pwc-archive/papers-with-abstracts huggingface dataset:
Neural Network Approach to Railway Stand Lateral Skew Control
Authors: Peter Mark Benes, Ivo Bukovsky, Matous Cejnek, Jan Kalivoda
Date: 2014-02-28 00:00:00

The paper presents a study of an adaptive approach to lateral skew control
for an experimental railway stand. The preliminary experiments with the real
experimental railway stand and simulations with its 3-D mechanical model,
indicates difficulties of model-based control of the device. Thus, use of
neural networks for identification and control of lateral skew shall be
investigated. This paper focuses on real-data based modeling of the railway
stand by various neural network models, i.e; linear neural unit and quadratic
neural unit architectures. Furthermore, training methods of these neural
architectures as such, real-time-recurrent-learning and a variation of
back-propagation-through-time are examined, accompanied by a discussion of the
produced experimental results.
Exact Post Model Selection Inference for Marginal Screening
Authors: Jason D. Lee, Jonathan E. Taylor
Date: 2014-02-23 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Model Selection, regression, Two-sample testing, valid

We develop a framework for post model selection inference, via marginal
screening, in linear regression. At the core of this framework is a result that
characterizes the exact distribution of linear functions of the response $y$,
conditional on the model being selected (``condition on selection" framework).
This allows us to construct valid confidence intervals and hypothesis tests for
regression coefficients that account for the selection procedure. In contrast
to recent work in high-dimensional statistics, our results are exact
(non-asymptotic) and require no eigenvalue-like assumptions on the design
matrix $X$. Furthermore, the computational cost of marginal regression,
constructing confidence intervals and hypothesis testing is negligible compared
to the cost of linear regression, thus making our methods particularly suitable
for extremely large datasets. Although we focus on marginal screening to
illustrate the applicability of the condition on selection framework, this
framework is much more broadly applicable. We show how to apply the proposed
framework to several other selection procedures including orthogonal matching
pursuit, non-negative least squares, and marginal screening+Lasso.
Signal Recovery from Pooling Representations
Authors: Joan Bruna, Arthur Szlam, Yann Lecun
Date: 2013-11-16 00:00:00
Tasks: regression

In this work we compute lower Lipschitz bounds of $\ell_p$ pooling operators
for $p=1, 2, \infty$ as well as $\ell_p$ pooling operators preceded by
half-rectification layers. These give sufficient conditions for the design of
invertible neural network layers. Numerical experiments on MNIST and image
patches confirm that pooling layers can be inverted with phase recovery
algorithms. Moreover, the regularity of the inverse pooling, controlled by the
lower Lipschitz constant, is empirically verified with a nearest neighbor
regression.
Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch
Authors: Partha Pratim Talukdar, William Cohen
Date: 2013-10-10 00:00:00

Graph-based Semi-supervised learning (SSL) algorithms have been successfully
used in a large number of applications. These methods classify initially
unlabeled nodes by propagating label information over the structure of graph
starting from seed nodes. Graph-based SSL algorithms usually scale linearly
with the number of distinct labels (m), and require O(m) space on each node.
Unfortunately, there exist many applications of practical significance with
very large m over large graphs, demanding better space and time complexity. In
this paper, we propose MAD-SKETCH, a novel graph-based SSL algorithm which
compactly stores label distribution on each node using Count-min Sketch, a
randomized data structure. We present theoretical analysis showing that under
mild conditions, MAD-SKETCH can reduce space complexity at each node from O(m)
to O(log m), and achieve similar savings in time complexity as well. We support
our analysis through experiments on multiple real world datasets. We observe
that MAD-SKETCH achieves similar performance as existing state-of-the-art
graph- based SSL algorithms, while requiring smaller memory footprint and at
the same time achieving up to 10x speedup. We find that MAD-SKETCH is able to
scale to datasets with one million labels, which is beyond the scope of
existing graph- based SSL algorithms.
Bayesian Multi-Scale Optimistic Optimization
Authors: Ziyu Wang, Babak Shakibi, Lin Jin, Nando de Freitas
Date: 2014-02-27 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes, global-optimization

Bayesian optimization is a powerful global optimization technique for
expensive black-box functions. One of its shortcomings is that it requires
auxiliary optimization of an acquisition function at each iteration. This
auxiliary optimization can be costly and very hard to carry out in practice.
Moreover, it creates serious theoretical concerns, as most of the convergence
results assume that the exact optimum of the acquisition function can be found.
In this paper, we introduce a new technique for efficient global optimization
that combines Gaussian process confidence bounds and treed simultaneous
optimistic optimization to eliminate the need for auxiliary optimization of
acquisition functions. The experiments with global optimization benchmarks and
a novel application to automatic information extraction demonstrate that the
resulting technique is more efficient than the two approaches from which it
draws inspiration. Unlike most theoretical analyses of Bayesian optimization
with Gaussian processes, our finite-time convergence rate proofs do not require
exact optimization of an acquisition function. That is, our approach eliminates
the unsatisfactory assumption that a difficult, potentially NP-hard, problem
has to be solved in order to obtain vanishing regret rates.
Marginalizing Corrupted Features
Authors: Laurens van der Maaten, Minmin Chen, Stephen Tyree, Kilian Weinberger
Date: 2014-02-27 00:00:00
Tasks: Bayesian Inference

The goal of machine learning is to develop predictors that generalize well to
test data. Ideally, this is achieved by training on an almost infinitely large
training data set that captures all variations in the data distribution. In
practical learning settings, however, we do not have infinite data and our
predictors may overfit. Overfitting may be combatted, for example, by adding a
regularizer to the training objective or by defining a prior over the model
parameters and performing Bayesian inference. In this paper, we propose a
third, alternative approach to combat overfitting: we extend the training set
with infinitely many artificial training examples that are obtained by
corrupting the original training data. We show that this approach is practical
and efficient for a range of predictors and corruption models. Our approach,
called marginalized corrupted features (MCF), trains robust predictors by
minimizing the expected value of the loss function under the corruption model.
We show empirically on a variety of data sets that MCF classifiers can be
trained efficiently, may generalize substantially better to test data, and are
also more robust to feature deletion at test time.
Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices
Authors: Austin R. Benson, Jason D. Lee, Bartek Rajwa, David F. Gleich
Date: 2014-02-27 00:00:00
Proceeding: NeurIPS 2014 12

Numerous algorithms are used for nonnegative matrix factorization under the
assumption that the matrix is nearly separable. In this paper, we show how to
make these algorithms efficient for data matrices that have many more rows than
columns, so-called "tall-and-skinny matrices". One key component to these
improved methods is an orthogonal matrix transformation that preserves the
separability of the NMF problem. Our final methods need a single pass over the
data matrix and are suitable for streaming, multi-core, and MapReduce
architectures. We demonstrate the efficacy of these algorithms on
terabyte-sized synthetic matrices and real-world matrices from scientific
computing and bioinformatics.
A Parallel Memetic Algorithm to Solve the Vehicle Routing Problem with Time Windows
Authors: Jakub Nalepa, Zbigniew J. Czech
Date: 2014-02-27 00:00:00

This paper presents a parallel memetic algorithm for solving the vehicle
routing problem with time windows (VRPTW). The VRPTW is a well-known NP-hard
discrete optimization problem with two objectives. The main objective is to
minimize the number of vehicles serving customers scattered on the map, and the
second one is to minimize the total distance traveled by the vehicles. Here,
the fleet size is minimized in the first phase of the proposed method using the
parallel heuristic algorithm (PHA), and the traveled distance is minimized in
the second phase by the parallel memetic algorithm (PMA). In both parallel
algorithms, the parallel components co-operate periodically in order to
exchange the best solutions found so far. An extensive experimental study
performed on the Gehring and Homberger's benchmark proves the high convergence
capabilities and robustness of both PHA and PMA. Also, we present the speedup
analysis of the PMA.
Low-Cost Compressive Sensing for Color Video and Depth
Authors: Xin Yuan, Patrick Llull, Xuejun Liao, Jianbo Yang, Guillermo Sapiro, David J. Brady, Lawrence Carin
Date: 2014-02-27 00:00:00
Proceeding: CVPR 2014 6
Tasks: Compressive Sensing, Translation

A simple and inexpensive (low-power and low-bandwidth) modification is made
to a conventional off-the-shelf color video camera, from which we recover
{multiple} color frames for each of the original measured frames, and each of
the recovered frames can be focused at a different depth. The recovery of
multiple frames for each measured frame is made possible via high-speed coding,
manifested via translation of a single coded aperture; the inexpensive
translation is constituted by mounting the binary code on a piezoelectric
device. To simultaneously recover depth information, a {liquid} lens is
modulated at high speed, via a variable voltage. Consequently, during the
aforementioned coding process, the liquid lens allows the camera to sweep the
focus through multiple depths. In addition to designing and implementing the
camera, fast recovery is achieved by an anytime algorithm exploiting the
group-sparsity of wavelet/DCT coefficients.
A continuous-time approach to online optimization
Authors: Joon Kwon, Panayotis Mertikopoulos
Date: 2014-01-27 00:00:00

We consider a family of learning strategies for online optimization problems
that evolve in continuous time and we show that they lead to no regret. From a
more traditional, discrete-time viewpoint, this continuous-time approach allows
us to derive the no-regret properties of a large class of discrete-time
algorithms including as special cases the exponential weight algorithm, online
mirror descent, smooth fictitious play and vanishingly smooth fictitious play.
In so doing, we obtain a unified view of many classical regret bounds, and we
show that they can be decomposed into a term stemming from continuous-time
considerations and a term which measures the disparity between discrete and
continuous time. As a result, we obtain a general class of infinite horizon
learning strategies that guarantee an $\mathcal{O}(n^{-1/2})$ regret bound
without having to resort to a doubling trick.
CriPS: Critical Dynamics in Particle Swarm Optimization
Authors: Adam Erskine, J. Michael Herrmann
Date: 2014-02-27 00:00:00

Particle Swarm Optimisation (PSO) makes use of a dynamical system for solving
a search task. Instead of adding search biases in order to improve performance
in certain problems, we aim to remove algorithm-induced scales by controlling
the swarm with a mechanism that is scale-free except possibly for a suppression
of scales beyond the system size. In this way a very promising performance is
achieved due to the balance of large-scale exploration and local search. The
resulting algorithm shows evidence for self-organised criticality, brought
about via the intrinsic dynamics of the swarm as it interacts with the
objective function, rather than being explicitly specified. The Critical
Particle Swarm (CriPS) can be easily combined with many existing extensions
such as chaotic exploration, additional force terms or non-trivial topologies.
It's distributions all the way down!: Second order changes in statistical distributions also occur
Authors: M. T. Keane, A. Gerow
Date: 2014-02-27 00:00:00
Tasks: All

The textual, big-data literature misses Bentley, OBrien, & Brocks (Bentley et
als) message on distributions; it largely examines the first-order effects of
how a single, signature distribution can predict population behaviour,
neglecting second-order effects involving distributional shifts, either between
signature distributions or within a given signature distribution. Indeed,
Bentley et al. themselves under-emphasise the potential richness of the latter,
within-distribution effects.
Outlier Detection using Improved Genetic K-means
Authors: M. H. Marghny, Ahmed I. Taloba
Date: 2014-02-27 00:00:00
Tasks: Clustering, General Classification, Outlier Detection

The outlier detection problem in some cases is similar to the classification
problem. For example, the main concern of clustering-based outlier detection
algorithms is to find clusters and outliers, which are often regarded as noise
that should be removed in order to make more reliable clustering. In this
article, we present an algorithm that provides outlier detection and data
clustering simultaneously. The algorithmimprovesthe estimation of centroids of
the generative distribution during the process of clustering and outlier
discovery. The proposed algorithm consists of two stages. The first stage
consists of improved genetic k-means algorithm (IGK) process, while the second
stage iteratively removes the vectors which are far from their cluster
centroids.
An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study
Authors: M. H. Marghny, Rasha M. Abd El-Aziz, Ahmed I. Taloba
Date: 2014-02-27 00:00:00
Tasks: Clustering, Missing Values

Clustering analysis plays an important role in scientific research and
commercial application. K-means algorithm is a widely used partition method in
clustering. However, it is known that the K-means algorithm may get stuck at
suboptimal solutions, depending on the choice of the initial cluster centers.
In this article, we propose a technique to handle large scale data, which can
select initial clustering center purposefully using Genetic algorithms (GAs),
reduce the sensitivity to isolated point, avoid dissevering big cluster, and
overcome deflexion of data in some degree that caused by the disproportion in
data partitioning owing to adoption of multi-sampling. We applied our method to
some public datasets these show the advantages of the proposed approach for
example Hepatitis C dataset that has been taken from the machine learning
warehouse of University of California. Our aim is to evaluate hepatitis
dataset. In order to evaluate this dataset we did some preprocessing operation,
the reason to preprocessing is to summarize the data in the best and suitable
way for our algorithm. Missing values of the instances are adjusted using local
mean method.
Delay Learning Architectures for Memory and Classification
Authors: Shaista Hussain, Arindam Basu, R. Wang, Tara Julia Hamilton
Date: 2013-11-06 00:00:00
Tasks: Classification, General Classification

We present a neuromorphic spiking neural network, the DELTRON, that can
remember and store patterns by changing the delays of every connection as
opposed to modifying the weights. The advantage of this architecture over
traditional weight based ones is simpler hardware implementation without
multipliers or digital-analog converters (DACs) as well as being suited to
time-based computing. The name is derived due to similarity in the learning
rule with an earlier architecture called Tempotron. The DELTRON can remember
more patterns than other delay-based networks by modifying a few delays to
remember the most 'salient' or synchronous part of every spike pattern. We
present simulations of memory capacity and classification ability of the
DELTRON for different random spatio-temporal spike patterns. The memory
capacity for noisy spike patterns and missing spikes are also shown. Finally,
we present SPICE simulation results of the core circuits involved in a
reconfigurable mixed signal implementation of this architecture.
Information Evolution in Social Networks
Authors: Lada A. Adamic, Thomas M. Lento, Eytan Adar, Pauline C. Ng
Date: 2014-02-27 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Social networks readily transmit information, albeit with less than perfect
fidelity. We present a large-scale measurement of this imperfect information
copying mechanism by examining the dissemination and evolution of thousands of
memes, collectively replicated hundreds of millions of times in the online
social network Facebook. The information undergoes an evolutionary process that
exhibits several regularities. A meme's mutation rate characterizes the
population distribution of its variants, in accordance with the Yule process.
Variants further apart in the diffusion cascade have greater edit distance, as
would be expected in an iterative, imperfect replication process. Some text
sequences can confer a replicative advantage; these sequences are abundant and
transfer "laterally" between different memes. Subpopulations of the social
network can preferentially transmit a specific variant of a meme if the variant
matches their beliefs or culture. Understanding the mechanism driving change in
diffusing information has important implications for how we interpret and
harness the information that reaches us through our social networks.
Synthesis of Parametric Programs using Genetic Programming and Model Checking
Authors: Gal Katz, Doron Peled
Date: 2014-02-27 00:00:00

Formal methods apply algorithms based on mathematical principles to enhance
the reliability of systems. It would only be natural to try to progress from
verification, model checking or testing a system against its formal
specification into constructing it automatically. Classical algorithmic
synthesis theory provides interesting algorithms but also alarming high
complexity and undecidability results. The use of genetic programming, in
combination with model checking and testing, provides a powerful heuristic to
synthesize programs. The method is not completely automatic, as it is fine
tuned by a user that sets up the specification and parameters. It also does not
guarantee to always succeed and converge towards a solution that satisfies all
the required properties. However, we applied it successfully on quite
nontrivial examples and managed to find solutions to hard programming
challenges, as well as to improve and to correct code. We describe here several
versions of our method for synthesizing sequential and concurrent systems.
A method to identify potential ambiguous Malay words through Ambiguity Attributes mapping: An exploratory Study
Authors: Hazlina Haron, Abdul Azim Abd. Ghani
Date: 2014-02-27 00:00:00
Tasks: Sentence

We describe here a methodology to identify a list of ambiguous Malay words
that are commonly being used in Malay documentations such as Requirement
Specification. We compiled several relevant and appropriate requirement quality
attributes and sentence rules from previous literatures and adopt it to come
out with a set of ambiguity attributes that most suit Malay words. The
extracted Malay ambiguous words (potential) are then being mapped onto the
constructed ambiguity attributes to confirm their vagueness. The list is then
verified by Malay linguist experts. This paper aims to identify a list of
potential ambiguous words in Malay as an attempt to assist writers to avoid
using the vague words while documenting Malay Requirement Specification as well
as to any other related Malay documentation. The result of this study is a list
of 120 potential ambiguous Malay words that could act as guidelines in writing
Malay sentences
Linear Programming for Large-Scale Markov Decision Problems
Authors: Yasin Abbasi-Yadkori, Peter L. Bartlett, Alan Malek
Date: 2014-02-27 00:00:00

We consider the problem of controlling a Markov decision process (MDP) with a
large state space, so as to minimize average cost. Since it is intractable to
compete with the optimal policy for large scale problems, we pursue the more
modest goal of competing with a low-dimensional family of policies. We use the
dual linear programming formulation of the MDP average cost problem, in which
the variable is a stationary distribution over state-action pairs, and we
consider a neighborhood of a low-dimensional subset of the set of stationary
distributions (defined in terms of state-action features) as the comparison
class. We propose two techniques, one based on stochastic convex optimization,
and one based on constraint sampling. In both cases, we give bounds that show
that the performance of our algorithms approaches the best achievable by any
policy in the comparison class. Most importantly, these results depend on the
size of the comparison class, but not on the size of the state space.
Preliminary experiments show the effectiveness of the proposed algorithms in a
queuing application.
Why Are You More Engaged? Predicting Social Engagement from Word Use
Authors: Jalal Mahmud, Jilin Chen, Jeffrey Nichols
Date: 2014-02-26 00:00:00

We present a study to analyze how word use can predict social engagement
behaviors such as replies and retweets in Twitter. We compute psycholinguistic
category scores from word usage, and investigate how people with different
scores exhibited different reply and retweet behaviors on Twitter. We also
found psycholinguistic categories that show significant correlations with such
social engagement behaviors. In addition, we have built predictive models of
replies and retweets from such psycholinguistic category based features. Our
experiments using a real world dataset collected from Twitter validates that
such predictions can be done with reasonable accuracy.
Enaction-Based Artificial Intelligence: Toward Coevolution with Humans in the Loop
Authors: Pierre De Loor, Kristen Manach, Jacques Tisseau
Date: 2014-02-26 00:00:00

This article deals with the links between the enaction paradigm and
artificial intelligence. Enaction is considered a metaphor for artificial
intelligence, as a number of the notions which it deals with are deemed
incompatible with the phenomenal field of the virtual. After explaining this
stance, we shall review previous works regarding this issue in terms of
artifical life and robotics. We shall focus on the lack of recognition of
co-evolution at the heart of these approaches. We propose to explicitly
integrate the evolution of the environment into our approach in order to refine
the ontogenesis of the artificial system, and to compare it with the enaction
paradigm. The growing complexity of the ontogenetic mechanisms to be activated
can therefore be compensated by an interactive guidance system emanating from
the environment. This proposition does not however resolve that of the
relevance of the meaning created by the machine (sense-making). Such
reflections lead us to integrate human interaction into this environment in
order to construct relevant meaning in terms of participative artificial
intelligence. This raises a number of questions with regards to setting up an
enactive interaction. The article concludes by exploring a number of issues,
thereby enabling us to associate current approaches with the principles of
morphogenesis, guidance, the phenomenology of interactions and the use of
minimal enactive interfaces in setting up experiments which will deal with the
problem of artificial intelligence in a variety of enaction-based ways.