pwc-archive/papers-with-abstracts huggingface dataset:
Multitask Learning of Vegetation Biochemistry from Hyperspectral Data
Authors: Utsav B. Gewali, Sildomar T. Monteiro
Date: 2016-10-22 00:00:00

Statistical models have been successful in accurately estimating the
biochemical contents of vegetation from the reflectance spectra. However, their
performance deteriorates when there is a scarcity of sizable amount of ground
truth data for modeling the complex non-linear relationship occurring between
the spectrum and the biochemical quantity. We propose a novel Gaussian process
based multitask learning method for improving the prediction of a biochemical
through the transfer of knowledge from the learned models for predicting
related biochemicals. This method is most advantageous when there are few
ground truth data for the biochemical of interest, but plenty of ground truth
data for related biochemicals. The proposed multitask Gaussian process
hypothesizes that the inter-relationship between the biochemical quantities is
better modeled by using a combination of two or more covariance functions and
inter-task correlation matrices. In the experiments, our method outperformed
the current methods on two real-world datasets.
Spectral Angle Based Unary Energy Functions for Spatial-Spectral Hyperspectral Classification using Markov Random Fields
Authors: Utsav B. Gewali, Sildomar T. Monteiro
Date: 2016-10-22 00:00:00
Tasks: Gaussian Processes, General Classification

In this paper, we propose and compare two spectral angle based approaches for
spatial-spectral classification. Our methods use the spectral angle to generate
unary energies in a grid-structured Markov random field defined over the pixel
labels of a hyperspectral image. The first approach is to use the exponential
spectral angle mapper (ESAM) kernel/covariance function, a spectral angle based
function, with the support vector machine and the Gaussian process classifier.
The second approach is to directly use the minimum spectral angle between the
test pixel and the training pixels as the unary energy. We compare the proposed
methods with the state-of-the-art Markov random field methods that use support
vector machines and Gaussian processes with squared exponential
kernel/covariance function. In our experiments with two datasets, it is seen
that using minimum spectral angle as unary energy produces better or comparable
results to the existing methods at a smaller running time.
Semi-Supervised Learning with Generative Adversarial Networks
Authors: Augustus Odena
Date: 2016-06-05 00:00:00

We extend Generative Adversarial Networks (GANs) to the semi-supervised
context by forcing the discriminator network to output class labels. We train a
generative model G and a discriminator D on a dataset with inputs belonging to
one of N classes. At training time, D is made to predict which of N+1 classes
the input belongs to, where an extra class is added to correspond to the
outputs of G. We show that this method can be used to create a more
data-efficient classifier and that it allows for generating higher quality
samples than a regular GAN.
A Theory of Local Learning, the Learning Channel, and the Optimality of Backpropagation
Authors: Pierre Baldi, Peter Sadowski
Date: 2015-06-22 00:00:00

In a physical neural system, where storage and processing are intimately
intertwined, the rules for adjusting the synaptic weights can only depend on
variables that are available locally, such as the activity of the pre- and
post-synaptic neurons, resulting in local learning rules. A systematic
framework for studying the space of local learning rules is obtained by first
specifying the nature of the local variables, and then the functional form that
ties them together into each learning rule. Such a framework enables also the
systematic discovery of new learning rules and exploration of relationships
between learning rules and group symmetries. We study polynomial local learning
rules stratified by their degree and analyze their behavior and capabilities in
both linear and non-linear units and networks. Stacking local learning rules in
deep feedforward networks leads to deep local learning. While deep local
learning can learn interesting representations, it cannot learn complex
input-output functions, even when targets are available for the top layer.
Learning complex input-output functions requires local deep learning where
target information is communicated to the deep layers through a backward
learning channel. The nature of the communicated information about the targets
and the structure of the learning channel partition the space of learning
algorithms. We estimate the learning channel capacity associated with several
algorithms and show that backpropagation outperforms them by simultaneously
maximizing the information rate and minimizing the computational cost, even in
recurrent networks. The theory clarifies the concept of Hebbian learning,
establishes the power and limitations of local learning rules, introduces the
learning channel which enables a formal analysis of the optimality of
backpropagation, and explains the sparsity of the space of learning rules
discovered so far.
Learning Cost-Effective Treatment Regimes using Markov Decision Processes
Authors: Himabindu Lakkaraju, Cynthia Rudin
Date: 2016-10-21 00:00:00
Tasks: Diagnostic

Decision makers, such as doctors and judges, make crucial decisions such as
recommending treatments to patients, and granting bails to defendants on a
daily basis. Such decisions typically involve weighting the potential benefits
of taking an action against the costs involved. In this work, we aim to
automate this task of learning \emph{cost-effective, interpretable and
actionable treatment regimes}. We formulate this as a problem of learning a
decision list -- a sequence of if-then-else rules -- which maps characteristics
of subjects (eg., diagnostic test results of patients) to treatments. We
propose a novel objective to construct a decision list which maximizes outcomes
for the population, and minimizes overall costs. We model the problem of
learning such a list as a Markov Decision Process (MDP) and employ a variant of
the Upper Confidence Bound for Trees (UCT) strategy which leverages customized
checks for pruning the search space effectively. Experimental results on real
world observational data capturing judicial bail decisions and treatment
recommendations for asthma patients demonstrate the effectiveness of our
approach.
Mean-Field Variational Inference for Gradient Matching with Gaussian Processes
Authors: Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann
Date: 2016-10-21 00:00:00
Tasks: Gaussian Processes, Variational Inference

Gradient matching with Gaussian processes is a promising tool for learning
parameters of ordinary differential equations (ODE's). The essence of gradient
matching is to model the prior over state variables as a Gaussian process which
implies that the joint distribution given the ODE's and GP kernels is also
Gaussian distributed. The state-derivatives are integrated out analytically
since they are modelled as latent variables. However, the state variables
themselves are also latent variables because they are contaminated by noise.
Previous work sampled the state variables since integrating them out is
\textit{not} analytically tractable. In this paper we use mean-field
approximation to establish tight variational lower bounds that decouple state
variables and are therefore, in contrast to the integral over state variables,
analytically tractable and even concave for a restricted family of ODE's,
including nonlinear and periodic ODE's. Such variational lower bounds
facilitate "hill climbing" to determine the maximum a posteriori estimate of
ODE parameters. An additional advantage of our approach over sampling methods
is the determination of a proxy to the intractable posterior distribution over
state variables given observations and the ODE's.
Automatic Image De-fencing System
Authors: Krishna Kanth Nakka
Date: 2016-10-21 00:00:00

Tourists and Wild-life photographers are often hindered in capturing their
cherished images or videos by a fence that limits accessibility to the scene of
interest. The situation has been exacerbated by growing concerns of security at
public places and a need exists to provide a tool that can be used for
post-processing such fenced videos to produce a de-fenced image. There are
several challenges in this problem, we identify them as Robust detection of
fence/occlusions and Estimating pixel motion of background scenes and Filling
in the fence/occlusions by utilizing information in multiple frames of the
input video. In this work, we aim to build an automatic post-processing tool
that can efficiently rid the input video of occlusion artifacts like fences.
Our work is distinguished by two major contributions. The first is the
introduction of learning based technique to detect the fences patterns with
complicated backgrounds. The second is the formulation of objective function
and further minimization through loopy belief propagation to fill-in the fence
pixels. We observe that grids of Histogram of oriented gradients descriptor
using Support vector machines based classifier significantly outperforms
detection accuracy of texels in a lattice. We present results of experiments
using several real-world videos to demonstrate the effectiveness of the
proposed fence detection and de-fencing algorithm.
Learning to Protect Communications with Adversarial Neural Cryptography
Authors: Martín Abadi, David G. Andersen
Date: 2016-10-21 00:00:00

We ask whether neural networks can learn to use secret keys to protect
information from other neural networks. Specifically, we focus on ensuring
confidentiality properties in a multiagent system, and we specify those
properties in terms of an adversary. Thus, a system may consist of neural
networks named Alice and Bob, and we aim to limit what a third neural network
named Eve learns from eavesdropping on the communication between Alice and Bob.
We do not prescribe specific cryptographic algorithms to these neural networks;
instead, we train end-to-end, adversarially. We demonstrate that the neural
networks can learn how to perform forms of encryption and decryption, and also
how to apply these operations selectively in order to meet confidentiality
goals.
Enhanced Object Detection via Fusion With Prior Beliefs from Image Classification
Authors: Yilun Cao, Hyungtae Lee, Heesung Kwon
Date: 2016-10-21 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Object, object-detection, Object Detection

In this paper, we introduce a novel fusion method that can enhance object
detection performance by fusing decisions from two different types of computer
vision tasks: object detection and image classification. In the proposed work,
the class label of an image obtained from image classification is viewed as
prior knowledge about existence or non-existence of certain objects. The prior
knowledge is then fused with the decisions of object detection to improve
detection accuracy by mitigating false positives of an object detector that are
strongly contradicted with the prior knowledge. A recently introduced novel
fusion approach called dynamic belief fusion (DBF) is used to fuse the detector
output with the classification prior. Experimental results show that the
detection performance of all the detection algorithms used in the proposed work
is improved on benchmark datasets via the proposed fusion framework.
Dictionary Learning Strategies for Compressed Fiber Sensing Using a Probabilistic Sparse Model
Authors: Christian Weiss, Abdelhak M. Zoubir
Date: 2016-10-21 00:00:00
Tasks: Dictionary Learning

We present a sparse estimation and dictionary learning framework for
compressed fiber sensing based on a probabilistic hierarchical sparse model. To
handle severe dictionary coherence, selective shrinkage is achieved using a
Weibull prior, which can be related to non-convex optimization with $p$-norm
constraints for $0 < p < 1$. In addition, we leverage the specific dictionary
structure to promote collective shrinkage based on a local similarity model.
This is incorporated in form of a kernel function in the joint prior density of
the sparse coefficients, thereby establishing a Markov random field-relation.
Approximate inference is accomplished using a hybrid technique that combines
Hamilton Monte Carlo and Gibbs sampling. To estimate the dictionary parameter,
we pursue two strategies, relying on either a deterministic or a probabilistic
model for the dictionary parameter. In the first strategy, the parameter is
estimated based on alternating estimation. In the second strategy, it is
jointly estimated along with the sparse coefficients. The performance is
evaluated in comparison to an existing method in various scenarios using
simulations and experimental data.
MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
Authors: Ethan Rudd, Manuel Günther, Terrance Boult
Date: 2016-03-22 00:00:00
Tasks: Attribute, Attribute Extraction, Face Recognition

Attribute recognition, particularly facial, extracts many labels for each
image. While some multi-task vision problems can be decomposed into separate
tasks and stages, e.g., training independent models for each task, for a
growing set of problems joint optimization across all tasks has been shown to
improve performance. We show that for deep convolutional neural network (DCNN)
facial attribute extraction, multi-task optimization is better. Unfortunately,
it can be difficult to apply joint optimization to DCNNs when training data is
imbalanced, and re-balancing multi-label data directly is structurally
infeasible, since adding/removing data to balance one label will change the
sampling of the other labels. This paper addresses the multi-label imbalance
problem by introducing a novel mixed objective optimization network (MOON) with
a loss function that mixes multiple task objectives with domain adaptive
re-weighting of propagated loss. Experiments demonstrate that not only does
MOON advance the state of the art in facial attribute recognition, but it also
outperforms independently trained DCNNs using the same data. When using facial
attributes for the LFW face recognition task, we show that our balanced (domain
adapted) network outperforms the unbalanced trained network.
Automated Big Text Security Classification
Authors: Khudran Alzhrani, Ethan M. Rudd, Terrance E. Boult, C. Edward Chow
Date: 2016-10-21 00:00:00
Tasks: Classification, General Classification

In recent years, traditional cybersecurity safeguards have proven ineffective
against insider threats. Famous cases of sensitive information leaks caused by
insiders, including the WikiLeaks release of diplomatic cables and the Edward
Snowden incident, have greatly harmed the U.S. government's relationship with
other governments and with its own citizens. Data Leak Prevention (DLP) is a
solution for detecting and preventing information leaks from within an
organization's network. However, state-of-art DLP detection models are only
able to detect very limited types of sensitive information, and research in the
field has been hindered due to the lack of available sensitive texts. Many
researchers have focused on document-based detection with artificially labeled
"confidential documents" for which security labels are assigned to the entire
document, when in reality only a portion of the document is sensitive. This
type of whole-document based security labeling increases the chances of
preventing authorized users from accessing non-sensitive information within
sensitive documents. In this paper, we introduce Automated Classification
Enabled by Security Similarity (ACESS), a new and innovative detection model
that penetrates the complexity of big text security classification/detection.
To analyze the ACESS system, we constructed a novel dataset, containing
formerly classified paragraphs from diplomatic cables made public by the
WikiLeaks organization. To our knowledge this paper is the first to analyze a
dataset that contains actual formerly sensitive information annotated at
paragraph granularity.
Enhanced EZW Technique for Compression of Image by Setting Detail Retaining Pass Number
Authors: Isha Tyagi, Ashish Nautiyal, Vishwanath Bijalwan, Meenu Balodhi
Date: 2014-07-03 00:00:00

This submission has been withdrawn by arXiv administrators because it
contains excessive and unattributed reuse of content from other authors.
Learning Theory for Distribution Regression
Authors: Zoltan Szabo, Bharath Sriperumbudur, Barnabas Poczos, Arthur Gretton
Date: 2014-11-08 00:00:00
Tasks: Density Estimation, Learning Theory, Open-Ended Question Answering, regression

We focus on the distribution regression problem: regressing to vector-valued
outputs from probability measures. Many important machine learning and
statistical tasks fit into this framework, including multi-instance learning
and point estimation problems without analytical solution (such as
hyperparameter or entropy estimation). Despite the large number of available
heuristics in the literature, the inherent two-stage sampled nature of the
problem makes the theoretical analysis quite challenging, since in practice
only samples from sampled distributions are observable, and the estimates have
to rely on similarities computed between sets of points. To the best of our
knowledge, the only existing technique with consistency guarantees for
distribution regression requires kernel density estimation as an intermediate
step (which often performs poorly in practice), and the domain of the
distributions to be compact Euclidean. In this paper, we study a simple,
analytically computable, ridge regression-based alternative to distribution
regression, where we embed the distributions to a reproducing kernel Hilbert
space, and learn the regressor from the embeddings to the outputs. Our main
contribution is to prove that this scheme is consistent in the two-stage
sampled setup under mild conditions (on separable topological domains enriched
with kernels): we present an exact computational-statistical efficiency
trade-off analysis showing that our estimator is able to match the one-stage
sampled minimax optimal rate [Caponnetto and De Vito, 2007; Steinwart et al.,
2009]. This result answers a 17-year-old open question, establishing the
consistency of the classical set kernel [Haussler, 1999; Gaertner et. al, 2002]
in regression. We also cover consistency for more recent kernels on
distributions, including those due to [Christmann and Steinwart, 2010].
Deep Models for Engagement Assessment With Scarce Label Information
Authors: Feng Li, Guangfan Zhang, Wei Wang, Roger Xu, Tom Schnell, Jonathan Wen, Frederic McKenzie, Jiang Li
Date: 2016-10-21 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Task engagement is defined as loadings on energetic arousal (affect), task
motivation, and concentration (cognition). It is usually challenging and
expensive to label cognitive state data, and traditional computational models
trained with limited label information for engagement assessment do not perform
well because of overfitting. In this paper, we proposed two deep models (i.e.,
a deep classifier and a deep autoencoder) for engagement assessment with scarce
label information. We recruited 15 pilots to conduct a 4-h flight simulation
from Seattle to Chicago and recorded their electroencephalograph (EEG) signals
during the simulation. Experts carefully examined the EEG signals and labeled
20 min of the EEG data for each pilot. The EEG signals were preprocessed and
power spectral features were extracted. The deep models were pretrained by the
unlabeled data and were fine-tuned by a different proportion of the labeled
data (top 1%, 3%, 5%, 10%, 15%, and 20%) to learn new representations for
engagement assessment. The models were then tested on the remaining labeled
data. We compared performances of the new data representations with the
original EEG features for engagement assessment. Experimental results show that
the representations learned by the deep models yielded better accuracies for
the six scenarios (77.09%, 80.45%, 83.32%, 85.74%, 85.78%, and 86.52%), based
on different proportions of the labeled data for training, as compared with the
corresponding accuracies (62.73%, 67.19%, 73.38%, 79.18%, 81.47%, and 84.92%)
achieved by the original EEG features. Deep models are effective for engagement
assessment especially when less label information was used for training.
Convex Formulation for Kernel PCA and its Use in Semi-Supervised Learning
Authors: Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens
Date: 2016-10-21 00:00:00
Tasks: General Classification

In this paper, Kernel PCA is reinterpreted as the solution to a convex
optimization problem. Actually, there is a constrained convex problem for each
principal component, so that the constraints guarantee that the principal
component is indeed a solution, and not a mere saddle point. Although these
insights do not imply any algorithmic improvement, they can be used to further
understand the method, formulate possible extensions and properly address them.
As an example, a new convex optimization problem for semi-supervised
classification is proposed, which seems particularly well-suited whenever the
number of known labels is small. Our formulation resembles a Least Squares SVM
problem with a regularization parameter multiplied by a negative sign, combined
with a variational principle for Kernel PCA. Our primal optimization principle
for semi-supervised learning is solved in terms of the Lagrange multipliers.
Numerical experiments in several classification tasks illustrate the
performance of the proposed model in problems with only a few labeled data.
Robust training on approximated minimal-entropy set
Authors: Tianpei Xie, Nasser. M. Narabadi, Alfred O. Hero
Date: 2016-10-21 00:00:00
Tasks: Anomaly Detection, General Classification, Robust classification

In this paper, we propose a general framework to learn a robust large-margin
binary classifier when corrupt measurements, called anomalies, caused by sensor
failure might be present in the training set. The goal is to minimize the
generalization error of the classifier on non-corrupted measurements while
controlling the false alarm rate associated with anomalous samples. By
incorporating a non-parametric regularizer based on an empirical entropy
estimator, we propose a Geometric-Entropy-Minimization regularized Maximum
Entropy Discrimination (GEM-MED) method to learn to classify and detect
anomalies in a joint manner. We demonstrate using simulated data and a real
multimodal data set. Our GEM-MED method can yield improved performance over
previous robust classification methods in terms of both classification accuracy
and anomaly detection rate.
UTA-poly and UTA-splines: additive value functions with polynomial marginals
Authors: Olivier Sobrie, Nicolas Gillis, Vincent Mousseau, Marc Pirlot
Date: 2016-03-05 00:00:00

Additive utility function models are widely used in multiple criteria
decision analysis. In such models, a numerical value is associated to each
alternative involved in the decision problem. It is computed by aggregating the
scores of the alternative on the different criteria of the decision problem.
The score of an alternative is determined by a marginal value function that
evolves monotonically as a function of the performance of the alternative on
this criterion. Determining the shape of the marginals is not easy for a
decision maker. It is easier for him/her to make statements such as
"alternative $a$ is preferred to $b$". In order to help the decision maker, UTA
disaggregation procedures use linear programming to approximate the marginals
by piecewise linear functions based only on such statements. In this paper, we
propose to infer polynomials and splines instead of piecewise linear functions
for the marginals. In this aim, we use semidefinite programming instead of
linear programming. We illustrate this new elicitation method and present some
experimental results.