align the recipe steps to the (automatically generated) speech transcript.
We then refine this alignment using a state-of-the-art visual food detector,
based on a deep convolutional neural network. We show that our technique
outperforms simpler techniques based on keyword spotting. It also enables
interesting applications, such as automatically illustrating recipes with
keyframes, and searching within a video for events of interest.
ASKIT: Approximate Skeletonization Kernel-Independent Treecode in High Dimensions
Authors: William B. March, Bo Xiao, George Biros
Date: 2014-10-01 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We present a fast algorithm for kernel summation problems in high-dimensions.
These problems appear in computational physics, numerical approximation,
non-parametric statistics, and machine learning. In our context, the sums
depend on a kernel function that is a pair potential defined on a dataset of
points in a high-dimensional Euclidean space. A direct evaluation of the sum
scales quadratically with the number of points. Fast kernel summation methods
can reduce this cost to linear complexity, but the constants involved do not
scale well with the dimensionality of the dataset.
  The main algorithmic components of fast kernel summation algorithms are the
separation of the kernel sum between near and far field (which is the basis for
pruning) and the efficient and accurate approximation of the far field.
  We introduce novel methods for pruning and approximating the far field. Our
far field approximation requires only kernel evaluations and does not use
analytic expansions. Pruning is not done using bounding boxes but rather
combinatorially using a sparsified nearest-neighbor graph of the input. The
time complexity of our algorithm depends linearly on the ambient dimension. The
error in the algorithm depends on the low-rank approximability of the far
field, which in turn depends on the kernel function and on the intrinsic
dimensionality of the distribution of the points. The error of the far field
approximation does not depend on the ambient dimension.
  We present the new algorithm along with experimental results that demonstrate
its performance. We report results for Gaussian kernel sums for 100 million
points in 64 dimensions, for one million points in 1000 dimensions, and for
problems in which the Gaussian kernel has a variable bandwidth. To the best of
our knowledge, all of these experiments are impossible or prohibitively
expensive with existing fast kernel summation methods.
Transitive reasoning with imprecise probabilities
Authors: Angelo Gilio, Niki Pfeifer, Giuseppe Sanfilippo
Date: 2015-03-13 00:00:00

We study probabilistically informative (weak) versions of transitivity, by
using suitable definitions of defaults and negated defaults, in the setting of
coherence and imprecise probabilities. We represent p-consistent sequences of
defaults and/or negated defaults by g-coherent imprecise probability
assessments on the respective sequences of conditional events. Finally, we
prove the coherent probability propagation rules for Weak Transitivity and the
validity of selected inference patterns by proving the p-entailment for the
associated knowledge bases.
Sparse Code Formation with Linear Inhibition
Authors: Nam Do-Hoang Le
Date: 2015-03-13 00:00:00

Sparse code formation in the primary visual cortex (V1) has been inspiration
for many state-of-the-art visual recognition systems. To stimulate this
behavior, networks are trained networks under mathematical constraint of
sparsity or selectivity. In this paper, the authors exploit another approach
which uses lateral interconnections in feature learning networks. However,
instead of adding direct lateral interconnections among neurons, we introduce
an inhibitory layer placed right after normal encoding layer. This idea
overcomes the challenge of computational cost and complexity on lateral
networks while preserving crucial objective of sparse code formation. To
demonstrate this idea, we use sparse autoencoder as normal encoding layer and
apply inhibitory layer. Early experiments in visual recognition show relative
improvements over traditional approach on CIFAR-10 dataset. Moreover, simple
installment and training process using Hebbian rule allow inhibitory layer to
be integrated into existing networks, which enables further analysis in the
future.
Hybrid multi-layer Deep CNN/Aggregator feature for image classification
Authors: Praveen Kulkarni, Joaquin Zepeda, Frederic Jurie, Patrick Perez, Louis Chevallier
Date: 2015-03-13 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Deep Convolutional Neural Networks (DCNN) have established a remarkable
performance benchmark in the field of image classification, displacing
classical approaches based on hand-tailored aggregations of local descriptors.
Yet DCNNs impose high computational burdens both at training and at testing
time, and training them requires collecting and annotating large amounts of
training data. Supervised adaptation methods have been proposed in the
literature that partially re-learn a transferred DCNN structure from a new
target dataset. Yet these require expensive bounding-box annotations and are
still computationally expensive to learn. In this paper, we address these
shortcomings of DCNN adaptation schemes by proposing a hybrid approach that
combines conventional, unsupervised aggregators such as Bag-of-Words (BoW),
with the DCNN pipeline by treating the output of intermediate layers as densely
extracted local descriptors.
  We test a variant of our approach that uses only intermediate DCNN layers on
the standard PASCAL VOC 2007 dataset and show performance significantly higher
than the standard BoW model and comparable to Fisher vector aggregation but
with a feature that is 150 times smaller. A second variant of our approach that
includes the fully connected DCNN layers significantly outperforms Fisher
vector schemes and performs comparably to DCNN approaches adapted to Pascal VOC
2007, yet at only a small fraction of the training and testing cost.
Characterizing driving behavior using automatic visual analysis
Authors: Mrinal Haloi, Dinesh Babu Jayagopi
Date: 2015-03-13 00:00:00

In this work, we present the problem of rash driving detection algorithm
using a single wide angle camera sensor, particularly useful in the Indian
context. To our knowledge this rash driving problem has not been addressed
using Image processing techniques (existing works use other sensors such as
accelerometer). Car Image processing literature, though rich and mature, does
not address the rash driving problem. In this work-in-progress paper, we
present the need to address this problem, our approach and our future plans to
build a rash driving detector.
An implementation of Apertium based Assamese morphological analyzer
Authors: Mirzanur Rahman, Shikhar Kumar Sarma
Date: 2015-03-13 00:00:00
Tasks: Morphological Analysis

Morphological Analysis is an important branch of linguistics for any Natural
Language Processing Technology. Morphology studies the word structure and
formation of word of a language. In current scenario of NLP research,
morphological analysis techniques have become more popular day by day. For
processing any language, morphology of the word should be first analyzed.
Assamese language contains very complex morphological structure. In our work we
have used Apertium based Finite-State-Transducers for developing morphological
analyzer for Assamese Language with some limited domain and we get 72.7%
accuracy