pwc-archive/papers-with-abstracts huggingface dataset:
HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition
Authors: Zhicheng Yan, Hao Zhang, Robinson Piramuthu, Vignesh Jagadeesh, Dennis Decoste, Wei Di, Yizhou Yu
Date: 2014-10-03 00:00:00
Tasks: image-classification, Image Classification, Object Recognition

In image classification, visual separability between different object
categories is highly uneven, and some categories are more difficult to
distinguish than others. Such difficult categories demand more dedicated
classifiers. However, existing deep convolutional neural networks (CNN) are
trained as flat N-way classifiers, and few efforts have been made to leverage
the hierarchical structure of categories. In this paper, we introduce
hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category
hierarchy. An HD-CNN separates easy classes using a coarse category classifier
while distinguishing difficult classes using fine category classifiers. During
HD-CNN training, component-wise pretraining is followed by global finetuning
with a multinomial logistic loss regularized by a coarse category consistency
term. In addition, conditional executions of fine category classifiers and
layer parameter compression make HD-CNNs scalable for large-scale visual
recognition. We achieve state-of-the-art results on both CIFAR100 and
large-scale ImageNet 1000-class benchmark datasets. In our experiments, we
build up three different HD-CNNs and they lower the top-1 error of the standard
CNNs by 2.65%, 3.1% and 1.1%, respectively.
An Analysis of Active Learning With Uniform Feature Noise
Authors: Aaditya Ramdas, Barnabas Poczos, Aarti Singh, Larry Wasserman
Date: 2015-05-15 00:00:00
Tasks: Active Learning, Binary Classification, regression

In active learning, the user sequentially chooses values for feature $X$ and
an oracle returns the corresponding label $Y$. In this paper, we consider the
effect of feature noise in active learning, which could arise either because
$X$ itself is being measured, or it is corrupted in transmission to the oracle,
or the oracle returns the label of a noisy version of the query point. In
statistics, feature noise is known as "errors in variables" and has been
studied extensively in non-active settings. However, the effect of feature
noise in active learning has not been studied before. We consider the
well-known Berkson errors-in-variables model with additive uniform noise of
width $\sigma$.
  Our simple but revealing setting is that of one-dimensional binary
classification setting where the goal is to learn a threshold (point where the
probability of a $+$ label crosses half). We deal with regression functions
that are antisymmetric in a region of size $\sigma$ around the threshold and
also satisfy Tsybakov's margin condition around the threshold. We prove minimax
lower and upper bounds which demonstrate that when $\sigma$ is smaller than the
minimiax active/passive noiseless error derived in \cite{CN07}, then noise has
no effect on the rates and one achieves the same noiseless rates. For larger
$\sigma$, the \textit{unflattening} of the regression function on convolution
with uniform noise, along with its local antisymmetry around the threshold,
together yield a behaviour where noise \textit{appears} to be beneficial. Our
key result is that active learning can buy significant improvement over a
passive strategy even in the presence of feature noise.
Algorithmic Connections Between Active Learning and Stochastic Convex Optimization
Authors: Aaditya Ramdas, Aarti Singh
Date: 2015-05-15 00:00:00
Tasks: Active Learning

Interesting theoretical associations have been established by recent papers
between the fields of active learning and stochastic convex optimization due to
the common role of feedback in sequential querying mechanisms. In this paper,
we continue this thread in two parts by exploiting these relations for the
first time to yield novel algorithms in both fields, further motivating the
study of their intersection. First, inspired by a recent optimization algorithm
that was adaptive to unknown uniform convexity parameters, we present a new
active learning algorithm for one-dimensional thresholds that can yield minimax
rates by adapting to unknown noise parameters. Next, we show that one can
perform $d$-dimensional stochastic minimization of smooth uniformly convex
functions when only granted oracle access to noisy gradient signs along any
coordinate instead of real-valued gradients, by using a simple randomized
coordinate descent procedure where each line search can be solved by
$1$-dimensional active learning, provably achieving the same error convergence
rate as having the entire real-valued gradient. Combining these two parts
yields an algorithm that solves stochastic convex optimization of uniformly
convex and smooth functions using only noisy gradient signs by repeatedly
performing active learning, achieves optimal rates and is adaptive to all
unknown convexity and smoothness parameters.
Arabic Inquiry-Answer Dialogue Acts Annotation Schema
Authors: AbdelRahim A. Elmadany, Sherif M. Abdou, Mervat Gheith
Date: 2015-05-15 00:00:00

We present an annotation schema as part of an effort to create a manually
annotated corpus for Arabic dialogue language understanding including spoken
dialogue and written "chat" dialogue for inquiry-answer domain. The proposed
schema handles mainly the request and response acts that occurs frequently in
inquiry-answer debate conversations expressing request services, suggests, and
offers. We applied the proposed schema on 83 Arabic inquiry-answer dialogues.
Stabilizing Value Iteration with and without Approximation Errors
Authors: Ali Heydari
Date: 2014-12-17 00:00:00

Adaptive optimal control using value iteration (VI) initiated from a
stabilizing policy is theoretically analyzed in various aspects including the
continuity of the result, the stability of the system operated using any
single/constant resulting control policy, the stability of the system operated
using the evolving/time-varying control policy, the convergence of the
algorithm, and the optimality of the limit function. Afterwards, the effect of
presence of approximation errors in the involved function approximation
processes is incorporated and another set of results for boundedness of the
approximate VI as well as stability of the system operated under the results
for both cases of applying a single policy or an evolving policy are derived. A
feature of the presented results is providing estimations of the region of
attraction so that if the initial condition is within the region, the whole
trajectory will remain inside it and hence, the function approximation results
will be reliable.
Theoretical and Numerical Analysis of Approximate Dynamic Programming with Approximation Errors
Authors: Ali Heydari
Date: 2014-12-18 00:00:00

This study is aimed at answering the famous question of how the approximation
errors at each iteration of Approximate Dynamic Programming (ADP) affect the
quality of the final results considering the fact that errors at each iteration
affect the next iteration. To this goal, convergence of Value Iteration scheme
of ADP for deterministic nonlinear optimal control problems with undiscounted
cost functions is investigated while considering the errors existing in
approximating respective functions. The boundedness of the results around the
optimal solution is obtained based on quantities which are known in a general
optimal control problem and assumptions which are verifiable. Moreover, since
the presence of the approximation errors leads to the deviation of the results
from optimality, sufficient conditions for stability of the system operated by
the result obtained after a finite number of value iterations, along with an
estimation of its region of attraction, are derived in terms of a calculable
upper bound of the control approximation error. Finally, the process of
implementation of the method on an orbital maneuver problem is investigated
through which the assumptions made in the theoretical developments are verified
and the sufficient conditions are applied for guaranteeing stability and near
optimality.
Reinforcement Learning applied to Single Neuron
Authors: Zhipeng Wang, Mingbo Cai
Date: 2015-05-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper extends the reinforcement learning ideas into the multi-agents
system, which is far more complicated than the previously studied single-agent
system. We studied two different multi-agents systems. One is the
fully-connected neural network consists of multiple single neurons. Another one
is the simplified mechanical arm system which is controlled by multiple
neurons. We suppose that each neuron is like an agent and it can do Gibbs
sampling of the posterior probability of stimulus features. The policy is
optimized in a way that the cumulative global rewards are maximized. The
algorithm for the second system is based on the same idea but we incorporate
the physics model into the constraints. The simulation results show that for
the first system our algorithm converges well. For the second system it does
not converge well in a reasonable simulation time length. In summary, we took
the initial endeavor to study the reinforcement learning for multi-agents
system. The computational complexity is always an issue and significant amount
of works have to be done in order to better understand the problem.
Dense Semantic Correspondence where Every Pixel is a Classifier
Authors: Hilton Bristow, Jack Valmadre, Simon Lucey
Date: 2015-05-15 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object Recognition, Semantic correspondence

Determining dense semantic correspondences across objects and scenes is a
difficult problem that underpins many higher-level computer vision algorithms.
Unlike canonical dense correspondence problems which consider images that are
spatially or temporally adjacent, semantic correspondence is characterized by
images that share similar high-level structures whose exact appearance and
geometry may differ.
  Motivated by object recognition literature and recent work on rapidly
estimating linear classifiers, we treat semantic correspondence as a
constrained detection problem, where an exemplar LDA classifier is learned for
each pixel. LDA classifiers have two distinct benefits: (i) they exhibit higher
average precision than similarity metrics typically used in correspondence
problems, and (ii) unlike exemplar SVM, can output globally interpretable
posterior probabilities without calibration, whilst also being significantly
faster to train.
  We pose the correspondence problem as a graphical model, where the unary
potentials are computed via convolution with the set of exemplar classifiers,
and the joint potentials enforce smoothly varying correspondence assignment.
Consistent Algorithms for Multiclass Classification with a Reject Option
Authors: Harish G. Ramaswamy, Ambuj Tewari, Shivani Agarwal
Date: 2015-05-15 00:00:00
Tasks: Classification, General Classification

We consider the problem of $n$-class classification ($n\geq 2$), where the
classifier can choose to abstain from making predictions at a given cost, say,
a factor $\alpha$ of the cost of misclassification. Designing consistent
algorithms for such $n$-class classification problems with a `reject option' is
the main goal of this paper, thereby extending and generalizing previously
known results for $n=2$. We show that the Crammer-Singer surrogate and the one
vs all hinge loss, albeit with a different predictor than the standard argmax,
yield consistent algorithms for this problem when $\alpha=\frac{1}{2}$. More
interestingly, we design a new convex surrogate that is also consistent for
this problem when $\alpha=\frac{1}{2}$ and operates on a much lower dimensional
space ($\log(n)$ as opposed to $n$). We also generalize all three surrogates to
be consistent for any $\alpha\in[0, \frac{1}{2}]$.
Discrimination and characterization of Parkinsonian rest tremors by analyzing long-term correlations and multifractal signatures
Authors: Lorenzo Livi, Alireza Sadeghian, Hamid Sadeghian
Date: 2015-04-10 00:00:00
Tasks: General Classification

In this paper, we analyze 48 signals of rest tremor velocity related to 12
distinct subjects affected by Parkinson's disease. The subjects belong to two
different groups, formed by four and eight subjects with, respectively, high-
and low-amplitude rest tremors. Each subject is tested in four settings, given
by combining the use of deep brain stimulation and L-DOPA medication. We
develop two main feature-based representations of such signals, which are
obtained by considering (i) the long-term correlations and multifractal
properties, and (ii) the power spectra. The feature-based representations are
initially utilized for the purpose of characterizing the subjects under
different settings. In agreement with previous studies, we show that deep brain
stimulation does not significantly characterize neither of the two groups,
regardless of the adopted representation. On the other hand, the medication
effect yields statistically significant differences in both high- and
low-amplitude tremor groups. We successively test several different instances
of the two feature-based representations of the signals in the setting of
supervised classification and (nonlinear) feature transformation. We consider
three different classification problems, involving the recognition of (i) the
presence of medication, (ii) the use of deep brain stimulation, and (iii) the
membership to the high- and low-amplitude tremor groups. Classification results
show that the use of medication can be discriminated with higher accuracy,
considering many of the feature-based representations. Notably, we show that
the best results are obtained with a parsimonious, two-dimensional
representation encoding the long-term correlations and multifractal character
of the signals.
Margins, Kernels and Non-linear Smoothed Perceptrons
Authors: Aaditya Ramdas, Javier Peña
Date: 2015-05-15 00:00:00

We focus on the problem of finding a non-linear classification function that
lies in a Reproducing Kernel Hilbert Space (RKHS) both from the primal point of
view (finding a perfect separator when one exists) and the dual point of view
(giving a certificate of non-existence), with special focus on generalizations
of two classical schemes - the Perceptron (primal) and Von-Neumann (dual)
algorithms.
  We cast our problem as one of maximizing the regularized normalized
hard-margin ($\rho$) in an RKHS and %use the Representer Theorem to rephrase it
in terms of a Mahalanobis dot-product/semi-norm associated with the kernel's
(normalized and signed) Gram matrix. We derive an accelerated smoothed
algorithm with a convergence rate of $\tfrac{\sqrt {\log n}}{\rho}$ given $n$
separable points, which is strikingly similar to the classical kernelized
Perceptron algorithm whose rate is $\tfrac1{\rho^2}$. When no such classifier
exists, we prove a version of Gordan's separation theorem for RKHSs, and give a
reinterpretation of negative margins. This allows us to give guarantees for a
primal-dual algorithm that halts in $\min\{\tfrac{\sqrt n}{|\rho|},
\tfrac{\sqrt n}{\epsilon}\}$ iterations with a perfect separator in the RKHS if
the primal is feasible or a dual $\epsilon$-certificate of near-infeasibility.
Discovering Attribute Shades of Meaning with the Crowd
Authors: Adriana Kovashka, Kristen Grauman
Date: 2015-05-15 00:00:00
Tasks: Attribute, Image Retrieval

To learn semantic attributes, existing methods typically train one
discriminative model for each word in a vocabulary of nameable properties.
However, this "one model per word" assumption is problematic: while a word
might have a precise linguistic definition, it need not have a precise visual
definition. We propose to discover shades of attribute meaning. Given an
attribute name, we use crowdsourced image labels to discover the latent factors
underlying how different annotators perceive the named concept. We show that
structure in those latent factors helps reveal shades, that is, interpretations
for the attribute shared by some group of annotators. Using these shades, we
train classifiers to capture the primary (often subtle) variants of the
attribute. The resulting models are both semantic and visually precise. By
catering to users' interpretations, they improve attribute prediction accuracy
on novel images. Shades also enable more successful attribute-based image
search, by providing robust personalized models for retrieving multi-attribute
query results. They are widely applicable to tasks that involve describing
visual content, such as zero-shot category learning and organization of photo
collections.
How, What and Why to test an ontology
Authors: Jennifer D. Warrender, Phillip Lord
Date: 2015-05-15 00:00:00

Ontology development relates to software development in that they both
involve the production of formal computational knowledge. It is possible,
therefore, that some of the techniques used in software engineering could also
be used for ontologies; for example, in software engineering testing is a
well-established process, and part of many different methodologies.
  The application of testing to ontologies, therefore, seems attractive. The
Karyotype Ontology is developed using the novel Tawny-OWL library. This
provides a fully programmatic environment for ontology development, which
includes a complete test harness.
  In this paper, we describe how we have used this harness to build an
extensive series of tests as well as used a commodity continuous integration
system to link testing deeply into our development process; this environment,
is applicable to any OWL ontology whether written using Tawny-OWL or not.
Moreover, we present a novel analysis of our tests, introducing a new
classification of what our different tests are. For each class of test, we
describe why we use these tests, also by comparison to software tests. We
believe that this systematic comparison between ontology and software
development will help us move to a more agile form of ontology development.
OntoSOC: Sociocultural Knowledge Ontology
Authors: Guidedi Kaladzavi, Papa Fary Diallo, Kolyang, Moussa Lo
Date: 2015-05-15 00:00:00
Tasks: Information Retrieval, Retrieval

This paper presents a sociocultural knowledge ontology (OntoSOC) modeling
approach. OntoSOC modeling approach is based on Engestrom Human Activity Theory
(HAT). That Theory allowed us to identify fundamental concepts and
relationships between them. The top-down precess has been used to define
differents sub-concepts. The modeled vocabulary permits us to organise data, to
facilitate information retrieval by introducing a semantic layer in social web
platform architecture, we project to implement. This platform can be considered
as a collective memory and Participative and Distributed Information System
(PDIS) which will allow Cameroonian communities to share an co-construct
knowledge on permanent organized activities.
MCODE: Multivariate Conditional Outlier Detection
Authors: Charmgil Hong, Milos Hauskrecht
Date: 2015-05-15 00:00:00
Tasks: General Classification, Outlier Detection

Outlier detection aims to identify unusual data instances that deviate from
expected patterns. The outlier detection is particularly challenging when
outliers are context dependent and when they are defined by unusual
combinations of multiple outcome variable values. In this paper, we develop and
study a new conditional outlier detection approach for multivariate outcome
spaces that works by (1) transforming the conditional detection to the outlier
detection problem in a new (unconditional) space and (2) defining outlier
scores by analyzing the data in the new space. Our approach relies on the
classifier chain decomposition of the multi-dimensional classification problem
that lets us transform the output space into a probability vector, one
probability for each dimension of the output space. Outlier scores applied to
these transformed vectors are then used to detect the outliers. Experiments on
multiple multi-dimensional classification problems with the different outlier
injection rates show that our methodology is robust and able to successfully
identify outliers when outliers are either sparse (manifested in one or very
few dimensions) or dense (affecting multiple dimensions).
Optimal Low-Rank Tensor Recovery from Separable Measurements: Four Contractions Suffice
Authors: Parikshit Shah, Nikhil Rao, Gongguo Tang
Date: 2015-05-15 00:00:00
Tasks: Matrix Completion, Tensor Decomposition

Tensors play a central role in many modern machine learning and signal
processing applications. In such applications, the target tensor is usually of
low rank, i.e., can be expressed as a sum of a small number of rank one
tensors. This motivates us to consider the problem of low rank tensor recovery
from a class of linear measurements called separable measurements. As specific
examples, we focus on two distinct types of separable measurement mechanisms
(a) Random projections, where each measurement corresponds to an inner product
of the tensor with a suitable random tensor, and (b) the completion problem
where measurements constitute revelation of a random set of entries. We present
a computationally efficient algorithm, with rigorous and order-optimal sample
complexity results (upto logarithmic factors) for tensor recovery. Our method
is based on reduction to matrix completion sub-problems and adaptation of
Leurgans' method for tensor decomposition. We extend the methodology and sample
complexity results to higher order tensors, and experimentally validate our
theoretical results.
Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices
Authors: Jie Wang, Jieping Ye
Date: 2015-05-15 00:00:00
Tasks: Clustering

Multi-task feature learning (MTFL) is a powerful technique in boosting the
predictive performance by learning multiple related
classification/regression/clustering tasks simultaneously. However, solving the
MTFL problem remains challenging when the feature dimension is extremely large.
In this paper, we propose a novel screening rule---that is based on the dual
projection onto convex sets (DPC)---to quickly identify the inactive
features---that have zero coefficients in the solution vectors across all
tasks. One of the appealing features of DPC is that: it is safe in the sense
that the detected inactive features are guaranteed to have zero coefficients in
the solution vectors across all tasks. Thus, by removing the inactive features
from the training phase, we may have substantial savings in the computational
cost and memory usage without sacrificing accuracy. To the best of our
knowledge, it is the first screening rule that is applicable to sparse models
with multiple data matrices. A key challenge in deriving DPC is to solve a
nonconvex problem. We show that we can solve for the global optimum efficiently
via a properly chosen parametrization of the constraint set. Moreover, DPC has
very low computational cost and can be integrated with any existing solvers. We
have evaluated the proposed DPC rule on both synthetic and real data sets. The
experiments indicate that DPC is very effective in identifying the inactive
features---especially for high dimensional data---which leads to a speedup up
to several orders of magnitude.