pwc-archive/papers-with-abstracts huggingface dataset:
Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval
Authors: Weixun Zhou, Shawn Newsam, Congmin Li, Zhenfeng Shao
Date: 2016-10-10 00:00:00
Tasks: Image Retrieval, Retrieval

Learning powerful feature representations for image retrieval has always been
a challenging task in the field of remote sensing. Traditional methods focus on
extracting low-level hand-crafted features which are not only time-consuming
but also tend to achieve unsatisfactory performance due to the content
complexity of remote sensing images. In this paper, we investigate how to
extract deep feature representations based on convolutional neural networks
(CNN) for high-resolution remote sensing image retrieval (HRRSIR). To this end,
two effective schemes are proposed to generate powerful feature representations
for HRRSIR. In the first scheme, the deep features are extracted from the
fully-connected and convolutional layers of the pre-trained CNN models,
respectively; in the second scheme, we propose a novel CNN architecture based
on conventional convolution layers and a three-layer perceptron. The novel CNN
model is then trained on a large remote sensing dataset to learn low
dimensional features. The two schemes are evaluated on several public and
challenging datasets, and the results indicate that the proposed schemes and in
particular the novel CNN are able to achieve state-of-the-art performance.
Bayesian Learning of Dynamic Multilayer Networks
Authors: Daniele Durante, Nabanita Mukherjee, Rebecca C. Steorts
Date: 2016-08-07 00:00:00
Tasks: Dimensionality Reduction, Gaussian Processes

A plethora of networks is being collected in a growing number of fields,
including disease transmission, international relations, social interactions,
and others. As data streams continue to grow, the complexity associated with
these highly multidimensional connectivity data presents novel challenges. In
this paper, we focus on the time-varying interconnections among a set of actors
in multiple contexts, called layers. Current literature lacks flexible
statistical models for dynamic multilayer networks, which can enhance quality
in inference and prediction by efficiently borrowing information within each
network, across time, and between layers. Motivated by this gap, we develop a
Bayesian nonparametric model leveraging latent space representations. Our
formulation characterizes the edge probabilities as a function of shared and
layer-specific actors positions in a latent space, with these positions
changing in time via Gaussian processes. This representation facilitates
dimensionality reduction and incorporates different sources of information in
the observed data. In addition, we obtain tractable procedures for posterior
computation, inference, and prediction. We provide theoretical results on the
flexibility of our model. Our methods are tested on simulations and infection
studies monitoring dynamic face-to-face contacts among individuals in multiple
days, where we perform better than current methods in inference and prediction.
On statistical learning via the lens of compression
Authors: Ofir David, Shay Moran, Amir Yehudayoff
Date: 2016-10-12 00:00:00
Tasks: Binary Classification, Learning Theory

This work continues the study of the relationship between sample compression
schemes and statistical learning, which has been mostly investigated within the
framework of binary classification. The central theme of this work is
establishing equivalences between learnability and compressibility, and
utilizing these equivalences in the study of statistical learning theory.
  We begin with the setting of multiclass categorization (zero/one loss). We
prove that in this case learnability is equivalent to compression of
logarithmic sample size, and that uniform convergence implies compression of
constant size.
  We then consider Vapnik's general learning setting: we show that in order to
extend the compressibility-learnability equivalence to this case, it is
necessary to consider an approximate variant of compression.
  Finally, we provide some applications of the compressibility-learnability
equivalences:
  (i) Agnostic-case learnability and realizable-case learnability are
equivalent in multiclass categorization problems (in terms of sample
complexity).
  (ii) This equivalence between agnostic-case learnability and realizable-case
learnability does not hold for general learning problems: There exists a
learning problem whose loss function takes just three values, under which
agnostic-case and realizable-case learnability are not equivalent.
  (iii) Uniform convergence implies compression of constant size in multiclass
categorization problems. Part of the argument includes an analysis of the
uniform convergence rate in terms of the graph dimension, in which we improve
upon previous bounds.
  (iv) A dichotomy for sample compression in multiclass categorization
problems: If a non-trivial compression exists then a compression of logarithmic
size exists.
  (v) A compactness theorem for multiclass categorization problems.
Clustering with Confidence: Finding Clusters with Statistical Guarantees
Authors: Andreas Henelius, Kai Puolamäki, Henrik Boström, Panagiotis Papapetrou
Date: 2016-12-27 00:00:00
Tasks: Clustering

Clustering is a widely used unsupervised learning method for finding
structure in the data. However, the resulting clusters are typically presented
without any guarantees on their robustness; slightly changing the used data
sample or re-running a clustering algorithm involving some stochastic component
may lead to completely different clusters. There is, hence, a need for
techniques that can quantify the instability of the generated clusters. In this
study, we propose a technique for quantifying the instability of a clustering
solution and for finding robust clusters, termed core clusters, which
correspond to clusters where the co-occurrence probability of each data item
within a cluster is at least $1 - \alpha$. We demonstrate how solving the core
clustering problem is linked to finding the largest maximal cliques in a graph.
We show that the method can be used with both clustering and classification
algorithms. The proposed method is tested on both simulated and real datasets.
The results show that the obtained clusters indeed meet the guarantees on
robustness.
PAMPO: using pattern matching and pos-tagging for effective Named Entities recognition in Portuguese
Authors: Conceição Rocha, Alípio Jorge, Roberta Sionara, Paula Brito, Carlos Pimenta, Solange Rezende
Date: 2016-12-30 00:00:00
Tasks: Entity Extraction using GAN, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Part-Of-Speech Tagging, POS, POS Tagging

This paper deals with the entity extraction task (named entity recognition)
of a text mining process that aims at unveiling non-trivial semantic
structures, such as relationships and interaction between entities or
communities. In this paper we present a simple and efficient named entity
extraction algorithm. The method, named PAMPO (PAttern Matching and POs tagging
based algorithm for NER), relies on flexible pattern matching, part-of-speech
tagging and lexical-based rules. It was developed to process texts written in
Portuguese, however it is potentially applicable to other languages as well.
  We compare our approach with current alternatives that support Named Entity
Recognition (NER) for content written in Portuguese. These are Alchemy, Zemanta
and Rembrandt. Evaluation of the efficacy of the entity extraction method on
several texts written in Portuguese indicates a considerable improvement on
$recall$ and $F_1$ measures.
Clique-Width and Directed Width Measures for Answer-Set Programming
Authors: Bernhard Bliem, Sebastian Ordyniak, Stefan Woltran
Date: 2016-06-30 00:00:00

Disjunctive Answer Set Programming (ASP) is a powerful declarative
programming paradigm whose main decision problems are located on the second
level of the polynomial hierarchy. Identifying tractable fragments and
developing efficient algorithms for such fragments are thus important
objectives in order to complement the sophisticated ASP systems available to
date. Hard problems can become tractable if some problem parameter is bounded
by a fixed constant; such problems are then called fixed-parameter tractable
(FPT). While several FPT results for ASP exist, parameters that relate to
directed or signed graphs representing the program at hand have been neglected
so far. In this paper, we first give some negative observations showing that
directed width measures on the dependency graph of a program do not lead to FPT
results. We then consider the graph parameter of signed clique-width and
present a novel dynamic programming algorithm that is FPT w.r.t. this
parameter. Clique-width is more general than the well-known treewidth, and, to
the best of our knowledge, ours is the first FPT algorithm for bounded
clique-width for reasoning problems beyond SAT.
Adaptive Lambda Least-Squares Temporal Difference Learning
Authors: Timothy A. Mann, Hugo Penedones, Shie Mannor, Todd Hester
Date: 2016-12-30 00:00:00
Tasks: Reinforcement Learning

Temporal Difference learning or TD($\lambda$) is a fundamental algorithm in
the field of reinforcement learning. However, setting TD's $\lambda$ parameter,
which controls the timescale of TD updates, is generally left up to the
practitioner. We formalize the $\lambda$ selection problem as a bias-variance
trade-off where the solution is the value of $\lambda$ that leads to the
smallest Mean Squared Value Error (MSVE). To solve this trade-off we suggest
applying Leave-One-Trajectory-Out Cross-Validation (LOTO-CV) to search the
space of $\lambda$ values. Unfortunately, this approach is too computationally
expensive for most practical applications. For Least Squares TD (LSTD) we show
that LOTO-CV can be implemented efficiently to automatically tune $\lambda$ and
apply function optimization methods to efficiently search the space of
$\lambda$ values. The resulting algorithm, ALLSTD, is parameter free and our
experiments demonstrate that ALLSTD is significantly computationally faster
than the na\"{i}ve LOTO-CV implementation while achieving similar performance.
Data driven estimation of Laplace-Beltrami operator
Authors: Frédéric Chazal, Ilaria Giulini, Bertrand Michel
Date: 2016-12-30 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: BIG-bench Machine Learning

Approximations of Laplace-Beltrami operators on manifolds through graph
Lapla-cians have become popular tools in data analysis and machine learning.
These discretized operators usually depend on bandwidth parameters whose tuning
remains a theoretical and practical problem. In this paper, we address this
problem for the unnormalized graph Laplacian by establishing an oracle
inequality that opens the door to a well-founded data-driven procedure for the
bandwidth selection. Our approach relies on recent results by Lacour and
Massart [LM15] on the so-called Lepski's method.
Curiosity-Aware Bargaining
Authors: Cédric Buron, Sylvain Ductor, Zahia Guessoum
Date: 2016-12-30 00:00:00

Opponent modeling consists in modeling the strategy or preferences of an
agent thanks to the data it provides. In the context of automated negotiation
and with machine learning, it can result in an advantage so overwhelming that
it may restrain some casual agents to be part of the bargaining process. We
qualify as "curious" an agent driven by the desire of negotiating in order to
collect information and improve its opponent model. However, neither
curiosity-based rational-ity nor curiosity-robust protocol have been studied in
automatic negotiation. In this paper, we rely on mechanism design to propose
three extensions of the standard bargaining protocol that limit information
leak. Those extensions are supported by an enhanced rationality model, that
considers the exchanged information. Also, they are theoretically analyzed and
experimentally evaluated.
Video Ladder Networks
Authors: Francesco Cricri, Xingyang Ni, Mikko Honkala, Emre Aksu, Moncef Gabbouj
Date: 2016-12-06 00:00:00
Tasks: Decoder

We present the Video Ladder Network (VLN) for efficiently generating future
video frames. VLN is a neural encoder-decoder model augmented at all layers by
both recurrent and feedforward lateral connections. At each layer, these
connections form a lateral recurrent residual block, where the feedforward
connection represents a skip connection and the recurrent connection represents
the residual. Thanks to the recurrent connections, the decoder can exploit
temporal summaries generated from all layers of the encoder. This way, the top
layer is relieved from the pressure of modeling lower-level spatial and
temporal details. Furthermore, we extend the basic version of VLN to
incorporate ResNet-style residual blocks in the encoder and decoder, which help
improving the prediction results. VLN is trained in self-supervised regime on
the Moving MNIST dataset, achieving competitive results while having very
simple structure and providing fast inference.
Automatic labeling of molecular biomarkers of whole slide immunohistochemistry images using fully convolutional networks
Authors: Fahime Sheikhzadeh, Martial Guillaud, Rabab K. Ward
Date: 2016-12-30 00:00:00
Tasks: whole slide images

This paper addresses the problem of quantifying biomarkers in multi-stained
tissues, based on color and spatial information. A deep learning based method
that can automatically localize and quantify the cells expressing biomarker(s)
in a whole slide image is proposed. The deep learning network is a fully
convolutional network (FCN) whose input is the true RGB color image of a tissue
and output is a map of the different biomarkers. The FCN relies on a
convolutional neural network (CNN) that classifies each cell separately
according to the biomarker it expresses. In this study, images of
immunohistochemistry (IHC) stained slides were collected and used. More than
4,500 RGB images of cells were manually labeled based on the expressing
biomarkers. The labeled cell images were used to train the CNN (obtaining an
accuracy of 92% in a test set). The trained CNN is then extended to an FCN that
generates a map of all biomarkers in the whole slide image acquired by the
scanner (instead of classifying every cell image). To evaluate our method, we
manually labeled all nuclei expressing different biomarkers in two whole slide
images and used theses as the ground truth. Our proposed method for
immunohistochemical analysis compares well with the manual labeling by humans
(average F-score of 0.96).
Shape Estimation from Defocus Cue for Microscopy Images via Belief Propagation
Authors: Arnav Bhavsar
Date: 2016-12-30 00:00:00
Tasks: Combinatorial Optimization, Disparity Estimation, Image Restoration, Stereo Disparity Estimation

In recent years, the usefulness of 3D shape estimation is being realized in
microscopic or close-range imaging, as the 3D information can further be used
in various applications. Due to limited depth of field at such small distances,
the defocus blur induced in images can provide information about the 3D shape
of the object. The task of `shape from defocus' (SFD), involves the problem of
estimating good quality 3D shape estimates from images with depth-dependent
defocus blur. While the research area of SFD is quite well-established, the
approaches have largely demonstrated results on objects with bulk/coarse shape
variation. However, in many cases, objects studied under microscopes often
involve fine/detailed structures, which have not been explicitly considered in
most methods. In addition, given that, in recent years, large data volumes are
typically associated with microscopy related applications, it is also important
for such SFD methods to be efficient. In this work, we provide an indication of
the usefulness of the Belief Propagation (BP) approach in addressing these
concerns for SFD. BP has been known to be an efficient combinatorial
optimization approach, and has been empirically demonstrated to yield good
quality solutions in low-level vision problems such as image restoration,
stereo disparity estimation etc. For exploiting the efficiency of BP in SFD, we
assume local space-invariance of the defocus blur, which enables the
application of BP in a straightforward manner. Even with such an assumption,
the ability of BP to provide good quality solutions while using non-convex
priors, reflects in yielding plausible shape estimates in presence of fine
structures on the objects under microscopy imaging.
Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks
Authors: Pichao Wang, Wanqing Li, Chuankun Li, Yonghong Hou
Date: 2016-12-30 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Convolutional Neural Networks (ConvNets) have recently shown promising
performance in many computer vision tasks, especially image-based recognition.
How to effectively apply ConvNets to sequence-based data is still an open
problem. This paper proposes an effective yet simple method to represent
spatio-temporal information carried in $3D$ skeleton sequences into three $2D$
images by encoding the joint trajectories and their dynamics into color
distribution in the images, referred to as Joint Trajectory Maps (JTM), and
adopts ConvNets to learn the discriminative features for human action
recognition. Such an image-based representation enables us to fine-tune
existing ConvNets models for the classification of skeleton sequences without
training the networks afresh. The three JTMs are generated in three orthogonal
planes and provide complimentary information to each other. The final
recognition is further improved through multiply score fusion of the three
JTMs. The proposed method was evaluated on four public benchmark datasets, the
large NTU RGB+D Dataset, MSRC-12 Kinect Gesture Dataset (MSRC-12), G3D Dataset
and UTD Multimodal Human Action Dataset (UTD-MHAD) and achieved the
state-of-the-art results.
Neural Machine Translation Advised by Statistical Machine Translation
Authors: Xing Wang, Zhengdong Lu, Zhaopeng Tu, Hang Li, Deyi Xiong, Min Zhang
Date: 2016-10-17 00:00:00
Tasks: Machine Translation, NMT, Translation

Neural Machine Translation (NMT) is a new approach to machine translation
that has made great progress in recent years. However, recent studies show that
NMT generally produces fluent but inadequate translations (Tu et al. 2016b; Tu
et al. 2016a; He et al. 2016; Tu et al. 2017). This is in contrast to
conventional Statistical Machine Translation (SMT), which usually yields
adequate but non-fluent translations. It is natural, therefore, to leverage the
advantages of both models for better translations, and in this work we propose
to incorporate SMT model into NMT framework. More specifically, at each
decoding step, SMT offers additional recommendations of generated words based
on the decoding information from NMT (e.g., the generated partial translation
and attention history). Then we employ an auxiliary classifier to score the SMT
recommendations and a gating function to combine the SMT recommendations with
NMT generations, both of which are jointly trained within the NMT architecture
in an end-to-end manner. Experimental results on Chinese-English translation
show that the proposed approach achieves significant and consistent
improvements over state-of-the-art NMT and SMT systems on multiple NIST test
sets.
Analysis of the noise in back-projection light field acquisition and its optimization
Authors: Ni Chen, Zhenbo Ren, Dayan Li, Edmund Y. Lam, Guohai Situ
Date: 2016-12-30 00:00:00

Light field reconstruction from images captured by focal plane sweeping can
achieve high lateral resolution comparable to the modern camera sensor. This is
impossible for the conventional micro-lenslet based light field capture
systems. However, the severe defocus noise and the low depth resolution limit
its applications. In this paper, we analyze the defocus noise and the depth
resolution in the focal plane sweeping based light field reconstruction
technique, and propose a method to reduce the defocus noise and improve the
depth resolution. Both numerical and experimental results verify the proposed
method.
Robust Structure from Motion in the Presence of Outliers and Missing Data
Authors: Guanghui Wang
Date: 2016-09-09 00:00:00

Structure from motion is an import theme in computer vision. Although great
progress has been made both in theory and applications, most of the algorithms
only work for static scenes and rigid objects. In recent years, structure and
motion recovery of non-rigid objects and dynamic scenes have received a lot of
attention. In this paper, the state-of-the-art techniques for structure and
motion factorization of non-rigid objects are reviewed and discussed. First, an
introduction of the structure from motion problem is presented, followed by a
general formulation of non-rigid structure from motion. Second, an augmented
affined factorization framework, by using homogeneous representation, is
presented to solve the registration issue in the presence of outlying and
missing data. Third, based on the observation that the reprojection residuals
of outliers are significantly larger than those of inliers, a robust
factorization strategy with outlier rejection is proposed by means of the
reprojection residuals, followed by some comparative experimental evaluations.
Finally, some future research topics in non-rigid structure from motion are
discussed.
Communication-efficient Distributed Estimation and Inference for Transelliptical Graphical Models
Authors: Pan Xu, Lu Tian, Quanquan Gu
Date: 2016-12-29 00:00:00

We propose communication-efficient distributed estimation and inference
methods for the transelliptical graphical model, a semiparametric extension of
the elliptical distribution in the high dimensional regime. In detail, the
proposed method distributes the $d$-dimensional data of size $N$ generated from
a transelliptical graphical model into $m$ worker machines, and estimates the
latent precision matrix on each worker machine based on the data of size
$n=N/m$. It then debiases the local estimators on the worker machines and send
them back to the master machine. Finally, on the master machine, it aggregates
the debiased local estimators by averaging and hard thresholding. We show that
the aggregated estimator attains the same statistical rate as the centralized
estimator based on all the data, provided that the number of machines satisfies
$m \lesssim \min\{N\log d/d,\sqrt{N/(s^2\log d)}\}$, where $s$ is the maximum
number of nonzero entries in each column of the latent precision matrix. It is
worth noting that our algorithm and theory can be directly applied to Gaussian
graphical models, Gaussian copula graphical models and elliptical graphical
models, since they are all special cases of transelliptical graphical models.
Thorough experiments on synthetic data back up our theory.
Generalized Intersection Kernel
Authors: Ping Li
Date: 2016-12-29 00:00:00

Following the very recent line of work on the ``generalized min-max'' (GMM)
kernel, this study proposes the ``generalized intersection'' (GInt) kernel and
the related ``normalized generalized min-max'' (NGMM) kernel. In computer
vision, the (histogram) intersection kernel has been popular, and the GInt
kernel generalizes it to data which can have both negative and positive
entries. Through an extensive empirical classification study on 40 datasets
from the UCI repository, we are able to show that this (tuning-free) GInt
kernel performs fairly well.
  The empirical results also demonstrate that the NGMM kernel typically
outperforms the GInt kernel. Interestingly, the NGMM kernel has another
interpretation --- it is the ``asymmetrically transformed'' version of the GInt
kernel, based on the idea of ``asymmetric hashing''. Just like the GMM kernel,
the NGMM kernel can be efficiently linearized through (e.g.,) generalized
consistent weighted sampling (GCWS), as empirically validated in our study.
Owing to the discrete nature of hashed values, it also provides a scheme for
approximate near neighbor search.