pwc-archive/papers-with-abstracts huggingface dataset:
Evolutionary Landscape and Management of Population Diversity
Authors: Maumita Bhattacharya
Date: 2015-10-24 00:00:00
Tasks: Diversity, Management

The search ability of an Evolutionary Algorithm (EA) depends on the variation
among the individuals in the population [3, 4, 8]. Maintaining an optimal level
of diversity in the EA population is imperative to ensure that progress of the
EA search is unhindered by premature convergence to suboptimal solutions.
Clearer understanding of the concept of population diversity, in the context of
evolutionary search and premature convergence in particular, is the key to
designing efficient EAs. To this end, this paper first presents a brief
analysis of the EA population diversity issues. Next we present an
investigation on a counter-niching EA technique [4] that introduces and
maintains constructive diversity in the population. The proposed approach uses
informed genetic operations to reach promising, but unexplored or
under-explored areas of the search space, while discouraging premature local
convergence. Simulation runs on a suite of standard benchmark test functions
with Genetic Algorithm (GA) implementation shows promising results.
Higher-order MRFs based image super resolution: why not MAP?
Authors: Yunjin Chen
Date: 2014-10-27 00:00:00
Tasks: Computational Efficiency, Image Restoration, Image Super-Resolution, Super-Resolution

A trainable filter-based higher-order Markov Random Fields (MRFs) model - the
so called Fields of Experts (FoE), has proved a highly effective image prior
model for many classic image restoration problems. Generally, two options are
available to incorporate the learned FoE prior in the inference procedure: (1)
sampling-based minimum mean square error (MMSE) estimate, and (2) energy
minimization-based maximum a posteriori (MAP) estimate. This letter is devoted
to the FoE prior based single image super resolution (SR) problem, and we
suggest to make use of the MAP estimate for inference based on two facts: (I)
It is well-known that the MAP inference has a remarkable advantage of high
computational efficiency, while the sampling-based MMSE estimate is very time
consuming. (II) Practical SR experiment results demonstrate that the MAP
estimate works equally well compared to the MMSE estimate with exactly the same
FoE prior model. Moreover, it can lead to even further improvements by
incorporating our discriminatively trained FoE prior model. In summary, we hold
that for higher-order natural image prior based SR problem, it is better to
employ the MAP estimate for inference.
Image Parsing with a Wide Range of Classes and Scene-Level Context
Authors: Marian George
Date: 2015-10-24 00:00:00
Proceeding: CVPR 2015 6
Tasks: General Classification, Image Retrieval, Retrieval, Scene Parsing, Superpixels

This paper presents a nonparametric scene parsing approach that improves the
overall accuracy, as well as the coverage of foreground classes in scene
images. We first improve the label likelihood estimates at superpixels by
merging likelihood scores from different probabilistic classifiers. This boosts
the classification performance and enriches the representation of
less-represented classes. Our second contribution consists of incorporating
semantic context in the parsing process through global label costs. Our method
does not rely on image retrieval sets but rather assigns a global likelihood
estimate to each label, which is plugged into the overall energy function. We
evaluate our system on two large-scale datasets, SIFTflow and LMSun. We achieve
state-of-the-art performance on the SIFTflow dataset and near-record results on
LMSun.
On the Statistical Efficiency of $\ell_{1,p}$ Multi-Task Learning of Gaussian Graphical Models
Authors: Jean Honorio, Tommi Jaakkola, Dimitris Samaras
Date: 2012-07-18 00:00:00
Tasks: Multi-Task Learning

In this paper, we present $\ell_{1,p}$ multi-task structure learning for
Gaussian graphical models. We analyze the sufficient number of samples for the
correct recovery of the support union and edge signs. We also analyze the
necessary number of samples for any conceivable method by providing
information-theoretic lower bounds. We compare the statistical efficiency of
multi-task learning versus that of single-task learning. For experiments, we
use a block coordinate descent method that is provably convergent and generates
a sequence of positive definite solutions. We provide experimental validation
on synthetic data as well as on two publicly available real-world data sets,
including functional magnetic resonance imaging and gene expression data.
Predicting Face Recognition Performance Using Image Quality
Authors: Abhishek Dutta, Raymond Veldhuis, Luuk Spreeuwers
Date: 2015-10-24 00:00:00
Tasks: Face Recognition

This paper proposes a data driven model to predict the performance of a face
recognition system based on image quality features. We model the relationship
between image quality features (e.g. pose, illumination, etc.) and recognition
performance measures using a probability density function. To address the issue
of limited nature of practical training data inherent in most data driven
models, we have developed a Bayesian approach to model the distribution of
recognition performance measures in small regions of the quality space. Since
the model is based solely on image quality features, it can predict performance
even before the actual recognition has taken place. We evaluate the performance
predictive capabilities of the proposed model for six face recognition systems
(two commercial and four open source) operating on three independent data sets:
MultiPIE, FRGC and CAS-PEAL. Our results show that the proposed model can
accurately predict performance using an accurate and unbiased Image Quality
Assessor (IQA). Furthermore, our experiments highlight the impact of the
unaccounted quality space -- the image quality features not considered by IQA
-- in contributing to performance prediction errors.
Predicting Performance of a Face Recognition System Based on Image Quality
Authors: Abhishek Dutta
Date: 2015-10-24 00:00:00
Tasks: Face Recognition

In this dissertation, we present a generative model to capture the relation
between facial image quality features (like pose, illumination direction, etc)
and face recognition performance. Such a model can be used to predict the
performance of a face recognition system. Since the model is based solely on
image quality features, performance predictions can be done even before the
actual recognition has taken place thereby facilitating many preemptive action.
A practical limitation of such a data driven generative model is the limited
nature of training data set. To address this limitation, we have developed a
Bayesian approach to model the distribution of recognition performance measure
based on the number of match and non-match scores in small regions of the image
quality space. Random samples drawn from these models provide the initial data
essential for training the generative model. Experiment results based on six
face recognition systems operating on three independent data sets show that the
proposed performance prediction model can accurately predict face recognition
performance using an accurate and unbiased Image Quality Assessor (IQA).
Furthermore, our results show that variability in the unaccounted quality space
-- the image quality features not considered by the IQA -- is the major factor
causing inaccuracies in predicted performance.
Combine CRF and MMSEG to Boost Chinese Word Segmentation in Social Media
Authors: Yao Yushi, Huang Zheng
Date: 2015-10-24 00:00:00
Tasks: Chinese Word Segmentation, Segmentation

In this paper, we propose a joint algorithm for the word segmentation on
Chinese social media. Previous work mainly focus on word segmentation for plain
Chinese text, in order to develop a Chinese social media processing tool, we
need to take the main features of social media into account, whose grammatical
structure is not rigorous, and the tendency of using colloquial and Internet
terms makes the existing Chinese-processing tools inefficient to obtain good
performance on social media.
  In our approach, we combine CRF and MMSEG algorithm and extend features of
traditional CRF algorithm to train the model for word segmentation, We use
Internet lexicon in order to improve the performance of our model on Chinese
social media. Our experimental result on Sina Weibo shows that our approach
outperforms the state-of-the-art model.
Unsupervised Incremental Learning and Prediction of Music Signals
Authors: Ricard Marxer, Hendrik Purwins
Date: 2015-02-02 00:00:00
Tasks: Clustering, Incremental Learning, Onset Detection, Prediction

A system is presented that segments, clusters and predicts musical audio in
an unsupervised manner, adjusting the number of (timbre) clusters
instantaneously to the audio input. A sequence learning algorithm adapts its
structure to a dynamically changing clustering tree. The flow of the system is
as follows: 1) segmentation by onset detection, 2) timbre representation of
each segment by Mel frequency cepstrum coefficients, 3) discretization by
incremental clustering, yielding a tree of different sound classes (e.g.
instruments) that can grow or shrink on the fly driven by the instantaneous
sound events, resulting in a discrete symbol sequence, 4) extraction of
statistical regularities of the symbol sequence, using hierarchical N-grams and
the newly introduced conceptual Boltzmann machine, and 5) prediction of the
next sound event in the sequence. The system's robustness is assessed with
respect to complexity and noisiness of the signal. Clustering in isolation
yields an adjusted Rand index (ARI) of 82.7% / 85.7% for data sets of singing
voice and drums. Onset detection jointly with clustering achieve an ARI of
81.3% / 76.3% and the prediction of the entire system yields an ARI of 27.2% /
39.2%.
Objects2action: Classifying and localizing actions without any video example
Authors: Mihir Jain, Jan C. van Gemert, Thomas Mensink, Cees G. M. Snoek
Date: 2015-10-23 00:00:00
Proceeding: ICCV 2015 12
Tasks: Attribute, Object, Temporal Localization, Zero-Shot Action Recognition

The goal of this paper is to recognize actions in video without the need for
examples. Different from traditional zero-shot approaches we do not demand the
design and specification of attribute classifiers and class-to-attribute
mappings to allow for transfer from seen classes to unseen classes. Our key
contribution is objects2action, a semantic word embedding that is spanned by a
skip-gram model of thousands of object categories. Action labels are assigned
to an object encoding of unseen video based on a convex combination of action
and object affinities. Our semantic embedding has three main characteristics to
accommodate for the specifics of actions. First, we propose a mechanism to
exploit multiple-word descriptions of actions and objects. Second, we
incorporate the automated selection of the most responsive objects per action.
And finally, we demonstrate how to extend our zero-shot approach to the
spatio-temporal localization of actions in video. Experiments on four action
datasets demonstrate the potential of our approach.
Semi-Automatic Segmentation of Autosomal Dominant Polycystic Kidneys using Random Forests
Authors: Kanishka Sharma, Loic Peter, Christian Rupprecht, Anna Caroli, Lichao Wang, Andrea Remuzzi, Maximilian Baust, Nassir Navab
Date: 2015-10-23 00:00:00
Tasks: Computed Tomography (CT), Segmentation

This paper presents a method for 3D segmentation of kidneys from patients
with autosomal dominant polycystic kidney disease (ADPKD) and severe renal
insufficiency, using computed tomography (CT) data. ADPKD severely alters the
shape of the kidneys due to non-uniform formation of cysts. As a consequence,
fully automatic segmentation of such kidneys is very challenging. We present a
segmentation method with minimal user interaction based on a random forest
classifier. One of the major novelties of the proposed approach is the usage of
geodesic distance volumes as additional source of information. These volumes
contain the intensity weighted distance to a manual outline of the respective
kidney in only one slice (for each kidney) of the CT volume. We evaluate our
method qualitatively and quantitatively on 55 CT acquisitions using ground
truth annotations from clinical experts.
Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm
Authors: Canyi Lu, Jinhui Tang, Shuicheng Yan, Zhouchen Lin
Date: 2015-10-23 00:00:00
Tasks: Compressive Sensing

The nuclear norm is widely used as a convex surrogate of the rank function in
compressive sensing for low rank matrix recovery with its applications in image
recovery and signal processing. However, solving the nuclear norm based relaxed
convex problem usually leads to a suboptimal solution of the original rank
minimization problem. In this paper, we propose to perform a family of
nonconvex surrogates of $L_0$-norm on the singular values of a matrix to
approximate the rank function. This leads to a nonconvex nonsmooth minimization
problem. Then we propose to solve the problem by Iteratively Reweighted Nuclear
Norm (IRNN) algorithm. IRNN iteratively solves a Weighted Singular Value
Thresholding (WSVT) problem, which has a closed form solution due to the
special properties of the nonconvex surrogate functions. We also extend IRNN to
solve the nonconvex problem with two or more blocks of variables. In theory, we
prove that IRNN decreases the objective function value monotonically, and any
limit point is a stationary point. Extensive experiments on both synthesized
data and real images demonstrate that IRNN enhances the low-rank matrix
recovery compared with state-of-the-art convex algorithms.
Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks
Authors: Minhyung Cho, Chandra Shekhar Dhir, Jaehyung Lee
Date: 2015-09-11 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: General Classification, Handwriting Recognition

Multidimensional recurrent neural networks (MDRNNs) have shown a remarkable
performance in the area of speech and handwriting recognition. The performance
of an MDRNN is improved by further increasing its depth, and the difficulty of
learning the deeper network is overcome by using Hessian-free (HF)
optimization. Given that connectionist temporal classification (CTC) is
utilized as an objective of learning an MDRNN for sequence labeling, the
non-convexity of CTC poses a problem when applying HF to the network. As a
solution, a convex approximation of CTC is formulated and its relationship with
the EM algorithm and the Fisher information matrix is discussed. An MDRNN up to
a depth of 15 layers is successfully trained using HF, resulting in an improved
performance for sequence labeling.
Fast Latent Variable Models for Inference and Visualization on Mobile Devices
Authors: Joseph W Robinson, Aaron Q. Li
Date: 2015-10-23 00:00:00

In this project we outline Vedalia, a high performance distributed network
for performing inference on latent variable models in the context of Amazon
review visualization. We introduce a new model, RLDA, which extends Latent
Dirichlet Allocation (LDA) [Blei et al., 2003] for the review space by
incorporating auxiliary data available in online reviews to improve modeling
while simultaneously remaining compatible with pre-existing fast sampling
techniques such as [Yao et al., 2009; Li et al., 2014a] to achieve high
performance. The network is designed such that computation is efficiently
offloaded to the client devices using the Chital system [Robinson & Li, 2015],
improving response times and reducing server costs. The resulting system is
able to rapidly compute a large number of specialized latent variable models
while requiring minimal server resources.
Learning in the Rational Speech Acts Model
Authors: Will Monroe, Christopher Potts
Date: 2015-10-23 00:00:00
Tasks: model, Text Generation

The Rational Speech Acts (RSA) model treats language use as a recursive
process in which probabilistic speaker and listener agents reason about each
other's intentions to enrich the literal semantics of their language along
broadly Gricean lines. RSA has been shown to capture many kinds of
conversational implicature, but it has been criticized as an unrealistic model
of speakers, and it has so far required the manual specification of a semantic
lexicon, preventing its use in natural language processing applications that
learn lexical knowledge from data. We address these concerns by showing how to
define and optimize a trained statistical classifier that uses the intermediate
agents of RSA as hidden layers of representation forming a non-linear
activation function. This treatment opens up new application domains and new
possibilities for learning effectively from data. We validate the model on a
referential expression generation task, showing that the best performance is
achieved by incorporating features approximating well-established insights
about natural language generation into RSA.
Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to MRI
Authors: Saiprasad Ravishankar, Yoram Bresler
Date: 2015-01-13 00:00:00
Tasks: compressed sensing, Image Reconstruction

Natural signals and images are well-known to be approximately sparse in
transform domains such as Wavelets and DCT. This property has been heavily
exploited in various applications in image processing and medical imaging.
Compressed sensing exploits the sparsity of images or image patches in a
transform domain or synthesis dictionary to reconstruct images from
undersampled measurements. In this work, we focus on blind compressed sensing,
where the underlying sparsifying transform is a priori unknown, and propose a
framework to simultaneously reconstruct the underlying image as well as the
sparsifying transform from highly undersampled measurements. The proposed block
coordinate descent type algorithms involve highly efficient optimal updates.
Importantly, we prove that although the proposed blind compressed sensing
formulations are highly nonconvex, our algorithms are globally convergent
(i.e., they converge from any initialization) to the set of critical points of
the objectives defining the formulations. These critical points are guaranteed
to be at least partial global and partial local minimizers. The exact point(s)
of convergence may depend on initialization. We illustrate the usefulness of
the proposed framework for magnetic resonance image reconstruction from highly
undersampled k-space measurements. As compared to previous methods involving
the synthesis dictionary model, our approach is much faster, while also
providing promising reconstruction quality.
Adaptive Mixtures of Factor Analyzers
Authors: Heysem Kaya, Albert Ali Salah
Date: 2015-07-10 00:00:00
Tasks: Clustering, Dimensionality Reduction, Model Selection

A mixture of factor analyzers is a semi-parametric density estimator that
generalizes the well-known mixtures of Gaussians model by allowing each
Gaussian in the mixture to be represented in a different lower-dimensional
manifold. This paper presents a robust and parsimonious model selection
algorithm for training a mixture of factor analyzers, carrying out simultaneous
clustering and locally linear, globally nonlinear dimensionality reduction.
Permitting different number of factors per mixture component, the algorithm
adapts the model complexity to the data complexity. We compare the proposed
algorithm with related automatic model selection algorithms on a number of
benchmarks. The results indicate the effectiveness of this fast and robust
approach in clustering, manifold learning and class-conditional modeling.
ZNN - A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-Core and Many-Core Shared Memory Machines
Authors: Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung
Date: 2015-10-22 00:00:00
Tasks: Benchmarking, CPU, GPU

Convolutional networks (ConvNets) have become a popular approach to computer
vision. It is important to accelerate ConvNet training, which is
computationally costly. We propose a novel parallel algorithm based on
decomposition into a set of tasks, most of which are convolutions or FFTs.
Applying Brent's theorem to the task dependency graph implies that linear
speedup with the number of processors is attainable within the PRAM model of
parallel computation, for wide network architectures. To attain such
performance on real shared-memory machines, our algorithm computes convolutions
converging on the same node of the network with temporal locality to reduce
cache misses, and sums the convergent convolution outputs via an almost
wait-free concurrent method to reduce time spent in critical sections. We
implement the algorithm with a publicly available software package called ZNN.
Benchmarking with multi-core CPUs shows that ZNN can attain speedup roughly
equal to the number of physical cores. We also show that ZNN can attain over
90x speedup on a many-core CPU (Xeon Phi Knights Corner). These speedups are
achieved for network architectures with widths that are in common use. The task
parallelism of the ZNN algorithm is suited to CPUs, while the SIMD parallelism
of previous algorithms is compatible with GPUs. Through examples, we show that
ZNN can be either faster or slower than certain GPU implementations depending
on specifics of the network architecture, kernel sizes, and density and size of
the output patch. ZNN may be less costly to develop and maintain, due to the
relative ease of general-purpose CPU programming.
Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization?
Authors: Chenxin Ma, Martin Takáč
Date: 2015-10-22 00:00:00
Tasks: Distributed Optimization

In this paper we study the effect of the way that the data is partitioned in
distributed optimization. The original DiSCO algorithm [Communication-Efficient
Distributed Optimization of Self-Concordant Empirical Loss, Yuchen Zhang and
Lin Xiao, 2015] partitions the input data based on samples. We describe how the
original algorithm has to be modified to allow partitioning on features and
show its efficiency both in theory and also in practice.
Efficient Unsupervised Temporal Segmentation of Motion Data
Authors: Björn Krüger, Anna Vögele, Tobias Willig, Angela Yao, Reinhard Klein, Andreas Weber
Date: 2015-10-22 00:00:00
Tasks: Clustering, Markerless Motion Capture, Segmentation

We introduce a method for automated temporal segmentation of human motion
data into distinct actions and compositing motion primitives based on
self-similar structures in the motion sequence. We use neighbourhood graphs for
the partitioning and the similarity information in the graph is further
exploited to cluster the motion primitives into larger entities of semantic
significance. The method requires no assumptions about the motion sequences at
hand and no user interaction is required for the segmentation or clustering. In
addition, we introduce a feature bundling preprocessing technique to make the
segmentation more robust to noise, as well as a notion of motion symmetry for
more refined primitive detection. We test our method on several sensor
modalities, including markered and markerless motion capture as well as on
electromyograph and accelerometer recordings. The results highlight our
system's capabilities for both segmentation and for analysis of the finer
structures of motion data, all in a completely unsupervised manner.