pwc-archive/papers-with-abstracts huggingface dataset:
On Graphical Models via Univariate Exponential Family Distributions
Authors: Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu
Date: 2013-01-17 00:00:00

Undirected graphical models, or Markov networks, are a popular class of
statistical models, used in a wide variety of applications. Popular instances
of this class include Gaussian graphical models and Ising models. In many
settings, however, it might not be clear which subclass of graphical models to
use, particularly for non-Gaussian and non-categorical data. In this paper, we
consider a general sub-class of graphical models where the node-wise
conditional distributions arise from exponential families. This allows us to
derive multivariate graphical model distributions from univariate exponential
family distributions, such as the Poisson, negative binomial, and exponential
distributions. Our key contributions include a class of M-estimators to fit
these graphical model distributions; and rigorous statistical analysis showing
that these M-estimators recover the true graphical model structure exactly,
with high probability. We provide examples of genomic and proteomic networks
learned via instances of our class of graphical models derived from Poisson and
exponential distributions.
Ascribing Consciousness to Artificial Intelligence
Authors: Murray Shanahan
Date: 2015-04-22 00:00:00

This paper critically assesses the anti-functionalist stance on consciousness
adopted by certain advocates of integrated information theory (IIT), a
corollary of which is that human-level artificial intelligence implemented on
conventional computing hardware is necessarily not conscious. The critique
draws on variations of a well-known gradual neuronal replacement thought
experiment, as well as bringing out tensions in IIT's treatment of
self-knowledge. The aim, though, is neither to reject IIT outright nor to
champion functionalism in particular. Rather, it is suggested that both ideas
have something to offer a scientific understanding of consciousness, as long as
they are not dressed up as solutions to illusory metaphysical problems. As for
human-level AI, we must await its development before we can decide whether or
not to ascribe consciousness to it.
Gravitational Clustering
Authors: Armen Aghajanyan
Date: 2015-09-05 00:00:00
Tasks: Clustering, General Classification

The downfall of many supervised learning algorithms, such as neural networks,
is the inherent need for a large amount of training data. Although there is a
lot of buzz about big data, there is still the problem of doing classification
from a small dataset. Other methods such as support vector machines, although
capable of dealing with few samples, are inherently binary classifiers, and are
in need of learning strategies such as One vs All in the case of
multi-classification. In the presence of a large number of classes this can
become problematic. In this paper we present, a novel approach to supervised
learning through the method of clustering. Unlike traditional methods such as
K-Means, Gravitational Clustering does not require the initial number of
clusters, and automatically builds the clusters, individual samples can be
arbitrarily weighted and it requires only few samples while staying resilient
to over-fitting.
Co-interest Person Detection from Multiple Wearable Camera Videos
Authors: Yuewei Lin, Kareem Ezzeldeen, Youjie Zhou, Xiaochuan Fan, Hongkai Yu, Hui Qian, Song Wang
Date: 2015-09-05 00:00:00
Proceeding: ICCV 2015 12
Tasks: Human Detection

Wearable cameras, such as Google Glass and Go Pro, enable video data
collection over larger areas and from different views. In this paper, we tackle
a new problem of locating the co-interest person (CIP), i.e., the one who draws
attention from most camera wearers, from temporally synchronized videos taken
by multiple wearable cameras. Our basic idea is to exploit the motion patterns
of people and use them to correlate the persons across different videos,
instead of performing appearance-based matching as in traditional video
co-segmentation/localization. This way, we can identify CIP even if a group of
people with similar appearance are present in the view. More specifically, we
detect a set of persons on each frame as the candidates of the CIP and then
build a Conditional Random Field (CRF) model to select the one with consistent
motion patterns in different videos and high spacial-temporal consistency in
each video. We collect three sets of wearable-camera videos for testing the
proposed algorithm. All the involved people have similar appearances in the
collected videos and the experiments demonstrate the effectiveness of the
proposed algorithm.
Stochastic gradient variational Bayes for gamma approximating distributions
Authors: David A. Knowles
Date: 2015-09-04 00:00:00
Tasks: Variational Inference

While stochastic variational inference is relatively well known for scaling
inference in Bayesian probabilistic models, related methods also offer ways to
circumnavigate the approximation of analytically intractable expectations. The
key challenge in either setting is controlling the variance of gradient
estimates: recent work has shown that for continuous latent variables,
particularly multivariate Gaussians, this can be achieved by using the gradient
of the log posterior. In this paper we apply the same idea to gamma distributed
latent variables given gamma variational distributions, enabling
straightforward "black box" variational inference in models where sparsity and
non-negativity are appropriate. We demonstrate the method on a recently
proposed gamma process model for network data, as well as a novel sparse factor
analysis. We outperform generic sampling algorithms and the approach of using
Gaussian variational distributions on transformed variables.
Chebyshev and Conjugate Gradient Filters for Graph Image Denoising
Authors: Dong Tian, Hassan Mansour, Andrew Knyazev, Anthony Vetro
Date: 2015-09-04 00:00:00
Tasks: Denoising, Image Denoising, Image Enhancement

In 3D image/video acquisition, different views are often captured with
varying noise levels across the views. In this paper, we propose a graph-based
image enhancement technique that uses a higher quality view to enhance a
degraded view. A depth map is utilized as auxiliary information to match the
perspectives of the two views. Our method performs graph-based filtering of the
noisy image by directly computing a projection of the image to be filtered onto
a lower dimensional Krylov subspace of the graph Laplacian. We discuss two
graph spectral denoising methods: first using Chebyshev polynomials, and second
using iterations of the conjugate gradient algorithm. Our framework generalizes
previously known polynomial graph filters, and we demonstrate through numerical
simulations that our proposed technique produces subjectively cleaner images
with about 1-3 dB improvement in PSNR over existing polynomial graph filters.
Semantic Video Segmentation : Exploring Inference Efficiency
Authors: Subarna Tripathi, Serge Belongie, Youngbae Hwang, Truong Nguyen
Date: 2015-09-04 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

We explore the efficiency of the CRF inference beyond image level semantic
segmentation and perform joint inference in video frames. The key idea is to
combine best of two worlds: semantic co-labeling and more expressive models.
Our formulation enables us to perform inference over ten thousand images within
seconds and makes the system amenable to perform video semantic segmentation
most effectively. On CamVid dataset, with TextonBoost unaries, our proposed
method achieves up to 8% improvement in accuracy over individual semantic image
segmentation without additional time overhead. The source code is available at
https://github.com/subtri/video_inference
Object Recognition from Short Videos for Robotic Perception
Authors: Ivan Bogun, Anelia Angelova, Navdeep Jaitly
Date: 2015-09-04 00:00:00
Tasks: Object, Object Recognition

Deep neural networks have become the primary learning technique for object
recognition. Videos, unlike still images, are temporally coherent which makes
the application of deep networks non-trivial. Here, we investigate how motion
can aid object recognition in short videos. Our approach is based on Long
Short-Term Memory (LSTM) deep networks. Unlike previous applications of LSTMs,
we implement each gate as a convolution. We show that convolutional-based LSTM
models are capable of learning motion dependencies and are able to improve the
recognition accuracy when more frames in a sequence are available. We evaluate
our approach on the Washington RGBD Object dataset and on the Washington RGBD
Scenes dataset. Our approach outperforms deep nets applied to still images and
sets a new state-of-the-art in this domain.
Regret vs. Communication: Distributed Stochastic Multi-Armed Bandits and Beyond
Authors: Shuang Liu, Cheng Chen, Zhihua Zhang
Date: 2015-04-14 00:00:00
Tasks: Multi-Armed Bandits

In this paper, we consider the distributed stochastic multi-armed bandit
problem, where a global arm set can be accessed by multiple players
independently. The players are allowed to exchange their history of
observations with each other at specific points in time. We study the
relationship between regret and communication. When the time horizon is known,
we propose the Over-Exploration strategy, which only requires one-round
communication and whose regret does not scale with the number of players. When
the time horizon is unknown, we measure the frequency of communication through
a new notion called the density of the communication set, and give an exact
characterization of the interplay between regret and communication.
Specifically, a lower bound is established and stable strategies that match the
lower bound are developed. The results and analyses in this paper are specific
but can be translated into more general settings.
Particle approximations of the score and observed information matrix for parameter estimation in state space models with linear computational cost
Authors: Christopher Nemeth, Paul Fearnhead, Lyudmila Mihaylova
Date: 2013-06-04 00:00:00
Tasks: Density Estimation, parameter estimation, State Space Models

Poyiadjis et al. (2011) show how particle methods can be used to estimate
both the score and the observed information matrix for state space models.
These methods either suffer from a computational cost that is quadratic in the
number of particles, or produce estimates whose variance increases
quadratically with the amount of data. This paper introduces an alternative
approach for estimating these terms at a computational cost that is linear in
the number of particles. The method is derived using a combination of kernel
density estimation, to avoid the particle degeneracy that causes the
quadratically increasing variance, and Rao-Blackwellisation. Crucially, we show
the method is robust to the choice of bandwidth within the kernel density
estimation, as it has good asymptotic properties regardless of this choice. Our
estimates of the score and observed information matrix can be used within both
online and batch procedures for estimating parameters for state space models.
Empirical results show improved parameter estimates compared to existing
methods at a significantly reduced computational cost. Supplementary materials
including code are available.
Conjugate Gradient Acceleration of Non-Linear Smoothing Filters
Authors: Andrew Knyazev, Alexander Malyshev
Date: 2015-09-04 00:00:00
Tasks: Denoising

The most efficient signal edge-preserving smoothing filters, e.g., for
denoising, are non-linear. Thus, their acceleration is challenging and is often
performed in practice by tuning filter parameters, such as by increasing the
width of the local smoothing neighborhood, resulting in more aggressive
smoothing of a single sweep at the cost of increased edge blurring. We propose
an alternative technology, accelerating the original filters without tuning, by
running them through a special conjugate gradient method, not affecting their
quality. The filter non-linearity is dealt with by careful freezing and
restarting. Our initial numerical experiments on toy one-dimensional signals
demonstrate 20x acceleration of the classical bilateral filter and 3-5x
acceleration of the recently developed guided filter.
Non-normal modalities in variants of Linear Logic
Authors: Daniele Porello, Nicolas Troquard
Date: 2015-03-13 00:00:00

This article presents modal versions of resource-conscious logics. We
concentrate on extensions of variants of Linear Logic with one minimal
non-normal modality. In earlier work, where we investigated agency in
multi-agent systems, we have shown that the results scale up to logics with
multiple non-minimal modalities. Here, we start with the language of
propositional intuitionistic Linear Logic without the additive disjunction, to
which we add a modality. We provide an interpretation of this language on a
class of Kripke resource models extended with a neighbourhood function: modal
Kripke resource models. We propose a Hilbert-style axiomatization and a
Gentzen-style sequent calculus. We show that the proof theories are sound and
complete with respect to the class of modal Kripke resource models. We show
that the sequent calculus admits cut elimination and that proof-search is in
PSPACE. We then show how to extend the results when non-commutative connectives
are added to the language. Finally, we put the logical framework to use by
instantiating it as logics of agency. In particular, we propose a logic to
reason about the resource-sensitive use of artefacts and illustrate it with a
variety of examples.
NoSPaM Manual - A Tool for Node-Specific Triad Pattern Mining
Authors: Marco Winkler
Date: 2015-09-04 00:00:00

The detection of triadic subgraph motifs is a common methodology in
complex-networks research. The procedure usually applied in order to detect
motifs evaluates whether a certain subgraph pattern is overrepresented in a
network as a whole. However, motifs do not necessarily appear frequently in
every region of a graph. For this reason, we recently introduced the framework
of Node-Specific Pattern Mining (NoSPaM). This work is a manual for an
implementation of NoSPaM which can be downloaded from www.mwinkler.eu.
Quantization based Fast Inner Product Search
Authors: Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha
Date: 2015-09-04 00:00:00
Tasks: Quantization

We propose a quantization based approach for fast approximate Maximum Inner
Product Search (MIPS). Each database vector is quantized in multiple subspaces
via a set of codebooks, learned directly by minimizing the inner product
quantization error. Then, the inner product of a query to a database vector is
approximated as the sum of inner products with the subspace quantizers.
Different from recently proposed LSH approaches to MIPS, the database vectors
and queries do not need to be augmented in a higher dimensional feature space.
We also provide a theoretical analysis of the proposed approach, consisting of
the concentration results under mild assumptions. Furthermore, if a small
sample of example queries is given at the training time, we propose a modified
codebook learning procedure which further improves the accuracy. Experimental
results on a variety of datasets including those arising from deep neural
networks show that the proposed approach significantly outperforms the existing
state-of-the-art.
On Computing the Translations Norm in the Epipolar Graph
Authors: Federica Arrigoni, Beatrice Rossi, Andrea Fusiello
Date: 2015-03-12 00:00:00
Tasks: Translation

This paper deals with the problem of recovering the unknown norm of relative
translations between cameras based on the knowledge of relative rotations and
translation directions. We provide theoretical conditions for the solvability
of such a problem, and we propose a two-stage method to solve it. First, a
cycle basis for the epipolar graph is computed, then all the scaling factors
are recovered simultaneously by solving a homogeneous linear system. We
demonstrate the accuracy of our solution by means of synthetic and real
experiments.
Predicting SLA Violations in Real Time using Online Machine Learning
Authors: Jawwad Ahmed, Andreas Johnsson, Rerngvit Yanggratoke, John Ardelius, Christofer Flinta, Rolf Stadler
Date: 2015-09-04 00:00:00
Tasks: BIG-bench Machine Learning

Detecting faults and SLA violations in a timely manner is critical for
telecom providers, in order to avoid loss in business, revenue and reputation.
At the same time predicting SLA violations for user services in telecom
environments is difficult, due to time-varying user demands and infrastructure
load conditions.
  In this paper, we propose a service-agnostic online learning approach,
whereby the behavior of the system is learned on the fly, in order to predict
client-side SLA violations. The approach uses device-level metrics, which are
collected in a streaming fashion on the server side.
  Our results show that the approach can produce highly accurate predictions
(>90% classification accuracy and < 10% false alarm rate) in scenarios where
SLA violations are predicted for a video-on-demand service under changing load
patterns. The paper also highlight the limitations of traditional offline
learning methods, which perform significantly worse in many of the considered
scenarios.
Ontology Based SMS Controller for Smart Phones
Authors: Mohammed A. Balubaid, Umar Manzoor
Date: 2015-09-04 00:00:00
Tasks: Lexical Analysis, Marketing

Text analysis includes lexical analysis of the text and has been widely
studied and used in diverse applications. In the last decade, researchers have
proposed many efficient solutions to analyze / classify large text dataset,
however, analysis / classification of short text is still a challenge because
1) the data is very sparse 2) It contains noise words and 3) It is difficult to
understand the syntactical structure of the text. Short Messaging Service (SMS)
is a text messaging service for mobile/smart phone and this service is
frequently used by all mobile users. Because of the popularity of SMS service,
marketing companies nowadays are also using this service for direct marketing
also known as SMS marketing.In this paper, we have proposed Ontology based SMS
Controller which analyze the text message and classify it using ontology
aslegitimate or spam. The proposed system has been tested on different
scenarios and experimental results shows that the proposed solution is
effective both in terms of efficiency and time.
A statistical shape space model of the palate surface trained on 3D MRI scans of the vocal tract
Authors: Alexander Hewer, Ingmar Steiner, Timo Bolkart, Stefanie Wuhrer, Korin Richmond
Date: 2015-09-04 00:00:00

We describe a minimally-supervised method for computing a statistical shape
space model of the palate surface. The model is created from a corpus of
volumetric magnetic resonance imaging (MRI) scans collected from 12 speakers.
We extract a 3D mesh of the palate from each speaker, then train the model
using principal component analysis (PCA). The palate model is then tested using
3D MRI from another corpus and evaluated using a high-resolution optical scan.
We find that the error is low even when only a handful of measured coordinates
are available. In both cases, our approach yields promising results. It can be
applied to extract the palate shape from MRI data, and could be useful to other
analysis modalities, such as electromagnetic articulography (EMA) and
ultrasound tongue imaging (UTI).
CNN Based Hashing for Image Retrieval
Authors: Jinma Guo, Jianmin Li
Date: 2015-09-04 00:00:00
Tasks: Image Retrieval, Retrieval

Along with data on the web increasing dramatically, hashing is becoming more
and more popular as a method of approximate nearest neighbor search. Previous
supervised hashing methods utilized similarity/dissimilarity matrix to get
semantic information. But the matrix is not easy to construct for a new
dataset. Rather than to reconstruct the matrix, we proposed a straightforward
CNN-based hashing method, i.e. binarilizing the activations of a fully
connected layer with threshold 0 and taking the binary result as hash codes.
This method achieved the best performance on CIFAR-10 and was comparable with
the state-of-the-art on MNIST. And our experiments on CIFAR-10 suggested that
the signs of activations may carry more information than the relative values of
activations between samples, and that the co-adaption between feature extractor
and hash functions is important for hashing.
Diffusion-KLMS Algorithm and its Performance Analysis for Non-Linear Distributed Networks
Authors: Rangeet Mitra, Vimal Bhatia
Date: 2015-09-04 00:00:00
Tasks: parameter estimation

In a distributed network environment, the diffusion-least mean squares (LMS)
algorithm gives faster convergence than the original LMS algorithm. It has also
been observed that, the diffusion-LMS generally outperforms other distributed
LMS algorithms like spatial LMS and incremental LMS. However, both the original
LMS and diffusion-LMS are not applicable in non-linear environments where data
may not be linearly separable. A variant of LMS called kernel-LMS (KLMS) has
been proposed in the literature for such non-linearities. In this paper, we
propose kernelised version of diffusion-LMS for non-linear distributed
environments. Simulations show that the proposed approach has superior
convergence as compared to algorithms of the same genre. We also introduce a
technique to predict the transient and steady-state behaviour of the proposed
algorithm. The techniques proposed in this work (or algorithms of same genre)
can be easily extended to distributed parameter estimation applications like
cooperative spectrum sensing and massive multiple input multiple output (MIMO)
receiver design which are potential components for 5G communication systems.
Parallel and Distributed Approaches for Graph Based Semi-supervised Learning
Authors: Konstantin Avrachenkov, Vivek Borkar, Krishnakant Saboo
Date: 2015-09-04 00:00:00

Two approaches for graph based semi-supervised learning are proposed. The
firstapproach is based on iteration of an affine map. A key element of the
affine map iteration is sparsematrix-vector multiplication, which has several
very efficient parallel implementations. The secondapproach belongs to the
class of Markov Chain Monte Carlo (MCMC) algorithms. It is based onsampling of
nodes by performing a random walk on the graph. The latter approach is
distributedby its nature and can be easily implemented on several processors or
over the network. Boththeoretical and practical evaluations are provided. It is
found that the nodes are classified intotheir class with very small error. The
sampling algorithm's ability to track new incoming nodesand to classify them is
also demonstrated.
Deep Broad Learning - Big Models for Big Data
Authors: Nayyar A. Zaidi, Geoffrey I. Webb, Mark J. Carman, Francois Petitjean
Date: 2015-09-04 00:00:00

Deep learning has demonstrated the power of detailed modeling of complex
high-order (multivariate) interactions in data. For some learning tasks there
is power in learning models that are not only Deep but also Broad. By Broad, we
mean models that incorporate evidence from large numbers of features. This is
of especial value in applications where many different features and
combinations of features all carry small amounts of information about the
class. The most accurate models will integrate all that information. In this
paper, we propose an algorithm for Deep Broad Learning called DBL. The proposed
algorithm has a tunable parameter $n$, that specifies the depth of the model.
It provides straightforward paths towards out-of-core learning for large data.
We demonstrate that DBL learns models from large quantities of data with
accuracy that is highly competitive with the state-of-the-art.