pwc-archive/papers-with-abstracts huggingface dataset:
MuProp: Unbiased Backpropagation for Stochastic Neural Networks
Authors: Shixiang Gu, Sergey Levine, Ilya Sutskever, andriy mnih
Date: 2015-11-16 00:00:00

Deep neural networks are powerful parametric models that can be trained
efficiently using the backpropagation algorithm. Stochastic neural networks
combine the power of large parametric functions with that of graphical models,
which makes it possible to learn very complex distributions. However, as
backpropagation is not directly applicable to stochastic networks that include
discrete sampling operations within their computational graph, training such
networks remains difficult. We present MuProp, an unbiased gradient estimator
for stochastic networks, designed to make this task easier. MuProp improves on
the likelihood-ratio estimator by reducing its variance using a control variate
based on the first-order Taylor expansion of a mean-field network. Crucially,
unlike prior attempts at using backpropagation for training stochastic
networks, the resulting estimator is unbiased and well behaved. Our experiments
on structured output prediction and discrete latent variable modeling
demonstrate that MuProp yields consistently good performance across a range of
difficult tasks.
PCA/LDA Approach for Text-Independent Speaker Recognition
Authors: Zhenhao Ge, Sudhendu R. Sharma, Mark J. T. Smith
Date: 2016-02-25 00:00:00
Tasks: Speaker Recognition, Text-Independent Speaker Recognition

Various algorithms for text-independent speaker recognition have been
developed through the decades, aiming to improve both accuracy and efficiency.
This paper presents a novel PCA/LDA-based approach that is faster than
traditional statistical model-based methods and achieves competitive results.
First, the performance based on only PCA and only LDA is measured; then a mixed
model, taking advantages of both methods, is introduced. A subset of the TIMIT
corpus composed of 200 male speakers, is used for enrollment, validation and
testing. The best results achieve 100%; 96% and 95% classification rate at
population level 50; 100 and 200, using 39-dimensional MFCC features with delta
and double delta. These results are based on 12-second text-independent speech
for training and 4-second data for test. These are comparable to the
conventional MFCC-GMM methods, but require significantly less time to train and
operate.
Local entropy as a measure for sampling solutions in Constraint Satisfaction Problems
Authors: Carlo Baldassi, Alessandro Ingrosso, Carlo Lucibello, Luca Saglietti, Riccardo Zecchina
Date: 2015-11-18 00:00:00

We introduce a novel Entropy-driven Monte Carlo (EdMC) strategy to
efficiently sample solutions of random Constraint Satisfaction Problems (CSPs).
First, we extend a recent result that, using a large-deviation analysis, shows
that the geometry of the space of solutions of the Binary Perceptron Learning
Problem (a prototypical CSP), contains regions of very high-density of
solutions. Despite being sub-dominant, these regions can be found by optimizing
a local entropy measure. Building on these results, we construct a fast solver
that relies exclusively on a local entropy estimate, and can be applied to
general CSPs. We describe its performance not only for the Perceptron Learning
Problem but also for the random $K$-Satisfiabilty Problem (another prototypical
CSP with a radically different structure), and show numerically that a simple
zero-temperature Metropolis search in the smooth local entropy landscape can
reach sub-dominant clusters of optimal solutions in a small number of steps,
while standard Simulated Annealing either requires extremely long cooling
procedures or just fails. We also discuss how the EdMC can heuristically be
made even more efficient for the cases we studied.
Meta-learning within Projective Simulation
Authors: Adi Makmal, Alexey A. Melnikov, Vedran Dunjko, Hans J. Briegel
Date: 2016-02-25 00:00:00
Tasks: Meta-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning models of artificial intelligence can nowadays perform very well on
a large variety of tasks. However, in practice different task environments are
best handled by different learning models, rather than a single, universal,
approach. Most non-trivial models thus require the adjustment of several to
many learning parameters, which is often done on a case-by-case basis by an
external party. Meta-learning refers to the ability of an agent to autonomously
and dynamically adjust its own learning parameters, or meta-parameters. In this
work we show how projective simulation, a recently developed model of
artificial intelligence, can naturally be extended to account for meta-learning
in reinforcement learning settings. The projective simulation approach is based
on a random walk process over a network of clips. The suggested meta-learning
scheme builds upon the same design and employs clip networks to monitor the
agent's performance and to adjust its meta-parameters "on the fly". We
distinguish between "reflexive adaptation" and "adaptation through learning",
and show the utility of both approaches. In addition, a trade-off between
flexibility and learning-time is addressed. The extended model is examined on
three different kinds of reinforcement learning tasks, in which the agent has
different optimal values of the meta-parameters, and is shown to perform well,
reaching near-optimal to optimal success rates in all of them, without ever
needing to manually adjust any meta-parameter.
Prioritized Experience Replay
Authors: Tom Schaul, John Quan, Ioannis Antonoglou, David Silver
Date: 2015-11-18 00:00:00
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Experience replay lets online reinforcement learning agents remember and
reuse experiences from the past. In prior work, experience transitions were
uniformly sampled from a replay memory. However, this approach simply replays
transitions at the same frequency that they were originally experienced,
regardless of their significance. In this paper we develop a framework for
prioritizing experience, so as to replay important transitions more frequently,
and therefore learn more efficiently. We use prioritized experience replay in
Deep Q-Networks (DQN), a reinforcement learning algorithm that achieved
human-level performance across many Atari games. DQN with prioritized
experience replay achieves a new state-of-the-art, outperforming DQN with
uniform replay on 41 out of 49 games.
Practical Riemannian Neural Networks
Authors: Gaétan Marceau-Caron, Yann Ollivier
Date: 2016-02-25 00:00:00

We provide the first experimental results on non-synthetic datasets for the
quasi-diagonal Riemannian gradient descents for neural networks introduced in
[Ollivier, 2015]. These include the MNIST, SVHN, and FACE datasets as well as a
previously unpublished electroencephalogram dataset. The quasi-diagonal
Riemannian algorithms consistently beat simple stochastic gradient gradient
descents by a varying margin. The computational overhead with respect to simple
backpropagation is around a factor $2$. Perhaps more interestingly, these
methods also reach their final performance quickly, thus requiring fewer
training epochs and a smaller total computation time.
  We also present an implementation guide to these Riemannian gradient descents
for neural networks, showing how the quasi-diagonal versions can be implemented
with minimal effort on top of existing routines which compute gradients.
How effective can simple ordinal peer grading be?
Authors: Ioannis Caragiannis, George A. Krimpas, Alexandros A. Voudouris
Date: 2016-02-25 00:00:00

Ordinal peer grading has been proposed as a simple and scalable solution for computing reliable information about student performance in massive open online courses. The idea is to outsource the grading task to the students themselves as follows. After the end of an exam, each student is asked to rank -- in terms of quality -- a bundle of exam papers by fellow students. An aggregation rule then combines the individual rankings into a global one that contains all students. We define a broad class of simple aggregation rules, which we call type-ordering aggregation rules, and present a theoretical framework for assessing their effectiveness. When statistical information about the grading behaviour of students is available (in terms of a noise matrix that characterizes the grading behaviour of the average student from a student population), the framework can be used to compute the optimal rule from this class with respect to a series of performance objectives that compare the ranking returned by the aggregation rule to the underlying ground truth ranking. For example, a natural rule known as Borda is proved to be optimal when students grade correctly. In addition, we present extensive simulations that validate our theory and prove it to be extremely accurate in predicting the performance of aggregation rules even when only rough information about grading behaviour (i.e., an approximation of the noise matrix) is available. Both in the application of our theoretical framework and in our simulations, we exploit data about grading behaviour of students that have been extracted from two field experiments in the University of Patras.
Robust Convolutional Neural Networks under Adversarial Noise
Authors: Jonghoon Jin, Aysegul Dundar, Eugenio Culurciello
Date: 2015-11-19 00:00:00

Recent studies have shown that Convolutional Neural Networks (CNNs) are
vulnerable to a small perturbation of input called "adversarial examples". In
this work, we propose a new feedforward CNN that improves robustness in the
presence of adversarial noise. Our model uses stochastic additive noise added
to the input image and to the CNN models. The proposed model operates in
conjunction with a CNN trained with either standard or adversarial objective
function. In particular, convolution, max-pooling, and ReLU layers are modified
to benefit from the noise model. Our feedforward model is parameterized by only
a mean and variance per pixel which simplifies computations and makes our
method scalable to a deep architecture. From CIFAR-10 and ImageNet test, the
proposed model outperforms other methods and the improvement is more evident
for difficult classification tasks or stronger adversarial noise.
Accurate Urban Road Centerline Extraction from VHR Imagery via Multiscale Segmentation and Tensor Voting
Authors: Guangliang Cheng, Feiyun Zhu, Shiming Xiang, Chunhong Pan
Date: 2015-08-25 00:00:00
Tasks: Road Segmentation

It is very useful and increasingly popular to extract accurate road
centerlines from very-high-resolution (VHR) re- mote sensing imagery for
various applications, such as road map generation and updating etc. There are
three shortcomings of current methods: (a) Due to the noise and occlusions
(owing to vehicles and trees), most road extraction methods bring in
heterogeneous classification results; (b) Morphological thinning algorithm is
widely used to extract road centerlines, while it pro- duces small spurs around
the centerlines; (c) Many methods are ineffective to extract centerlines around
the road intersections. To address the above three issues, we propose a novel
method to ex- tract smooth and complete road centerlines via three techniques:
the multiscale joint collaborative representation (MJCR) & graph cuts (GC),
tensor voting (TV) & non-maximum suppression (NMS) and fitting based connection
algorithm. Specifically, a MJCR-GC based road area segmentation method is
proposed by incorporating mutiscale features and spatial information. In this
way, a homogenous road segmentation result is achieved. Then, to obtain a
smooth and correct road centerline network, a TV-NMS based centerline
extraction method is introduced. This method not only extracts smooth road
centerlines, but also connects the discontinuous road centerlines. Finally, to
overcome the ineffectiveness of current methods in the road intersection, a
fitting based road centerline connection algorithm is proposed. As a result, we
can get a complete road centerline network. Extensive experiments on two
datasets demonstrate that our method achieves higher quantitative results, as
well as more satisfactory visual performances by comparing with state-of-the-
art methods.
Expanded Parts Model for Semantic Description of Humans in Still Images
Authors: Gaurav Sharma, Frederic Jurie, Cordelia Schmid
Date: 2015-09-14 00:00:00

We introduce an Expanded Parts Model (EPM) for recognizing human attributes
(e.g. young, short hair, wearing suit) and actions (e.g. running, jumping) in
still images. An EPM is a collection of part templates which are learnt
discriminatively to explain specific scale-space regions in the images (in
human centric coordinates). This is in contrast to current models which consist
of a relatively few (i.e. a mixture of) 'average' templates. EPM uses only a
subset of the parts to score an image and scores the image sparsely in space,
i.e. it ignores redundant and random background in an image. To learn our
model, we propose an algorithm which automatically mines parts and learns
corresponding discriminative templates together with their respective locations
from a large number of candidate parts. We validate our method on three recent
challenging datasets of human attributes and actions. We obtain convincing
qualitative and state-of-the-art quantitative results on the three datasets.
Firefly Algorithm for optimization problems with non-continuous variables: A Review and Analysis
Authors: Surafel Luleseged Tilahun, Jean Medard T Ngnotchouye
Date: 2016-02-25 00:00:00

Firefly algorithm is a swarm based metaheuristic algorithm inspired by the
flashing behavior of fireflies. It is an effective and an easy to implement
algorithm. It has been tested on different problems from different disciplines
and found to be effective. Even though the algorithm is proposed for
optimization problems with continuous variables, it has been modified and used
for problems with non-continuous variables, including binary and integer valued
problems. In this paper a detailed review of this modifications of firefly
algorithm for problems with non-continuous variables will be discussed. The
strength and weakness of the modifications along with possible future works
will be presented.
CNN for License Plate Motion Deblurring
Authors: Pavel Svoboda, Michal Hradis, Lukas Marsik, Pavel Zemcik
Date: 2016-02-25 00:00:00
Tasks: Deblurring, Denoising

In this work we explore the previously proposed approach of direct blind
deconvolution and denoising with convolutional neural networks in a situation
where the blur kernels are partially constrained. We focus on blurred images
from a real-life traffic surveillance system, on which we, for the first time,
demonstrate that neural networks trained on artificial data provide superior
reconstruction quality on real images compared to traditional blind
deconvolution methods. The training data is easy to obtain by blurring sharp
photos from a target system with a very rough approximation of the expected
blur kernels, thereby allowing custom CNNs to be trained for a specific
application (image content and blur range). Additionally, we evaluate the
behavior and limits of the CNNs with respect to blur direction range and
length.
Projected Estimators for Robust Semi-supervised Classification
Authors: Jesse H. Krijthe, Marco Loog
Date: 2016-02-25 00:00:00
Tasks: Classification, General Classification

For semi-supervised techniques to be applied safely in practice we at least
want methods to outperform their supervised counterparts. We study this
question for classification using the well-known quadratic surrogate loss
function. Using a projection of the supervised estimate onto a set of
constraints imposed by the unlabeled data, we find we can safely improve over
the supervised solution in terms of this quadratic loss. Unlike other
approaches to semi-supervised learning, the procedure does not rely on
assumptions that are not intrinsic to the classifier at hand. It is
theoretically demonstrated that, measured on the labeled and unlabeled training
data, this semi-supervised procedure never gives a lower quadratic loss than
the supervised alternative. To our knowledge this is the first approach that
offers such strong, albeit conservative, guarantees for improvement over the
supervised solution. The characteristics of our approach are explicated using
benchmark datasets to further understand the similarities and differences
between the quadratic loss criterion used in the theoretical results and the
classification accuracy often considered in practice.
Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood
Authors: Janne Leppä-aho, Johan Pensar, Teemu Roos, Jukka Corander
Date: 2016-02-25 00:00:00

We propose a Bayesian approximate inference method for learning the
dependence structure of a Gaussian graphical model. Using pseudo-likelihood, we
derive an analytical expression to approximate the marginal likelihood for an
arbitrary graph structure without invoking any assumptions about
decomposability. The majority of the existing methods for learning Gaussian
graphical models are either restricted to decomposable graphs or require
specification of a tuning parameter that may have a substantial impact on
learned structures. By combining a simple sparsity inducing prior for the graph
structures with a default reference prior for the model parameters, we obtain a
fast and easily applicable scoring function that works well for even
high-dimensional data. We demonstrate the favourable performance of our
approach by large-scale comparisons against the leading methods for learning
non-decomposable Gaussian graphical models. A theoretical justification for our
method is provided by showing that it yields a consistent estimator of the
graph structure.
Probably Approximately Correct Greedy Maximization with Efficient Bounds on Information Gain for Sensor Selection
Authors: Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek
Date: 2016-02-25 00:00:00
Tasks: Decision Making

Submodular function maximization finds application in a variety of real-world decision-making problems. However, most existing methods, based on greedy maximization, assume it is computationally feasible to evaluate F, the function being maximized. Unfortunately, in many realistic settings F is too expensive to evaluate exactly even once. We present probably approximately correct greedy maximization, which requires access only to cheap anytime confidence bounds on F and uses them to prune elements. We show that, with high probability, our method returns an approximately optimal set. We propose novel, cheap confidence bounds for conditional entropy, which appears in many common choices of F and for which it is difficult to find unbiased or bounded estimates. Finally, results on a real-world dataset from a multi-camera tracking system in a shopping mall demonstrate that our approach performs comparably to existing methods, but at a fraction of the computational cost.
Kernel Mean Shrinkage Estimators
Authors: Krikamol Muandet, Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf
Date: 2014-05-21 00:00:00

A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel
mean, is central to kernel methods in that it is used by many classical
algorithms such as kernel principal component analysis, and it also forms the
core inference step of modern kernel methods that rely on embedding probability
distributions in RKHSs. Given a finite sample, an empirical average has been
used commonly as a standard estimator of the true kernel mean. Despite a
widespread use of this estimator, we show that it can be improved thanks to the
well-known Stein phenomenon. We propose a new family of estimators called
kernel mean shrinkage estimators (KMSEs), which benefit from both theoretical
justifications and good empirical performance. The results demonstrate that the
proposed estimators outperform the standard one, especially in a "large d,
small n" paradigm.
Fast Nonsmooth Regularized Risk Minimization with Continuation
Authors: Shuai Zheng, Ruiliang Zhang, James T. Kwok
Date: 2016-02-25 00:00:00

In regularized risk minimization, the associated optimization problem becomes
particularly difficult when both the loss and regularizer are nonsmooth.
Existing approaches either have slow or unclear convergence properties, are
restricted to limited problem subclasses, or require careful setting of a
smoothing parameter. In this paper, we propose a continuation algorithm that is
applicable to a large class of nonsmooth regularized risk minimization
problems, can be flexibly used with a number of existing solvers for the
underlying smoothed subproblem, and with convergence results on the whole
algorithm rather than just one of its subproblems. In particular, when
accelerated solvers are used, the proposed algorithm achieves the fastest known
rates of $O(1/T^2)$ on strongly convex problems, and $O(1/T)$ on general convex
problems. Experiments on nonsmooth classification and regression tasks
demonstrate that the proposed algorithm outperforms the state-of-the-art.
Automated Word Prediction in Bangla Language Using Stochastic Language Models
Authors: Md. Masudul Haque, Md. Tarek Habib, Md. Mokhlesur Rahman
Date: 2016-02-25 00:00:00
Tasks: Language Modeling, Language Modelling, Sentence

Word completion and word prediction are two important phenomena in typing
that benefit users who type using keyboard or other similar devices. They can
have profound impact on the typing of disable people. Our work is based on word
prediction on Bangla sentence by using stochastic, i.e. N-gram language model
such as unigram, bigram, trigram, deleted Interpolation and backoff models for
auto completing a sentence by predicting a correct word in a sentence which
saves time and keystrokes of typing and also reduces misspelling. We use large
data corpus of Bangla language of different word types to predict correct word
with the accuracy as much as possible. We have found promising results. We hope
that our work will impact on the baseline for automated Bangla typing.
A Neutrosophic Recommender System for Medical Diagnosis Based on Algebraic Neutrosophic Measures
Authors: Mumtaz Ali, Nguyen Van Minh, Le Hoang Son
Date: 2016-02-25 00:00:00
Tasks: Medical Diagnosis, Recommendation Systems

Neutrosophic set has the ability to handle uncertain, incomplete,
inconsistent, indeterminate information in a more accurate way. In this paper,
we proposed a neutrosophic recommender system to predict the diseases based on
neutrosophic set which includes single-criterion neutrosophic recommender
system (SC-NRS) and multi-criterion neutrosophic recommender system (MC-NRS).
Further, we investigated some algebraic operations of neutrosophic recommender
system such as union, complement, intersection, probabilistic sum, bold sum,
bold intersection, bounded difference, symmetric difference, convex linear sum
of min and max operators, Cartesian product, associativity, commutativity and
distributive. Based on these operations, we studied the algebraic structures
such as lattices, Kleen algebra, de Morgan algebra, Brouwerian algebra, BCK
algebra, Stone algebra and MV algebra. In addition, we introduced several types
of similarity measures based on these algebraic operations and studied some of
their theoretic properties. Moreover, we accomplished a prediction formula
using the proposed algebraic similarity measure. We also proposed a new
algorithm for medical diagnosis based on neutrosophic recommender system.
Finally to check the validity of the proposed methodology, we made experiments
on the datasets Heart, RHC, Breast cancer, Diabetes and DMD. At the end, we
presented the MSE and computational time by comparing the proposed algorithm
with the relevant ones such as ICSM, DSM, CARE, CFMD, as well as other variants
namely Variant 67, Variant 69, and Varian 71 both in tabular and graphical form
to analyze the efficiency and accuracy. Finally we analyzed the strength of all
8 algorithms by ANOVA statistical tool.