pwc-archive/papers-with-abstracts huggingface dataset:
Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images
Authors: Moo. K. Chung, Anqi Qiu, Seongho Seo, Houri K. Vorperian
Date: 2014-09-23 00:00:00
Tasks: regression

We present a novel kernel regression framework for smoothing scalar surface
data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel
constructed from the eigenfunctions, we formulate a new bivariate kernel
regression framework as a weighted eigenfunction expansion with the heat kernel
as the weights. The new kernel regression is mathematically equivalent to
isotropic heat diffusion, kernel smoothing and recently popular diffusion
wavelets. Unlike many previous partial differential equation based approaches
involving diffusion, our approach represents the solution of diffusion
analytically, reducing numerical inaccuracy and slow convergence. The numerical
implementation is validated on a unit sphere using spherical harmonics. As an
illustration, we have applied the method in characterizing the localized growth
pattern of mandible surfaces obtained in CT images from subjects between ages 0
and 20 years by regressing the length of displacement vectors with respect to
the template surface.
Advances in Artificial Intelligence: Are you sure, we are on the right track?
Authors: Emanuel Diamant
Date: 2015-02-17 00:00:00
Tasks: Philosophy

Over the past decade, AI has made a remarkable progress. It is agreed that
this is due to the recently revived Deep Learning technology. Deep Learning
enables to process large amounts of data using simplified neuron networks that
simulate the way in which the brain works. However, there is a different point
of view, which posits that the brain is processing information, not data. This
unresolved duality hampered AI progress for years. In this paper, I propose a
notion of Integrated information that hopefully will resolve the problem. I
consider integrated information as a coupling between two separate entities -
physical information (that implies data processing) and semantic information
(that provides physical information interpretation). In this regard,
intelligence becomes a product of information processing. Extending further
this line of thinking, it can be said that information processing does not
require more a human brain for its implementation. Indeed, bacteria and amoebas
exhibit intelligent behavior without any sign of a brain. That dramatically
removes the need for AI systems to emulate the human brain complexity! The
paper tries to explore this shift in AI systems design philosophy.
Computational Curiosity (A Book Draft)
Authors: Qiong Wu
Date: 2015-02-17 00:00:00
Tasks: Recommendation Systems

This book discusses computational curiosity, from the psychology of curiosity
to the computational models of curiosity, and then showcases several
interesting applications of computational curiosity. A brief overview of the
book is given as follows. Chapter 1 discusses the underpinnings of curiosity in
human beings, including the major categories of curiosity, curiosity-related
emotions and behaviors, and the benefits of curiosity. Chapter 2 reviews the
arousal theories of curiosity in psychology and summarizes a general two-step
process model for computational curiosity. Base on the perspective of the
two-step process model, Chapter 3 reviews and analyzes some of the traditional
computational models of curiosity. Chapter 4 introduces a novel generic
computational model of curiosity, which is developed based on the arousal
theories of curiosity. After the discussion of computational models of
curiosity, we outline the important applications where computational curiosity
may bring significant impacts in Chapter 5. Chapter 6 discusses the application
of the generic computational model of curiosity in a machine learning
framework. Chapter 7 discusses the application of the generic computational
model of curiosity in a recommender system. In Chapter 8 and Chapter 9, the
generic computational model of curiosity is studied in two types of pedagogical
agents. In Chapter 8, a curious peer learner is studied. It is a non-player
character that aims to provide a believable virtual learning environment for
users. In Chapter 9, a curious learning companion is studied. It aims to
enhance users' learning experience through providing meaningful interactions
with them. Chapter 10 discusses open questions in the research field of
computation curiosity.
Learning Poisson Binomial Distributions
Authors: Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio
Date: 2011-07-13 00:00:00

We consider a basic problem in unsupervised learning: learning an unknown
\emph{Poisson Binomial Distribution}. A Poisson Binomial Distribution (PBD)
over $\{0,1,\dots,n\}$ is the distribution of a sum of $n$ independent
Bernoulli random variables which may have arbitrary, potentially non-equal,
expectations. These distributions were first studied by S. Poisson in 1837
\cite{Poisson:37} and are a natural $n$-parameter generalization of the
familiar Binomial Distribution. Surprisingly, prior to our work this basic
learning problem was poorly understood, and known results for it were far from
optimal.
  We essentially settle the complexity of the learning problem for this basic
class of distributions. As our first main result we give a highly efficient
algorithm which learns to $\eps$-accuracy (with respect to the total variation
distance) using $\tilde{O}(1/\eps^3)$ samples \emph{independent of $n$}. The
running time of the algorithm is \emph{quasilinear} in the size of its input
data, i.e., $\tilde{O}(\log(n)/\eps^3)$ bit-operations. (Observe that each draw
from the distribution is a $\log(n)$-bit string.) Our second main result is a
{\em proper} learning algorithm that learns to $\eps$-accuracy using
$\tilde{O}(1/\eps^2)$ samples, and runs in time $(1/\eps)^{\poly (\log
(1/\eps))} \cdot \log n$. This is nearly optimal, since any algorithm {for this
problem} must use $\Omega(1/\eps^2)$ samples. We also give positive and
negative results for some extensions of this learning problem to weighted sums
of independent Bernoulli random variables.
Model Selection for Topic Models via Spectral Decomposition
Authors: Dehua Cheng, Xinran He, Yan Liu
Date: 2014-10-23 00:00:00
Tasks: model, Model Selection, Tensor Decomposition, Topic Models

Topic models have achieved significant successes in analyzing large-scale
text corpus. In practical applications, we are always confronted with the
challenge of model selection, i.e., how to appropriately set the number of
topics. Following recent advances in topic model inference via tensor
decomposition, we make a first attempt to provide theoretical analysis on model
selection in latent Dirichlet allocation. Under mild conditions, we derive the
upper bound and lower bound on the number of topics given a text collection of
finite size. Experimental results demonstrate that our bounds are accurate and
tight. Furthermore, using Gaussian mixture model as an example, we show that
our methodology can be easily generalized to model selection analysis for other
latent models.
On the Predictive Properties of Binary Link Functions
Authors: Necla Gunduz, Ernest Fokoue
Date: 2015-02-16 00:00:00
Tasks: Binary Classification

This paper provides a theoretical and computational justification of the long
held claim that of the similarity of the probit and logit link functions often
used in binary classification. Despite this widespread recognition of the
strong similarities between these two link functions, very few (if any)
researchers have dedicated time to carry out a formal study aimed at
establishing and characterizing firmly all the aspects of the similarities and
differences. This paper proposes a definition of both structural and predictive
equivalence of link functions-based binary regression models, and explores the
various ways in which they are either similar or dissimilar. From a predictive
analytics perspective, it turns out that not only are probit and logit
perfectly predictively concordant, but the other link functions like cauchit
and complementary log log enjoy very high percentage of predictive equivalence.
Throughout this paper, simulated and real life examples demonstrate all the
equivalence results that we prove theoretically.
ICR: Iterative Convex Refinement for Sparse Signal Recovery Using Spike and Slab Priors
Authors: Hojjat S. Mousavi, Vishal Monga, Trac. D. Tran
Date: 2015-02-16 00:00:00

In this letter, we address sparse signal recovery using spike and slab
priors. In particular, we focus on a Bayesian framework where sparsity is
enforced on reconstruction coefficients via probabilistic priors. The
optimization resulting from spike and slab prior maximization is known to be a
hard non-convex problem, and existing solutions involve simplifying assumptions
and/or relaxations. We propose an approach called Iterative Convex Refinement
(ICR) that aims to solve the aforementioned optimization problem directly
allowing for greater generality in the sparse structure. Essentially, ICR
solves a sequence of convex optimization problems such that sequence of
solutions converges to a sub-optimal solution of the original hard optimization
problem. We propose two versions of our algorithm: a.) an unconstrained
version, and b.) with a non-negativity constraint on sparse coefficients, which
may be required in some real-world problems. Experimental validation is
performed on both synthetic data and for a real-world image recovery problem,
which illustrates merits of ICR over state of the art alternatives.
HEp-2 Cell Classification via Fusing Texture and Shape Information
Authors: Xianbiao Qi, Guoying Zhao, Chun-Guang Li, Jun Guo, Matti Pietikäinen
Date: 2015-02-16 00:00:00
Tasks: Classification, General Classification

Indirect Immunofluorescence (IIF) HEp-2 cell image is an effective evidence
for diagnosis of autoimmune diseases. Recently computer-aided diagnosis of
autoimmune diseases by IIF HEp-2 cell classification has attracted great
attention. However the HEp-2 cell classification task is quite challenging due
to large intra-class variation and small between-class variation. In this paper
we propose an effective and efficient approach for the automatic classification
of IIF HEp-2 cell image by fusing multi-resolution texture information and
richer shape information. To be specific, we propose to: a) capture the
multi-resolution texture information by a novel Pairwise Rotation Invariant
Co-occurrence of Local Gabor Binary Pattern (PRICoLGBP) descriptor, b) depict
the richer shape information by using an Improved Fisher Vector (IFV) model
with RootSIFT features which are sampled from large image patches in multiple
scales, and c) combine them properly. We evaluate systematically the proposed
approach on the IEEE International Conference on Pattern Recognition (ICPR)
2012, IEEE International Conference on Image Processing (ICIP) 2013 and ICPR
2014 contest data sets. The experimental results for the proposed methods
significantly outperform the winners of ICPR 2012 and ICIP 2013 contest, and
achieve comparable performance with the winner of the newly released ICPR 2014
contest.
Inferring 3D Object Pose in RGB-D Images
Authors: Saurabh Gupta, Pablo Arbeláez, Ross Girshick, Jitendra Malik
Date: 2015-02-16 00:00:00
Tasks: Object

The goal of this work is to replace objects in an RGB-D scene with
corresponding 3D models from a library. We approach this problem by first
detecting and segmenting object instances in the scene using the approach from
Gupta et al. [13]. We use a convolutional neural network (CNN) to predict the
pose of the object. This CNN is trained using pixel normals in images
containing rendered synthetic objects. When tested on real data, it outperforms
alternative algorithms trained on real data. We then use this coarse pose
estimate along with the inferred pixel support to align a small number of
prototypical models to the data, and place the model that fits the best into
the scene. We observe a 48% relative improvement in performance at the task of
3D detection over the current state-of-the-art [33], while being an order of
magnitude faster at the same time.
Large-scale optimization with the primal-dual column generation method
Authors: Jacek Gondzio, Pablo González-Brevis, Pedro Munari
Date: 2013-09-09 00:00:00
Tasks: CPU, Decision Making, Decision Making Under Uncertainty

The primal-dual column generation method (PDCGM) is a general-purpose column
generation technique that relies on the primal-dual interior point method to
solve the restricted master problems. The use of this interior point method
variant allows to obtain suboptimal and well-centered dual solutions which
naturally stabilizes the column generation. As recently presented in the
literature, reductions in the number of calls to the oracle and in the CPU
times are typically observed when compared to the standard column generation,
which relies on extreme optimal dual solutions. However, these results are
based on relatively small problems obtained from linear relaxations of
combinatorial applications. In this paper, we investigate the behaviour of the
PDCGM in a broader context, namely when solving large-scale convex optimization
problems. We have selected applications that arise in important real-life
contexts such as data analysis (multiple kernel learning problem),
decision-making under uncertainty (two-stage stochastic programming problems)
and telecommunication and transportation networks (multicommodity network flow
problem). In the numerical experiments, we use publicly available benchmark
instances to compare the performance of the PDCGM against recent results for
different methods presented in the literature, which were the best available
results to date. The analysis of these results suggests that the PDCGM offers
an attractive alternative over specialized methods since it remains competitive
in terms of number of iterations and CPU times even for large-scale
optimization problems.
About Tau-Chain
Authors: Ohad Asor
Date: 2015-02-16 00:00:00

Tau-chain is a decentralized peer-to-peer network having three unified faces:
Rules, Proofs, and Computer Programs, allowing a generalization of virtually
any centralized or decentralized P2P network, together with many new abilities,
as we present on this note.
Particle Gibbs for Bayesian Additive Regression Trees
Authors: Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Date: 2015-02-16 00:00:00
Tasks: regression

Additive regression trees are flexible non-parametric models and popular
off-the-shelf tools for real-world non-linear regression. In application
domains, such as bioinformatics, where there is also demand for probabilistic
predictions with measures of uncertainty, the Bayesian additive regression
trees (BART) model, introduced by Chipman et al. (2010), is increasingly
popular. As data sets have grown in size, however, the standard
Metropolis-Hastings algorithms used to perform inference in BART are proving
inadequate. In particular, these Markov chains make local changes to the trees
and suffer from slow mixing when the data are high-dimensional or the best
fitting trees are more than a few layers deep. We present a novel sampler for
BART based on the Particle Gibbs (PG) algorithm (Andrieu et al., 2010) and a
top-down particle filtering algorithm for Bayesian decision trees
(Lakshminarayanan et al., 2013). Rather than making local changes to individual
trees, the PG sampler proposes a complete tree to fit the residual. Experiments
show that the PG sampler outperforms existing samplers in many settings.
Deep Transform: Error Correction via Probabilistic Re-Synthesis
Authors: Andrew J. R. Simpson
Date: 2015-02-16 00:00:00

Errors in data are usually unwelcome and so some means to correct them is
useful. However, it is difficult to define, detect or correct errors in an
unsupervised way. Here, we train a deep neural network to re-synthesize its
inputs at its output layer for a given class of data. We then exploit the fact
that this abstract transformation, which we call a deep transform (DT),
inherently rejects information (errors) existing outside of the abstract
feature space. Using the DT to perform probabilistic re-synthesis, we
demonstrate the recovery of data that has been subject to extreme degradation.
Explaining robust additive utility models by sequences of preference swaps
Authors: K. Belahcene, C. Labreuche, N. Maudet, V. Mousseau, W. Ouerdane
Date: 2015-02-16 00:00:00

Multicriteria decision analysis aims at supporting a person facing a decision
problem involving conflicting criteria. We consider an additive utility model
which provides robust conclusions based on preferences elicited from the
decision maker. The recommendations based on these robust conclusions are even
more convincing if they are complemented by explanations. We propose a general
scheme, based on sequence of preference swaps, in which explanations can be
computed. We show first that the length of explanations can be unbounded in the
general case. However, in the case of binary reference scales, this length is
bounded and we provide an algorithm to compute the corresponding explanation.
The Ladder: A Reliable Leaderboard for Machine Learning Competitions
Authors: Avrim Blum, Moritz Hardt
Date: 2015-02-16 00:00:00
Tasks: BIG-bench Machine Learning

The organizer of a machine learning competition faces the problem of
maintaining an accurate leaderboard that faithfully represents the quality of
the best submission of each competing team. What makes this estimation problem
particularly challenging is its sequential and adaptive nature. As participants
are allowed to repeatedly evaluate their submissions on the leaderboard, they
may begin to overfit to the holdout data that supports the leaderboard. Few
theoretical results give actionable advice on how to design a reliable
leaderboard. Existing approaches therefore often resort to poorly understood
heuristics such as limiting the bit precision of answers and the rate of
re-submission.
  In this work, we introduce a notion of "leaderboard accuracy" tailored to the
format of a competition. We introduce a natural algorithm called "the Ladder"
and demonstrate that it simultaneously supports strong theoretical guarantees
in a fully adaptive model of estimation, withstands practical adversarial
attacks, and achieves high utility on real submission files from an actual
competition hosted by Kaggle.
  Notably, we are able to sidestep a powerful recent hardness result for
adaptive risk estimation that rules out algorithms such as ours under a
seemingly very similar notion of accuracy. On a practical note, we provide a
completely parameter-free variant of our algorithm that can be deployed in a
real competition with no tuning required whatsoever.
Mondrian Forests: Efficient Online Random Forests
Authors: Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Date: 2014-06-10 00:00:00
Proceeding: NeurIPS 2014 12

Ensembles of randomized decision trees, usually referred to as random
forests, are widely used for classification and regression tasks in machine
learning and statistics. Random forests achieve competitive predictive
performance and are computationally efficient to train and test, making them
excellent candidates for real-world prediction tasks. The most popular random
forest variants (such as Breiman's random forest and extremely randomized
trees) operate on batches of training data. Online methods are now in greater
demand. Existing online random forests, however, require more training data
than their batch counterpart to achieve comparable predictive performance. In
this work, we use Mondrian processes (Roy and Teh, 2009) to construct ensembles
of random decision trees we call Mondrian forests. Mondrian forests can be
grown in an incremental/online fashion and remarkably, the distribution of
online Mondrian forests is the same as that of batch Mondrian forests. Mondrian
forests achieve competitive predictive performance comparable with existing
online random forests and periodically re-trained batch random forests, while
being more than an order of magnitude faster, thus representing a better
computation vs accuracy tradeoff.
Spatial Diffuseness Features for DNN-Based Speech Recognition in Noisy and Reverberant Environments
Authors: Andreas Schwarz, Christian Huemmer, Roland Maas, Walter Kellermann
Date: 2014-10-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We propose a spatial diffuseness feature for deep neural network (DNN)-based
automatic speech recognition to improve recognition accuracy in reverberant and
noisy environments. The feature is computed in real-time from multiple
microphone signals without requiring knowledge or estimation of the direction
of arrival, and represents the relative amount of diffuse noise in each time
and frequency bin. It is shown that using the diffuseness feature as an
additional input to a DNN-based acoustic model leads to a reduced word error
rate for the REVERB challenge corpus, both compared to logmelspec features
extracted from noisy signals, and features enhanced by spectral subtraction.
The Responsibility Weighted Mahalanobis Kernel for Semi-Supervised Training of Support Vector Machines for Classification
Authors: Tobias Reitmaier, Bernhard Sick
Date: 2015-02-13 00:00:00
Tasks: General Classification

Kernel functions in support vector machines (SVM) are needed to assess the
similarity of input samples in order to classify these samples, for instance.
Besides standard kernels such as Gaussian (i.e., radial basis function, RBF) or
polynomial kernels, there are also specific kernels tailored to consider
structure in the data for similarity assessment. In this article, we will
capture structure in data by means of probabilistic mixture density models, for
example Gaussian mixtures in the case of real-valued input spaces. From the
distance measures that are inherently contained in these models, e.g.,
Mahalanobis distances in the case of Gaussian mixtures, we derive a new kernel,
the responsibility weighted Mahalanobis (RWM) kernel. Basically, this kernel
emphasizes the influence of model components from which any two samples that
are compared are assumed to originate (that is, the "responsible" model
components). We will see that this kernel outperforms the RBF kernel and other
kernels capturing structure in data (such as the LAP kernel in Laplacian SVM)
in many applications where partially labeled data are available, i.e., for
semi-supervised training of SVM. Other key advantages are that the RWM kernel
can easily be used with standard SVM implementations and training algorithms
such as sequential minimal optimization, and heuristics known for the
parametrization of RBF kernels in a C-SVM can easily be transferred to this new
kernel. Properties of the RWM kernel are demonstrated with 20 benchmark data
sets and an increasing percentage of labeled samples in the training data.
Clustering by Descending to the Nearest Neighbor in the Delaunay Graph Space
Authors: Teng Qiu, Yong-Jie Li
Date: 2015-02-16 00:00:00
Tasks: Clustering

In our previous works, we proposed a physically-inspired rule to organize the
data points into an in-tree (IT) structure, in which some undesired edges are
allowed to occur. By removing those undesired or redundant edges, this IT
structure is divided into several separate parts, each representing one
cluster. In this work, we seek to prevent the undesired edges from arising at
the source. Before using the physically-inspired rule, data points are at first
organized into a proximity graph which restricts each point to select the
optimal directed neighbor just among its neighbors. Consequently, separated
in-trees or clusters automatically arise, without redundant edges requiring to
be removed.