pwc-archive/papers-with-abstracts huggingface dataset:
Fast estimation of approximate matrix ranks using spectral densities
Authors: Shashanka Ubaru, Yousef Saad, Abd-Krim Seghouane
Date: 2016-08-19 00:00:00

In many machine learning and data related applications, it is required to
have the knowledge of approximate ranks of large data matrices at hand. In this
paper, we present two computationally inexpensive techniques to estimate the
approximate ranks of such large matrices. These techniques exploit approximate
spectral densities, popular in physics, which are probability density
distributions that measure the likelihood of finding eigenvalues of the matrix
at a given point on the real line. Integrating the spectral density over an
interval gives the eigenvalue count of the matrix in that interval. Therefore
the rank can be approximated by integrating the spectral density over a
carefully selected interval. Two different approaches are discussed to estimate
the approximate rank, one based on Chebyshev polynomials and the other based on
the Lanczos algorithm. In order to obtain the appropriate interval, it is
necessary to locate a gap between the eigenvalues that correspond to noise and
the relevant eigenvalues that contribute to the matrix rank. A method for
locating this gap and selecting the interval of integration is proposed based
on the plot of the spectral density. Numerical experiments illustrate the
performance of these techniques on matrices from typical applications.
Solving a Mixture of Many Random Linear Equations by Tensor Decomposition and Alternating Minimization
Authors: Xinyang Yi, Constantine Caramanis, Sujay Sanghavi
Date: 2016-08-19 00:00:00
Tasks: Tensor Decomposition

We consider the problem of solving mixed random linear equations with $k$
components. This is the noiseless setting of mixed linear regression. The goal
is to estimate multiple linear models from mixed samples in the case where the
labels (which sample corresponds to which model) are not observed. We give a
tractable algorithm for the mixed linear equation problem, and show that under
some technical conditions, our algorithm is guaranteed to solve the problem
exactly with sample complexity linear in the dimension, and polynomial in $k$,
the number of components. Previous approaches have required either exponential
dependence on $k$, or super-linear dependence on the dimension. The proposed
algorithm is a combination of tensor decomposition and alternating
minimization. Our analysis involves proving that the initialization provided by
the tensor method allows alternating minimization, which is equivalent to EM in
our setting, to converge to the global optimum at a linear rate.
Space-Filling Curves as a Novel Crystal Structure Representation for Machine Learning Models
Authors: Dipti Jasrasaria, Edward O. Pyzer-Knapp, Dmitrij Rappoport, Alan Aspuru-Guzik
Date: 2016-08-19 00:00:00
Tasks: BIG-bench Machine Learning

A fundamental problem in applying machine learning techniques for chemical
problems is to find suitable representations for molecular and crystal
structures. While the structure representations based on atom connectivities
are prevalent for molecules, two-dimensional descriptors are not suitable for
describing molecular crystals. In this work, we introduce the SFC-M family of
feature representations, which are based on Morton space-filling curves, as an
alternative means of representing crystal structures. Latent Semantic Indexing
(LSI) was employed in a novel setting to reduce sparsity of feature
representations. The quality of the SFC-M representations were assessed by
using them in combination with artificial neural networks to predict Density
Functional Theory (DFT) single point, Ewald summed, lattice, and many-body
dispersion energies of 839 organic molecular crystal unit cells from the
Cambridge Structural Database that consist of the elements C, H, N, and O.
Promising initial results suggest that the SFC-M representations merit further
exploration to improve its ability to predict solid-state properties of organic
crystal structures
Fundamental principles of cortical computation: unsupervised learning with prediction, compression and feedback
Authors: Micah Richert, Dimitry Fisher, Filip Piekniewski, Eugene M. Izhikevich, Todd L. Hylton
Date: 2016-08-19 00:00:00
Tasks: Visual Tracking

There has been great progress in understanding of anatomical and functional
microcircuitry of the primate cortex. However, the fundamental principles of
cortical computation - the principles that allow the visual cortex to bind
retinal spikes into representations of objects, scenes and scenarios - have so
far remained elusive. In an attempt to come closer to understanding the
fundamental principles of cortical computation, here we present a functional,
phenomenological model of the primate visual cortex. The core part of the model
describes four hierarchical cortical areas with feedforward, lateral, and
recurrent connections. The three main principles implemented in the model are
information compression, unsupervised learning by prediction, and use of
lateral and top-down context. We show that the model reproduces key aspects of
the primate ventral stream of visual processing including Simple and Complex
cells in V1, increasingly complicated feature encoding, and increased
separability of object representations in higher cortical areas. The model
learns representations of the visual environment that allow for accurate
classification and state-of-the-art visual tracking performance on novel
objects.
Pilot Testing an Artificial Intelligence Algorithm That Selects Homeless Youth Peer Leaders Who Promote HIV Testing
Authors: Eric Rice, Robin Petering, Jaih Craddock, Amanda Yoshioka-Maxwell, Amulya Yadav, Milind Tambe
Date: 2016-08-19 00:00:00

Objective. To pilot test an artificial intelligence (AI) algorithm that
selects peer change agents (PCA) to disseminate HIV testing messaging in a
population of homeless youth. Methods. We recruited and assessed 62 youth at
baseline, 1 month (n = 48), and 3 months (n = 38). A Facebook app collected
preliminary social network data. Eleven PCAs selected by AI attended a 1-day
training and 7 weekly booster sessions. Mixed-effects models with random
effects were used to assess change over time. Results. Significant change over
time was observed in past 6-month HIV testing (57.9%, 82.4%, 76.3%; p < .05)
but not condom use (63.9%, 65.7%, 65.8%). Most youth reported speaking to a PCA
about HIV prevention (72.0% at 1 month, 61.5% at 3 months). Conclusions. AI is
a promising avenue for implementing PCA models for homeless youth. Increasing
rates of regular HIV testing is critical to HIV prevention and linking homeless
youth to treatment.
Automated Selection of Uniform Regions for CT Image Quality Detection
Authors: Maitham D Naeemi, Adam M Alessio, Sohini Roychowdhury
Date: 2016-08-13 00:00:00
Tasks: Diagnostic

CT images are widely used in pathology detection and follow-up treatment
procedures. Accurate identification of pathological features requires
diagnostic quality CT images with minimal noise and artifact variation. In this
work, a novel Fourier-transform based metric for image quality (IQ) estimation
is presented that correlates to additive CT image noise. In the proposed
method, two windowed CT image subset regions are analyzed together to identify
the extent of variation in the corresponding Fourier-domain spectrum. The two
square windows are chosen such that their center pixels coincide and one window
is a subset of the other. The Fourier-domain spectral difference between these
two sub-sampled windows is then used to isolate spatial regions-of-interest
(ROI) with low signal variation (ROI-LV) and high signal variation (ROI-HV),
respectively. Finally, the spatial variance ($var$), standard deviation
($std$), coefficient of variance ($cov$) and the fraction of abdominal ROI
pixels in ROI-LV ($\nu'(q)$), are analyzed with respect to CT image noise. For
the phantom CT images, $var$ and $std$ correlate to CT image noise ($|r|>0.76$
($p\ll0.001$)), though not as well as $\nu'(q)$ ($r=0.96$ ($p\ll0.001$)).
However, for the combined phantom and patient CT images, $var$ and $std$ do not
correlate well with CT image noise ($|r|<0.46$ ($p\ll0.001$)) as compared to
$\nu'(q)$ ($r=0.95$ ($p\ll0.001$)). Thus, the proposed method and the metric,
$\nu'(q)$, can be useful to quantitatively estimate CT image noise.
The languages of actions, formal grammars and qualitive modeling of companies
Authors: Vladislav B Kovchegov
Date: 2016-08-19 00:00:00

In this paper we discuss methods of using the language of actions, formal
languages, and grammars for qualitative conceptual linguistic modeling of
companies as technological and human institutions. The main problem following
the discussion is the problem to find and describe a language structure for
external and internal flow of information of companies. We anticipate that the
language structure of external and internal base flows determine the structure
of companies. In the structure modeling of an abstract industrial company an
internal base flow of information is constructed as certain flow of words
composed on the theoretical parts-processes-actions language. The language of
procedures is found for an external base flow of information for an insurance
company. The formal stochastic grammar for the language of procedures is found
by statistical methods and is used in understanding the tendencies of the
health care industry. We present the model of human communications as a random
walk on the semantic tree
Detecting Vanishing Points using Global Image Context in a Non-Manhattan World
Authors: Menghua Zhai, Scott Workman, Nathan Jacobs
Date: 2016-08-19 00:00:00
Proceeding: CVPR 2016 6
Tasks: Horizon Line Estimation

We propose a novel method for detecting horizontal vanishing points and the
zenith vanishing point in man-made environments. The dominant trend in existing
methods is to first find candidate vanishing points, then remove outliers by
enforcing mutual orthogonality. Our method reverses this process: we propose a
set of horizon line candidates and score each based on the vanishing points it
contains. A key element of our approach is the use of global image context,
extracted with a deep convolutional network, to constrain the set of candidates
under consideration. Our method does not make a Manhattan-world assumption and
can operate effectively on scenes with only a single horizontal vanishing
point. We evaluate our approach on three benchmark datasets and achieve
state-of-the-art performance on each. In addition, our approach is
significantly faster than the previous best method.
Operator-Valued Bochner Theorem, Fourier Feature Maps for Operator-Valued Kernels, and Vector-Valued Learning
Authors: Ha Quang Minh
Date: 2016-08-19 00:00:00

This paper presents a framework for computing random operator-valued feature
maps for operator-valued positive definite kernels. This is a generalization of
the random Fourier features for scalar-valued kernels to the operator-valued
case. Our general setting is that of operator-valued kernels corresponding to
RKHS of functions with values in a Hilbert space. We show that in general, for
a given kernel, there are potentially infinitely many random feature maps,
which can be bounded or unbounded. Most importantly, given a kernel, we present
a general, closed form formula for computing a corresponding probability
measure, which is required for the construction of the Fourier features, and
which, unlike the scalar case, is not uniquely and automatically determined by
the kernel. We also show that, under appropriate conditions, random bounded
feature maps can always be computed. Furthermore, we show the uniform
convergence, under the Hilbert-Schmidt norm, of the resulting approximate
kernel to the exact kernel on any compact subset of Euclidean space. Our
convergence requires differentiable kernels, an improvement over the
twice-differentiability requirement in previous work in the scalar setting. We
then show how operator-valued feature maps and their approximations can be
employed in a general vector-valued learning framework. The mathematical
formulation is illustrated by numerical examples on matrix-valued kernels.
Implementing a Relevance Tracker Module
Authors: Joachim Jansen, Jo Devriendt, Bart Bogaerts, Gerda Janssens, Marc Denecker
Date: 2016-08-19 00:00:00

PC(ID) extends propositional logic with inductive definitions: rule sets
under the well-founded semantics. Recently, a notion of relevance was
introduced for this language. This notion determines the set of undecided
literals that can still influence the satisfiability of a PC(ID) formula in a
given partial assignment. The idea is that the PC(ID) solver can make decisions
only on relevant literals without losing soundness and thus safely ignore
irrelevant literals.
  One important insight that the relevance of a literal is completely
determined by the current solver state. During search, the solver state changes
have an effect on the relevance of literals. In this paper, we discuss an
incremental, lightweight implementation of a relevance tracker module that can
be added to and interact with an out-of-the-box SAT(ID) solver.
Pixel Recurrent Neural Networks
Authors: Aaron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu
Date: 2016-01-25 00:00:00
Tasks: Image Generation

Modeling the distribution of natural images is a landmark problem in
unsupervised learning. This task requires an image model that is at once
expressive, tractable and scalable. We present a deep neural network that
sequentially predicts the pixels in an image along the two spatial dimensions.
Our method models the discrete probability of the raw pixel values and encodes
the complete set of dependencies in the image. Architectural novelties include
fast two-dimensional recurrent layers and an effective use of residual
connections in deep recurrent networks. We achieve log-likelihood scores on
natural images that are considerably better than the previous state of the art.
Our main results also provide benchmarks on the diverse ImageNet dataset.
Samples generated from the model appear crisp, varied and globally coherent.
Using Distributed Representations to Disambiguate Biomedical and Clinical Concepts
Authors: Stéphan Tulkens, Simon Šuster, Walter Daelemans
Date: 2016-08-19 00:00:00
Proceeding: WS 2016 8
Tasks: Word Sense Disambiguation

In this paper, we report a knowledge-based method for Word Sense
Disambiguation in the domains of biomedical and clinical text. We combine word
representations created on large corpora with a small number of definitions
from the UMLS to create concept representations, which we then compare to
representations of the context of ambiguous terms. Using no relational
information, we obtain comparable performance to previous approaches on the
MSH-WSD dataset, which is a well-known dataset in the biomedical domain.
Additionally, our method is fast and easy to set up and extend to other
domains. Supplementary materials, including source code, can be found at https:
//github.com/clips/yarn
Fast k-NN search
Authors: Ville Hyvönen, Teemu Pitkänen, Sotiris Tasoulis, Elias Jääsaari, Risto Tuomainen, Liang Wang, Jukka Corander, Teemu Roos
Date: 2015-09-23 00:00:00
Tasks: Recommendation Systems

Efficient index structures for fast approximate nearest neighbor queries are
required in many applications such as recommendation systems. In
high-dimensional spaces, many conventional methods suffer from excessive usage
of memory and slow response times. We propose a method where multiple random
projection trees are combined by a novel voting scheme. The key idea is to
exploit the redundancy in a large number of candidate sets obtained by
independently generated random projections in order to reduce the number of
expensive exact distance evaluations. The method is straightforward to
implement using sparse projections which leads to a reduced memory footprint
and fast index construction. Furthermore, it enables grouping of the required
computations into big matrix multiplications, which leads to additional savings
due to cache effects and low-level parallelization. We demonstrate by extensive
experiments on a wide variety of data sets that the method is faster than
existing partitioning tree or hashing based approaches, making it the fastest
available technique on high accuracy levels.
Learning Spatially Regularized Correlation Filters for Visual Tracking
Authors: Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan, Michael Felsberg
Date: 2016-08-19 00:00:00
Proceeding: ICCV 2015 12
Tasks: Video Object Tracking, Visual Tracking

Robust and accurate visual tracking is one of the most challenging computer
vision problems. Due to the inherent lack of training data, a robust approach
for constructing a target appearance model is crucial. Recently,
discriminatively learned correlation filters (DCF) have been successfully
applied to address this problem for tracking. These methods utilize a periodic
assumption of the training samples to efficiently learn a classifier on all
patches in the target neighborhood. However, the periodic assumption also
introduces unwanted boundary effects, which severely degrade the quality of the
tracking model.
  We propose Spatially Regularized Discriminative Correlation Filters (SRDCF)
for tracking. A spatial regularization component is introduced in the learning
to penalize correlation filter coefficients depending on their spatial
location. Our SRDCF formulation allows the correlation filters to be learned on
a significantly larger set of negative training samples, without corrupting the
positive samples. We further propose an optimization strategy, based on the
iterative Gauss-Seidel method, for efficient online learning of our SRDCF.
Experiments are performed on four benchmark datasets: OTB-2013, ALOV++,
OTB-2015, and VOT2014. Our approach achieves state-of-the-art results on all
four datasets. On OTB-2013 and OTB-2015, we obtain an absolute gain of 8.0% and
8.2% respectively, in mean overlap precision, compared to the best existing
trackers.
Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields
Authors: Roque Porchetto, Franco Stramana, Nikos Paragios, Enzo Ferrante
Date: 2016-08-19 00:00:00
Tasks: Image Registration, Medical Image Registration

Rigid slice-to-volume registration is a challenging task, which finds
application in medical imaging problems like image fusion for image guided
surgeries and motion correction for volume reconstruction. It is usually
formulated as an optimization problem and solved using standard continuous
methods. In this paper, we discuss how this task be formulated as a discrete
labeling problem on a graph. Inspired by previous works on discrete estimation
of linear transformations using Markov Random Fields (MRFs), we model it using
a pairwise MRF, where the nodes are associated to the rigid parameters, and the
edges encode the relation between the variables. We compare the performance of
the proposed method to a continuous formulation optimized using simplex, and we
discuss how it can be used to further improve the accuracy of our approach.
Promising results are obtained using a monomodal dataset composed of magnetic
resonance images (MRI) of a beating heart.
Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering
Authors: Yang Wang, Wenjie Zhang, Lin Wu, Xuemin Lin, Meng Fang, Shirui Pan
Date: 2016-08-19 00:00:00
Tasks: Clustering

Multi-view spectral clustering, which aims at yielding an agreement or
consensus data objects grouping across multi-views with their graph laplacian
matrices, is a fundamental clustering problem. Among the existing methods,
Low-Rank Representation (LRR) based method is quite superior in terms of its
effectiveness, intuitiveness and robustness to noise corruptions. However, it
aggressively tries to learn a common low-dimensional subspace for multi-view
data, while inattentively ignoring the local manifold structure in each view,
which is critically important to the spectral clustering; worse still, the
low-rank minimization is enforced to achieve the data correlation consensus
among all views, failing to flexibly preserve the local manifold structure for
each view. In this paper, 1) we propose a multi-graph laplacian regularized LRR
with each graph laplacian corresponding to one view to characterize its local
manifold structure. 2) Instead of directly enforcing the low-rank minimization
among all views for correlation consensus, we separately impose low-rank
constraint on each view, coupled with a mutual structural consensus constraint,
where it is able to not only well preserve the local manifold structure but
also serve as a constraint for that from other views, which iteratively makes
the views more agreeable. Extensive experiments on real-world multi-view data
sets demonstrate its superiority.
Learning to Start for Sequence to Sequence Architecture
Authors: Qingfu Zhu, Wei-Nan Zhang, Lianqiang Zhou, Ting Liu
Date: 2016-08-19 00:00:00
Tasks: Decoder, Machine Translation, Response Generation, Sentence, Short-Text Conversation, Translation

The sequence to sequence architecture is widely used in the response
generation and neural machine translation to model the potential relationship
between two sentences. It typically consists of two parts: an encoder that
reads from the source sentence and a decoder that generates the target sentence
word by word according to the encoder's output and the last generated word.
However, it faces to the cold start problem when generating the first word as
there is no previous word to refer. Existing work mainly use a special start
symbol 
to generate the first word. An obvious drawback of these work is
that there is not a learnable relationship between words and the start symbol.
Furthermore, it may lead to the error accumulation for decoding when the first
word is incorrectly generated. In this paper, we proposed a novel approach to
learning to generate the first word in the sequence to sequence architecture
rather than using the start symbol. Experimental results on the task of
response generation of short text conversation show that the proposed approach
outperforms the state-of-the-art approach in both of the automatic and manual
evaluations.
String and Membrane Gaussian Processes
Authors: Yves-Laurent Kom Samo, Stephen Roberts
Date: 2015-07-24 00:00:00
Tasks: Bayesian Inference, Gaussian Processes

In this paper we introduce a novel framework for making exact nonparametric
Bayesian inference on latent functions, that is particularly suitable for Big
Data tasks. Firstly, we introduce a class of stochastic processes we refer to
as string Gaussian processes (string GPs), which are not to be mistaken for
Gaussian processes operating on text. We construct string GPs so that their
finite-dimensional marginals exhibit suitable local conditional independence
structures, which allow for scalable, distributed, and flexible nonparametric
Bayesian inference, without resorting to approximations, and while ensuring
some mild global regularity constraints. Furthermore, string GP priors
naturally cope with heterogeneous input data, and the gradient of the learned
latent function is readily available for explanatory analysis. Secondly, we
provide some theoretical results relating our approach to the standard GP
paradigm. In particular, we prove that some string GPs are Gaussian processes,
which provides a complementary global perspective on our framework. Finally, we
derive a scalable and distributed MCMC scheme for supervised learning tasks
under string GP priors. The proposed MCMC scheme has computational time
complexity $\mathcal{O}(N)$ and memory requirement $\mathcal{O}(dN)$, where $N$
is the data size and $d$ the dimension of the input space. We illustrate the
efficacy of the proposed approach on several synthetic and real-world datasets,
including a dataset with $6$ millions input points and $8$ attributes.