pwc-archive/papers-with-abstracts huggingface dataset:
ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images
Authors: Kui Jia, Tsung-Han Chan, Zinan Zeng, Shenghua Gao, Gang Wang, Tianzhu Zhang, Yi Ma
Date: 2014-03-31 00:00:00
Tasks: 3D Reconstruction, Distributed Optimization, Motion Segmentation, Object, Object Localization, Object Recognition

Feature-based object matching is a fundamental problem for many applications
in computer vision, such as object recognition, 3D reconstruction, tracking,
and motion segmentation. In this work, we consider simultaneously matching
object instances in a set of images, where both inlier and outlier features are
extracted. The task is to identify the inlier features and establish their
consistent correspondences across the image set. This is a challenging
combinatorial problem, and the problem complexity grows exponentially with the
image number. To this end, we propose a novel framework, termed ROML, to
address this problem. ROML optimizes simultaneously a partial permutation
matrix (PPM) for each image, and feature correspondences are established by the
obtained PPMs. Two of our key contributions are summarized as follows. (1) We
formulate the problem as rank and sparsity minimization for PPM optimization,
and treat simultaneous optimization of multiple PPMs as a regularized consensus
problem in the context of distributed optimization. (2) We use the ADMM method
to solve the thus formulated ROML problem, in which a subproblem associated
with a single PPM optimization appears to be a difficult integer quadratic
program (IQP). We prove that under wildly applicable conditions, this IQP is
equivalent to a linear sum assignment problem (LSAP), which can be efficiently
solved to an exact solution. Extensive experiments on rigid/non-rigid object
matching, matching instances of a common object category, and common object
localization show the efficacy of our proposed method.
Algorithmic Robustness for Learning via $(ε, γ, τ)$-Good Similarity Functions
Authors: Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, Éric Gaussier, Massih-Reza Amini
Date: 2014-12-19 00:00:00
Tasks: Clustering, General Classification

The notion of metric plays a key role in machine learning problems such as
classification, clustering or ranking. However, it is worth noting that there
is a severe lack of theoretical guarantees that can be expected on the
generalization capacity of the classifier associated to a given metric. The
theoretical framework of $(\epsilon, \gamma, \tau)$-good similarity functions
(Balcan et al., 2008) has been one of the first attempts to draw a link between
the properties of a similarity function and those of a linear classifier making
use of it. In this paper, we extend and complete this theory by providing a new
generalization bound for the associated classifier based on the algorithmic
robustness framework.
Giving the AI definition a form suitable for the engineer
Authors: Dimiter Dobrev
Date: 2013-12-19 00:00:00
Tasks: Form

Artificial Intelligence - what is this? That is the question! In earlier
papers we already gave a formal definition for AI, but if one desires to build
an actual AI implementation, the following issues require attention and are
treated here: the data format to be used, the idea of Undef and Nothing
symbols, various ways for defining the "meaning of life", and finally, a new
notion of "incorrect move". These questions are of minor importance in the
theoretical discussion, but we already know the answer of the question "Does AI
exist?" Now we want to make the next step and to create this program.
Thompson Sampling for Learning Parameterized Markov Decision Processes
Authors: Aditya Gopalan, Shie Mannor
Date: 2014-06-29 00:00:00
Tasks: Form, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

We consider reinforcement learning in parameterized Markov Decision Processes
(MDPs), where the parameterization may induce correlation across transition
probabilities or rewards. Consequently, observing a particular state transition
might yield useful information about other, unobserved, parts of the MDP. We
present a version of Thompson sampling for parameterized reinforcement learning
problems, and derive a frequentist regret bound for priors over general
parameter spaces. The result shows that the number of instants where suboptimal
actions are chosen scales logarithmically with time, with high probability. It
holds for prior distributions that put significant probability near the true
model, without any additional, specific closed-form structure such as conjugate
or product-form priors. The constant factor in the logarithmic scaling encodes
the information complexity of learning the MDP in terms of the Kullback-Leibler
geometry of the parameter space.
Fast Label Embeddings for Extremely Large Output Spaces
Authors: Paul Mineiro, Nikos Karampatziakis
Date: 2015-03-30 00:00:00

Many modern multiclass and multilabel problems are characterized by
increasingly large output spaces. For these problems, label embeddings have
been shown to be a useful primitive that can improve computational and
statistical efficiency. In this work we utilize a correspondence between rank
constrained estimation and low dimensional label embeddings that uncovers a
fast label embedding algorithm which works in both the multiclass and
multilabel settings. The result is a randomized algorithm for partial least
squares, whose running time is exponentially faster than naive algorithms. We
demonstrate our techniques on two large-scale public datasets, from the Large
Scale Hierarchical Text Challenge and the Open Directory Project, where we
obtain state of the art results.
Decentralized learning for wireless communications and networking
Authors: Georgios B. Giannakis, Qing Ling, Gonzalo Mateos, Ioannis D. Schizas, Hao Zhu
Date: 2015-03-30 00:00:00
Tasks: Spectrum Cartography, State Estimation

This chapter deals with decentralized learning algorithms for in-network
processing of graph-valued data. A generic learning problem is formulated and
recast into a separable form, which is iteratively minimized using the
alternating-direction method of multipliers (ADMM) so as to gain the desired
degree of parallelization. Without exchanging elements from the distributed
training sets and keeping inter-node communications at affordable levels, the
local (per-node) learners consent to the desired quantity inferred globally,
meaning the one obtained if the entire training data set were centrally
available. Impact of the decentralized learning framework to contemporary
wireless communications and networking tasks is illustrated through case
studies including target tracking using wireless sensor networks, unveiling
Internet traffic anomalies, power system state estimation, as well as spectrum
cartography for wireless cognitive radio networks.
Reconciling saliency and object center-bias hypotheses in explaining free-viewing fixations
Authors: Ali Borji, James Tanner
Date: 2015-03-30 00:00:00
Tasks: Object

Predicting where people look in natural scenes has attracted a lot of
interest in computer vision and computational neuroscience over the past two
decades. Two seemingly contrasting categories of cues have been proposed to
influence where people look: \textit{low-level image saliency} and
\textit{high-level semantic information}. Our first contribution is to take a
detailed look at these cues to confirm the hypothesis proposed by
Henderson~\cite{henderson1993eye} and Nuthmann \&
Henderson~\cite{nuthmann2010object} that observers tend to look at the center
of objects. We analyzed fixation data for scene free-viewing over 17 observers
on 60 fully annotated images with various types of objects. Images contained
different types of scenes, such as natural scenes, line drawings, and 3D
rendered scenes. Our second contribution is to propose a simple combined model
of low-level saliency and object center-bias that outperforms each individual
component significantly over our data, as well as on the OSIE dataset by Xu et
al.~\cite{xu2014predicting}. The results reconcile saliency with object
center-bias hypotheses and highlight that both types of cues are important in
guiding fixations. Our work opens new directions to understand strategies that
humans use in observing scenes and objects, and demonstrates the construction
of combined models of low-level saliency and high-level object-based
information.
Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2D Face Pose Estimation and Heart Segmentation in 3D CT Images
Authors: Peng Sun, James K. Min, Guanglei Xiong
Date: 2015-03-30 00:00:00
Tasks: 3D Pose Estimation, Heart Segmentation, Pose Estimation

Recently, a successful pose estimation algorithm, called Cascade Pose
Regression (CPR), was proposed in the literature. Trained over Pose Index
Feature, CPR is a regressor ensemble that is similar to Boosting. In this paper
we show how CPR can be represented as a Neural Network. Specifically, we adopt
a Graph Transformer Network (GTN) representation and accordingly train CPR with
Back Propagation (BP) that permits globally tuning. In contrast, previous CPR
literature only took a layer wise training without any post fine tuning. We
empirically show that global training with BP outperforms layer-wise
(pre-)training. Our CPR-GTN adopts a Multi Layer Percetron as the regressor,
which utilized sparse connection to learn local image feature representation.
We tested the proposed CPR-GTN on 2D face pose estimation problem as in
previous CPR literature. Besides, we also investigated the possibility of
extending CPR-GTN to 3D pose estimation by doing experiments using 3D Computed
Tomography dataset for heart segmentation.
Deep Hierarchical Parsing for Semantic Segmentation
Authors: Abhishek Sharma, Oncel Tuzel, David W. Jacobs
Date: 2015-03-09 00:00:00
Proceeding: CVPR 2015 6
Tasks: General Classification, Scene Parsing, Segmentation, Semantic Segmentation

This paper proposes a learning-based approach to scene parsing inspired by
the deep Recursive Context Propagation Network (RCPN). RCPN is a deep
feed-forward neural network that utilizes the contextual information from the
entire image, through bottom-up followed by top-down context propagation via
random binary parse trees. This improves the feature representation of every
super-pixel in the image for better classification into semantic categories. We
analyze RCPN and propose two novel contributions to further improve the model.
We first analyze the learning of RCPN parameters and discover the presence of
bypass error paths in the computation graph of RCPN that can hinder contextual
propagation. We propose to tackle this problem by including the classification
loss of the internal nodes of the random parse trees in the original RCPN loss
function. Secondly, we use an MRF on the parse tree nodes to model the
hierarchical dependency present in the output. Both modifications provide
performance boosts over the original RCPN and the new system achieves
state-of-the-art performance on Stanford Background, SIFT-Flow and Daimler
urban datasets.
Orthogonal Matching Pursuit with Noisy and Missing Data: Low and High Dimensional Results
Authors: Yudong Chen, Constantine Caramanis
Date: 2012-06-05 00:00:00
Tasks: regression

Many models for sparse regression typically assume that the covariates are
known completely, and without noise. Particularly in high-dimensional
applications, this is often not the case. This paper develops efficient
OMP-like algorithms to deal with precisely this setting. Our algorithms are as
efficient as OMP, and improve on the best-known results for missing and noisy
data in regression, both in the high-dimensional setting where we seek to
recover a sparse vector from only a few measurements, and in the classical
low-dimensional setting where we recover an unstructured regressor. In the
high-dimensional setting, our support-recovery algorithm requires no knowledge
of even the statistics of the noise. Along the way, we also obtain improved
performance guarantees for OMP for the standard sparse regression problem with
Gaussian noise.
A Parzen-based distance between probability measures as an alternative of summary statistics in Approximate Bayesian Computation
Authors: Carlos D. Zuluaga, Edgar A. Valencia, Mauricio A. Álvarez
Date: 2015-03-30 00:00:00
Tasks: Bayesian Inference

Approximate Bayesian Computation (ABC) are likelihood-free Monte Carlo
methods. ABC methods use a comparison between simulated data, using different
parameters drew from a prior distribution, and observed data. This comparison
process is based on computing a distance between the summary statistics from
the simulated data and the observed data. For complex models, it is usually
difficult to define a methodology for choosing or constructing the summary
statistics. Recently, a nonparametric ABC has been proposed, that uses a
dissimilarity measure between discrete distributions based on empirical kernel
embeddings as an alternative for summary statistics. The nonparametric ABC
outperforms other methods including ABC, kernel ABC or synthetic likelihood
ABC. However, it assumes that the probability distributions are discrete, and
it is not robust when dealing with few observations. In this paper, we propose
to apply kernel embeddings using an smoother density estimator or Parzen
estimator for comparing the empirical data distributions, and computing the ABC
posterior. Synthetic data and real data were used to test the Bayesian
inference of our method. We compare our method with respect to state-of-the-art
methods, and demonstrate that our method is a robust estimator of the posterior
distribution in terms of the number of observations.
Regression with Linear Factored Functions
Authors: Wendelin Böhmer, Klaus Obermayer
Date: 2014-12-19 00:00:00
Tasks: Gaussian Processes, regression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many applications that use empirically estimated functions face a curse of
dimensionality, because the integrals over most function classes must be
approximated by sampling. This paper introduces a novel regression-algorithm
that learns linear factored functions (LFF). This class of functions has
structural properties that allow to analytically solve certain integrals and to
calculate point-wise products. Applications like belief propagation and
reinforcement learning can exploit these properties to break the curse and
speed up computation. We derive a regularized greedy optimization scheme, that
learns factored basis functions during training. The novel regression algorithm
performs competitively to Gaussian processes on benchmark tasks, and the
learned LFF functions are with 4-9 factored basis functions on average very
compact.
Sparse plus low-rank autoregressive identification in neuroimaging time series
Authors: Raphaël Liégeois, Bamdev Mishra, Mattia Zorzi, Rodolphe Sepulchre
Date: 2015-03-30 00:00:00
Tasks: Time Series, Time Series Analysis

This paper considers the problem of identifying multivariate autoregressive
(AR) sparse plus low-rank graphical models. Based on the corresponding problem
formulation recently presented, we use the alternating direction method of
multipliers (ADMM) to efficiently solve it and scale it to sizes encountered in
neuroimaging applications. We apply this decomposition on synthetic and real
neuroimaging datasets with a specific focus on the information encoded in the
low-rank structure of our model. In particular, we illustrate that this
information captures the spatio-temporal structure of the original data,
generalizing classical component analysis approaches.
Speech Recognition Front End Without Information Loss
Authors: Matthew Ager, Zoran Cvetkovic, Peter Sollich
Date: 2013-12-24 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), General Classification, speech-recognition, Speech Recognition

Speech representation and modelling in high-dimensional spaces of acoustic
waveforms, or a linear transformation thereof, is investigated with the aim of
improving the robustness of automatic speech recognition to additive noise. The
motivation behind this approach is twofold: (i) the information in acoustic
waveforms that is usually removed in the process of extracting low-dimensional
features might aid robust recognition by virtue of structured redundancy
analogous to channel coding, (ii) linear feature domains allow for exact noise
adaptation, as opposed to representations that involve non-linear processing
which makes noise adaptation challenging. Thus, we develop a generative
framework for phoneme modelling in high-dimensional linear feature domains, and
use it in phoneme classification and recognition tasks. Results show that
classification and recognition in this framework perform better than analogous
PLP and MFCC classifiers below 18 dB SNR. A combination of the high-dimensional
and MFCC features at the likelihood level performs uniformly better than either
of the individual representations across all noise levels.
LSHTC: A Benchmark for Large-Scale Text Classification
Authors: Ioannis Partalas, Aris Kosmopoulos, Nicolas Baskiotis, Thierry Artieres, George Paliouras, Eric Gaussier, Ion Androutsopoulos, Massih-Reza Amini, Patrick Galinari
Date: 2015-03-30 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

LSHTC is a series of challenges which aims to assess the performance of
classification systems in large-scale classification in a a large number of
classes (up to hundreds of thousands). This paper describes the dataset that
have been released along the LSHTC series. The paper details the construction
of the datsets and the design of the tracks as well as the evaluation measures
that we implemented and a quick overview of the results. All of these datasets
are available online and runs may still be submitted on the online server of
the challenges.
Normalization of Non-Standard Words in Croatian Texts
Authors: Slobodan Beliga, Miran Pobar, Sanda Martinčić-Ipšić
Date: 2015-03-27 00:00:00
Tasks: Form, General Classification, Speech Synthesis, Text Normalization, text-to-speech, Text to Speech, Text-To-Speech Synthesis

This paper presents text normalization which is an integral part of any
text-to-speech synthesis system. Text normalization is a set of methods with a
task to write non-standard words, like numbers, dates, times, abbreviations,
acronyms and the most common symbols, in their full expanded form are
presented. The whole taxonomy for classification of non-standard words in
Croatian language together with rule-based normalization methods combined with
a lookup dictionary are proposed. Achieved token rate for normalization of
Croatian texts is 95%, where 80% of expanded words are in correct morphological
form.
Optimal Bounds on Approximation of Submodular and XOS Functions by Juntas
Authors: Vitaly Feldman, Jan Vondrak
Date: 2013-07-12 00:00:00
Tasks: PAC learning

We investigate the approximability of several classes of real-valued
functions by functions of a small number of variables ({\em juntas}). Our main
results are tight bounds on the number of variables required to approximate a
function $f:\{0,1\}^n \rightarrow [0,1]$ within $\ell_2$-error $\epsilon$ over
the uniform distribution: 1. If $f$ is submodular, then it is $\epsilon$-close
to a function of $O(\frac{1}{\epsilon^2} \log \frac{1}{\epsilon})$ variables.
This is an exponential improvement over previously known results. We note that
$\Omega(\frac{1}{\epsilon^2})$ variables are necessary even for linear
functions. 2. If $f$ is fractionally subadditive (XOS) it is $\epsilon$-close
to a function of $2^{O(1/\epsilon^2)}$ variables. This result holds for all
functions with low total $\ell_1$-influence and is a real-valued analogue of
Friedgut's theorem for boolean functions. We show that $2^{\Omega(1/\epsilon)}$
variables are necessary even for XOS functions.
  As applications of these results, we provide learning algorithms over the
uniform distribution. For XOS functions, we give a PAC learning algorithm that
runs in time $2^{poly(1/\epsilon)} poly(n)$. For submodular functions we give
an algorithm in the more demanding PMAC learning model (Balcan and Harvey,
2011) which requires a multiplicative $1+\gamma$ factor approximation with
probability at least $1-\epsilon$ over the target distribution. Our uniform
distribution algorithm runs in time $2^{poly(1/(\gamma\epsilon))} poly(n)$.
This is the first algorithm in the PMAC model that over the uniform
distribution can achieve a constant approximation factor arbitrarily close to 1
for all submodular functions. As follows from the lower bounds in (Feldman et
al., 2013) both of these algorithms are close to optimal. We also give
applications for proper learning, testing and agnostic learning with value
queries of these classes.
Nonparametric Relational Topic Models through Dependent Gamma Processes
Authors: Junyu Xuan, Jie Lu, Guangquan Zhang, Richard Yi Da Xu, Xiangfeng Luo
Date: 2015-03-30 00:00:00
Tasks: Clustering, Link Prediction, Topic Models

Traditional Relational Topic Models provide a way to discover the hidden
topics from a document network. Many theoretical and practical tasks, such as
dimensional reduction, document clustering, link prediction, benefit from this
revealed knowledge. However, existing relational topic models are based on an
assumption that the number of hidden topics is known in advance, and this is
impractical in many real-world applications. Therefore, in order to relax this
assumption, we propose a nonparametric relational topic model in this paper.
Instead of using fixed-dimensional probability distributions in its generative
model, we use stochastic processes. Specifically, a gamma process is assigned
to each document, which represents the topic interest of this document.
Although this method provides an elegant solution, it brings additional
challenges when mathematically modeling the inherent network structure of
typical document network, i.e., two spatially closer documents tend to have
more similar topics. Furthermore, we require that the topics are shared by all
the documents. In order to resolve these challenges, we use a subsampling
strategy to assign each document a different gamma process from the global
gamma process, and the subsampling probabilities of documents are assigned with
a Markov Random Field constraint that inherits the document network structure.
Through the designed posterior inference algorithm, we can discover the hidden
topics and its number simultaneously. Experimental results on both synthetic
and real-world network datasets demonstrate the capabilities of learning the
hidden topics and, more importantly, the number of topics.