pwc-archive/papers-with-abstracts huggingface dataset:
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
Authors: Xirong Li, Tiberio Uricchio, Lamberto Ballan, Marco Bertini, Cees G. M. Snoek, Alberto del Bimbo
Date: 2015-03-28 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval, Survey, TAG

Where previous reviews on content-based image retrieval emphasize on what can
be seen in an image to bridge the semantic gap, this survey considers what
people tag about an image. A comprehensive treatise of three closely linked
problems, i.e., image tag assignment, refinement, and tag-based image retrieval
is presented. While existing works vary in terms of their targeted tasks and
methodology, they rely on the key functionality of tag relevance, i.e.
estimating the relevance of a specific tag with respect to the visual content
of a given image and its social context. By analyzing what information a
specific method exploits to construct its tag relevance function and how such
information is exploited, this paper introduces a taxonomy to structure the
growing literature, understand the ingredients of the main works, clarify their
connections and difference, and recognize their merits and limitations. For a
head-to-head comparison between the state-of-the-art, a new experimental
protocol is presented, with training sets containing 10k, 100k and 1m images
and an evaluation on three test sets, contributed by various research groups.
Eleven representative works are implemented and evaluated. Putting all this
together, the survey aims to provide an overview of the past and foster
progress for the near future.
Deep Ranking for Person Re-identification via Joint Representation Learning
Authors: Shi-Zhe Chen, Chun-Chao Guo, Jian-Huang Lai
Date: 2015-05-26 00:00:00
Tasks: Feature Engineering, Learning-To-Rank, Person Re-Identification, Representation Learning

This paper proposes a novel approach to person re-identification, a
fundamental task in distributed multi-camera surveillance systems. Although a
variety of powerful algorithms have been presented in the past few years, most
of them usually focus on designing hand-crafted features and learning metrics
either individually or sequentially. Different from previous works, we
formulate a unified deep ranking framework that jointly tackles both of these
key components to maximize their strengths. We start from the principle that
the correct match of the probe image should be positioned in the top rank
within the whole gallery set. An effective learning-to-rank algorithm is
proposed to minimize the cost corresponding to the ranking disorders of the
gallery. The ranking model is solved with a deep convolutional neural network
(CNN) that builds the relation between input image pairs and their similarity
scores through joint representation learning directly from raw image pixels.
The proposed framework allows us to get rid of feature engineering and does not
rely on any assumption. An extensive comparative evaluation is given,
demonstrating that our approach significantly outperforms all state-of-the-art
approaches, including both traditional and CNN-based methods on the challenging
VIPeR, CUHK-01 and CAVIAR4REID datasets. Additionally, our approach has better
ability to generalize across datasets without fine-tuning.
Semantic Object Parsing with Graph LSTM
Authors: Xiaodan Liang, Xiaohui Shen, Jiashi Feng, Liang Lin, Shuicheng Yan
Date: 2016-03-23 00:00:00
Tasks: Object, Superpixels

By taking the semantic object parsing task as an exemplar application
scenario, we propose the Graph Long Short-Term Memory (Graph LSTM) network,
which is the generalization of LSTM from sequential data or multi-dimensional
data to general graph-structured data. Particularly, instead of evenly and
fixedly dividing an image to pixels or patches in existing multi-dimensional
LSTM structures (e.g., Row, Grid and Diagonal LSTMs), we take each
arbitrary-shaped superpixel as a semantically consistent node, and adaptively
construct an undirected graph for each image, where the spatial relations of
the superpixels are naturally used as edges. Constructed on such an adaptive
graph topology, the Graph LSTM is more naturally aligned with the visual
patterns in the image (e.g., object boundaries or appearance similarities) and
provides a more economical information propagation route. Furthermore, for each
optimization step over Graph LSTM, we propose to use a confidence-driven scheme
to update the hidden and memory states of nodes progressively till all nodes
are updated. In addition, for each node, the forgets gates are adaptively
learned to capture different degrees of semantic correlation with neighboring
nodes. Comprehensive evaluations on four diverse semantic object parsing
datasets well demonstrate the significant superiority of our Graph LSTM over
other state-of-the-art solutions.
A Survey on Object Detection in Optical Remote Sensing Images
Authors: Gong Cheng, Junwei Han
Date: 2016-03-20 00:00:00
Tasks: Object, object-detection, Object Detection, Survey, Template Matching, Weakly-supervised Learning

Object detection in optical remote sensing images, being a fundamental but
challenging problem in the field of aerial and satellite image analysis, plays
an important role for a wide range of applications and is receiving significant
attention in recent years. While enormous methods exist, a deep review of the
literature concerning generic object detection is still lacking. This paper
aims to provide a review of the recent progress in this field. Different from
several previously published surveys that focus on a specific object class such
as building and road, we concentrate on more generic object categories
including, but are not limited to, road, building, tree, vehicle, ship,
airport, urban-area. Covering about 270 publications we survey 1) template
matching-based object detection methods, 2) knowledge-based object detection
methods, 3) object-based image analysis (OBIA)-based object detection methods,
4) machine learning-based object detection methods, and 5) five publicly
available datasets and three standard evaluation metrics. We also discuss the
challenges of current studies and propose two promising research directions,
namely deep learning-based feature representation and weakly supervised
learning-based geospatial object detection. It is our hope that this survey
will be beneficial for the researchers to have better understanding of this
research field.
Cosolver2B: An Efficient Local Search Heuristic for the Travelling Thief Problem
Authors: Mohamed El Yafrani, Belaïd Ahiod
Date: 2016-03-23 00:00:00
Tasks: Management

Real-world problems are very difficult to optimize. However, many researchers
have been solving benchmark problems that have been extensively investigated
for the last decades even if they have very few direct applications. The
Traveling Thief Problem (TTP) is a NP-hard optimization problem that aims to
provide a more realistic model. TTP targets particularly routing problem under
packing/loading constraints which can be found in supply chain management and
transportation. In this paper, TTP is presented and formulated mathematically.
A combined local search algorithm is proposed and compared with Random Local
Search (RLS) and Evolutionary Algorithm (EA). The obtained results are quite
promising since new better solutions were found.
Recurrent Neural Network Encoder with Attention for Community Question Answering
Authors: Wei-Ning Hsu, Yu Zhang, James Glass
Date: 2016-03-23 00:00:00
Tasks: Community Question Answering, Information Retrieval, Question Answering, Retrieval, Transfer Learning

We apply a general recurrent neural network (RNN) encoder framework to
community question answering (cQA) tasks. Our approach does not rely on any
linguistic processing, and can be applied to different languages or domains.
Further improvements are observed when we extend the RNN encoders with a neural
attention mechanism that encourages reasoning over entire sequences. To deal
with practical issues such as data sparsity and imbalanced labels, we apply
various techniques such as transfer learning and multitask learning. Our
experiments on the SemEval-2016 cQA task show 10% improvement on a MAP score
compared to an information retrieval-based approach, and achieve comparable
performance to a strong handcrafted feature-based method.
Comparing Human and Automated Evaluation of Open-Ended Student Responses to Questions of Evolution
Authors: Michael J Wiser, Louise S Mead, James J Smith, Robert T. Pennock
Date: 2016-03-22 00:00:00

Written responses can provide a wealth of data in understanding student
reasoning on a topic. Yet they are time- and labor-intensive to score,
requiring many instructors to forego them except as limited parts of summative
assessments at the end of a unit or course. Recent developments in Machine
Learning (ML) have produced computational methods of scoring written responses
for the presence or absence of specific concepts. Here, we compare the scores
from one particular ML program -- EvoGrader -- to human scoring of responses to
structurally- and content-similar questions that are distinct from the ones the
program was trained on. We find that there is substantial inter-rater
reliability between the human and ML scoring. However, sufficient systematic
differences remain between the human and ML scoring that we advise only using
the ML scoring for formative, rather than summative, assessment of student
reasoning.
Active Detection and Localization of Textureless Objects in Cluttered Environments
Authors: Marco Imperoli, Alberto Pretto
Date: 2016-03-22 00:00:00
Tasks: 3D Pose Estimation, Active Object Detection, Object, object-detection, Object Detection, Pose Estimation, Position

This paper introduces an active object detection and localization framework
that combines a robust untextured object detection and 3D pose estimation
algorithm with a novel next-best-view selection strategy. We address the
detection and localization problems by proposing an edge-based registration
algorithm that refines the object position by minimizing a cost directly
extracted from a 3D image tensor that encodes the minimum distance to an edge
point in a joint direction/location space. We face the next-best-view problem
by exploiting a sequential decision process that, for each step, selects the
next camera position which maximizes the mutual information between the state
and the next observations. We solve the intrinsic intractability of this
solution by generating observations that represent scene realizations, i.e.
combination samples of object hypothesis provided by the object detector, while
modeling the state by means of a set of constantly resampled particles.
Experiments performed on different real world, challenging datasets confirm the
effectiveness of the proposed methods.
Mutual Information and Diverse Decoding Improve Neural Machine Translation
Authors: Jiwei Li, Dan Jurafsky
Date: 2016-01-04 00:00:00
Tasks: Diversity, Machine Translation, Re-Ranking, Sentence, Translation

Sequence-to-sequence neural translation models learn semantic and syntactic
relations between sentence pairs by optimizing the likelihood of the target
given the source, i.e., $p(y|x)$, an objective that ignores other potentially
useful sources of information. We introduce an alternative objective function
for neural MT that maximizes the mutual information between the source and
target sentences, modeling the bi-directional dependency of sources and
targets. We implement the model with a simple re-ranking method, and also
introduce a decoding algorithm that increases diversity in the N-best list
produced by the first pass. Applied to the WMT German/English and
French/English tasks, the proposed models offers a consistent performance boost
on both standard LSTM and attention-based neural MT architectures.
Inference via Message Passing on Partially Labeled Stochastic Block Models
Authors: T. Tony Cai, Tengyuan Liang, Alexander Rakhlin
Date: 2016-03-22 00:00:00
Tasks: Community Detection

We study the community detection and recovery problem in partially-labeled
stochastic block models (SBM). We develop a fast linearized message-passing
algorithm to reconstruct labels for SBM (with $n$ nodes, $k$ blocks, $p,q$
intra and inter block connectivity) when $\delta$ proportion of node labels are
revealed. The signal-to-noise ratio ${\sf SNR}(n,k,p,q,\delta)$ is shown to
characterize the fundamental limitations of inference via local algorithms. On
the one hand, when ${\sf SNR}>1$, the linearized message-passing algorithm
provides the statistical inference guarantee with mis-classification rate at
most $\exp(-({\sf SNR}-1)/2)$, thus interpolating smoothly between strong and
weak consistency. This exponential dependence improves upon the known error
rate $({\sf SNR}-1)^{-1}$ in the literature on weak recovery. On the other
hand, when ${\sf SNR}<1$ (for $k=2$) and ${\sf SNR}<1/4$ (for general growing
$k$), we prove that local algorithms suffer an error rate at least $\frac{1}{2}
- \sqrt{\delta \cdot {\sf SNR}}$, which is only slightly better than random
guess for small $\delta$.
Completely random measures for modeling power laws in sparse graphs
Authors: Diana Cai, Tamara Broderick
Date: 2016-03-22 00:00:00
Tasks: Clustering

Network data appear in a number of applications, such as online social
networks and biological networks, and there is growing interest in both
developing models for networks as well as studying the properties of such data.
Since individual network datasets continue to grow in size, it is necessary to
develop models that accurately represent the real-life scaling properties of
networks. One behavior of interest is having a power law in the degree
distribution. However, other types of power laws that have been observed
empirically and considered for applications such as clustering and feature
allocation models have not been studied as frequently in models for graph data.
In this paper, we enumerate desirable asymptotic behavior that may be of
interest for modeling graph data, including sparsity and several types of power
laws. We outline a general framework for graph generative models using
completely random measures; by contrast to the pioneering work of Caron and Fox
(2015), we consider instantiating more of the existing atoms of the random
measure as the dataset size increases rather than adding new atoms to the
measure. We see that these two models can be complementary; they respectively
yield interpretations as (1) time passing among existing members of a network
and (2) new individuals joining a network. We detail a particular instance of
this framework and show simulated results that suggest this model exhibits some
desirable asymptotic power-law behavior.
Learning Discriminative Features using Encoder-Decoder type Deep Neural Nets
Authors: Vishwajeet Singh, Killamsetti Ravi Kumar, K. Eswaran
Date: 2016-03-22 00:00:00
Tasks: BIG-bench Machine Learning, Decoder, General Classification, Vocal Bursts Type Prediction

As machine learning is applied to an increasing variety of complex problems,
which are defined by high dimensional and complex data sets, the necessity for
task oriented feature learning grows in importance. With the advancement of
Deep Learning algorithms, various successful feature learning techniques have
evolved. In this paper, we present a novel way of learning discriminative
features by training Deep Neural Nets which have Encoder or Decoder type
architecture similar to an Autoencoder. We demonstrate that our approach can
learn discriminative features which can perform better at pattern
classification tasks when the number of training samples is relatively small in
size.
Edge-exchangeable graphs and sparsity
Authors: Tamara Broderick, Diana Cai
Date: 2016-03-22 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Clustering

A known failing of many popular random graph models is that the Aldous-Hoover
Theorem guarantees these graphs are dense with probability one; that is, the
number of edges grows quadratically with the number of nodes. This behavior is
considered unrealistic in observed graphs. We define a notion of edge
exchangeability for random graphs in contrast to the established notion of
infinite exchangeability for random graphs --- which has traditionally relied
on exchangeability of nodes (rather than edges) in a graph. We show that,
unlike node exchangeability, edge exchangeability encompasses models that are
known to provide a projective sequence of random graphs that circumvent the
Aldous-Hoover Theorem and exhibit sparsity, i.e., sub-quadratic growth of the
number of edges with the number of nodes. We show how edge-exchangeability of
graphs relates naturally to existing notions of exchangeability from clustering
(a.k.a. partitions) and other familiar combinatorial structures.
Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons
Authors: Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
Date: 2016-03-22 00:00:00

We study methods for aggregating pairwise comparison data in order to
estimate outcome probabilities for future comparisons among a collection of n
items. Working within a flexible framework that imposes only a form of strong
stochastic transitivity (SST), we introduce an adaptivity index defined by the
indifference sets of the pairwise comparison probabilities. In addition to
measuring the usual worst-case risk of an estimator, this adaptivity index also
captures the extent to which the estimator adapts to instance-specific
difficulty relative to an oracle estimator. We prove three main results that
involve this adaptivity index and different algorithms. First, we propose a
three-step estimator termed Count-Randomize-Least squares (CRL), and show that
it has adaptivity index upper bounded as $\sqrt{n}$ up to logarithmic factors.
We then show that that conditional on the hardness of planted clique, no
computationally efficient estimator can achieve an adaptivity index smaller
than $\sqrt{n}$. Second, we show that a regularized least squares estimator can
achieve a poly-logarithmic adaptivity index, thereby demonstrating a
$\sqrt{n}$-gap between optimal and computationally achievable adaptivity.
Finally, we prove that the standard least squares estimator, which is known to
be optimally adaptive in several closely related problems, fails to adapt in
the context of estimating pairwise probabilities.
Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems
Authors: Yuxin Chen, Emmanuel J. Candes
Date: 2015-05-19 00:00:00
Proceeding: NeurIPS 2015 12

We consider the fundamental problem of solving quadratic systems of equations
in $n$ variables, where $y_i = |\langle \boldsymbol{a}_i, \boldsymbol{x}
\rangle|^2$, $i = 1, \ldots, m$ and $\boldsymbol{x} \in \mathbb{R}^n$ is
unknown. We propose a novel method, which starting with an initial guess
computed by means of a spectral method, proceeds by minimizing a nonconvex
functional as in the Wirtinger flow approach. There are several key
distinguishing features, most notably, a distinct objective functional and
novel update rules, which operate in an adaptive fashion and drop terms bearing
too much influence on the search direction. These careful selection rules
provide a tighter initial guess, better descent directions, and thus enhanced
practical performance. On the theoretical side, we prove that for certain
unstructured models of quadratic systems, our algorithms return the correct
solution in linear time, i.e. in time proportional to reading the data
$\{\boldsymbol{a}_i\}$ and $\{y_i\}$ as soon as the ratio $m/n$ between the
number of equations and unknowns exceeds a fixed numerical constant. We extend
the theory to deal with noisy systems in which we only have $y_i \approx
|\langle \boldsymbol{a}_i, \boldsymbol{x} \rangle|^2$ and prove that our
algorithms achieve a statistical accuracy, which is nearly un-improvable. We
complement our theoretical study with numerical examples showing that solving
random quadratic systems is both computationally and statistically not much
harder than solving linear systems of the same size---hence the title of this
paper. For instance, we demonstrate empirically that the computational cost of
our algorithm is about four times that of solving a least-squares problem of
the same size.
Stopping criteria for boosting automatic experimental design using real-time fMRI with Bayesian optimization
Authors: Romy Lorenz, Ricardo P Monti, Ines R Violante, Aldo A. Faisal, Christoforos Anagnostopoulos, Robert Leech, Giovanni Montana
Date: 2015-11-24 00:00:00
Tasks: Bayesian Optimization, Experimental Design

Bayesian optimization has been proposed as a practical and efficient tool
through which to tune parameters in many difficult settings. Recently, such
techniques have been combined with real-time fMRI to propose a novel framework
which turns on its head the conventional functional neuroimaging approach. This
closed-loop method automatically designs the optimal experiment to evoke a
desired target brain pattern. One of the challenges associated with extending
such methods to real-time brain imaging is the need for adequate stopping
criteria, an aspect of Bayesian optimization which has received limited
attention. In light of high scanning costs and limited attentional capacities
of subjects an accurate and reliable stopping criteria is essential. In order
to address this issue we propose and empirically study the performance of two
stopping criteria.
Trading-off variance and complexity in stochastic gradient descent
Authors: Vatsal Shah, Megasthenis Asteris, Anastasios Kyrillidis, Sujay Sanghavi
Date: 2016-03-22 00:00:00

Stochastic gradient descent is the method of choice for large-scale machine
learning problems, by virtue of its light complexity per iteration. However, it
lags behind its non-stochastic counterparts with respect to the convergence
rate, due to high variance introduced by the stochastic updates. The popular
Stochastic Variance-Reduced Gradient (SVRG) method mitigates this shortcoming,
introducing a new update rule which requires infrequent passes over the entire
input dataset to compute the full-gradient.
  In this work, we propose CheapSVRG, a stochastic variance-reduction
optimization scheme. Our algorithm is similar to SVRG but instead of the full
gradient, it uses a surrogate which can be efficiently computed on a small
subset of the input data. It achieves a linear convergence rate ---up to some
error level, depending on the nature of the optimization problem---and features
a trade-off between the computational complexity and the convergence rate.
Empirical evaluation shows that CheapSVRG performs at least competitively
compared to the state of the art.
Enhanced perceptrons using contrastive biclusters
Authors: André L. V. Coelho, Fabrício O. de França
Date: 2016-03-22 00:00:00
Tasks: General Classification

Perceptrons are neuronal devices capable of fully discriminating linearly
separable classes. Although straightforward to implement and train, their
applicability is usually hindered by non-trivial requirements imposed by
real-world classification problems. Therefore, several approaches, such as
kernel perceptrons, have been conceived to counteract such difficulties. In
this paper, we investigate an enhanced perceptron model based on the notion of
contrastive biclusters. From this perspective, a good discriminative bicluster
comprises a subset of data instances belonging to one class that show high
coherence across a subset of features and high differentiation from nearest
instances of the other class under the same features (referred to as its
contrastive bicluster). Upon each local subspace associated with a pair of
contrastive biclusters a perceptron is trained and the model with highest area
under the receiver operating characteristic curve (AUC) value is selected as
the final classifier. Experiments conducted on a range of data sets, including
those related to a difficult biosignal classification problem, show that the
proposed variant can be indeed very useful, prevailing in most of the cases
upon standard and kernel perceptrons in terms of accuracy and AUC measures.