pwc-archive/papers-with-abstracts huggingface dataset:
Localized Complexities for Transductive Learning
Authors: Ilya Tolstikhin, Gilles Blanchard, Marius Kloft
Date: 2014-11-26 00:00:00
Tasks: Learning Theory, Transductive Learning

We show two novel concentration inequalities for suprema of empirical
processes when sampling without replacement, which both take the variance of
the functions into account. While these inequalities may potentially have broad
applications in learning theory in general, we exemplify their significance by
studying the transductive setting of learning theory. For which we provide the
first excess risk bounds based on the localized complexity of the hypothesis
class, which can yield fast rates of convergence also in the transductive
learning setting. We give a preliminary analysis of the localized complexities
for the prominent case of kernel classes.
How Crossover Speeds Up Building-Block Assembly in Genetic Algorithms
Authors: Dirk Sudholt
Date: 2014-03-26 00:00:00
Tasks: Evolutionary Algorithms

We re-investigate a fundamental question: how effective is crossover in
Genetic Algorithms in combining building blocks of good solutions? Although
this has been discussed controversially for decades, we are still lacking a
rigorous and intuitive answer. We provide such answers for royal road functions
and OneMax, where every bit is a building block. For the latter we show that
using crossover makes every ($\mu$+$\lambda$) Genetic Algorithm at least twice
as fast as the fastest evolutionary algorithm using only standard bit mutation,
up to small-order terms and for moderate $\mu$ and $\lambda$. Crossover is
beneficial because it effectively turns fitness-neutral mutations into
improvements by combining the right building blocks at a later stage. Compared
to mutation-based evolutionary algorithms, this makes multi-bit mutations more
useful. Introducing crossover changes the optimal mutation rate on OneMax from
$1/n$ to $(1+\sqrt{5})/2 \cdot 1/n \approx 1.618/n$. This holds both for
uniform crossover and $k$-point crossover. Experiments and statistical tests
confirm that our findings apply to a broad class of building-block functions.
A Fuzzy Syllogistic Reasoning Schema for Generalized Quantifiers
Authors: M. Pereira-Fariña, Juan. C. Vidal, F. Díaz-Hermida, A. Bugarín
Date: 2014-11-26 00:00:00

In this paper, a new approximate syllogistic reasoning schema is described
that expands some of the approaches expounded in the literature into two ways:
(i) a number of different types of quantifiers (logical, absolute,
proportional, comparative and exception) taken from Theory of Generalized
Quantifiers and similarity quantifiers, taken from statistics, are considered
and (ii) any number of premises can be taken into account within the reasoning
process. Furthermore, a systematic reasoning procedure to solve the syllogism
is also proposed, interpreting it as an equivalent mathematical optimization
problem, where the premises constitute the constraints of the searching space
for the quantifier in the conclusion.
Linking GloVe with word2vec
Authors: Tianze Shi, Zhiyuan Liu
Date: 2014-11-20 00:00:00

The Global Vectors for word representation (GloVe), introduced by Jeffrey
Pennington et al. is reported to be an efficient and effective method for
learning vector representations of words. State-of-the-art performance is also
provided by skip-gram with negative-sampling (SGNS) implemented in the word2vec
tool. In this note, we explain the similarities between the training objectives
of the two models, and show that the objective of SGNS is similar to the
objective of a specialized form of GloVe, though their cost functions are
defined differently.
Real time Detection of Lane Markers in Urban Streets
Authors: Mohamed Aly
Date: 2014-11-26 00:00:00

We present a robust and real time approach to lane marker detection in urban
streets. It is based on generating a top view of the road, filtering using
selective oriented Gaussian filters, using RANSAC line fitting to give initial
guesses to a new and fast RANSAC algorithm for fitting Bezier Splines, which is
then followed by a post-processing step. Our algorithm can detect all lanes in
still images of the street in various conditions, while operating at a rate of
50 Hz and achieving comparable results to previous techniques.
Structured Stochastic Variational Inference
Authors: Matthew D. Hoffman, David M. Blei
Date: 2014-04-16 00:00:00
Tasks: Sensitivity, Stochastic Optimization, Variational Inference

Stochastic variational inference makes it possible to approximate posterior
distributions induced by large datasets quickly using stochastic optimization.
The algorithm relies on the use of fully factorized variational distributions.
However, this "mean-field" independence approximation limits the fidelity of
the posterior approximation, and introduces local optima. We show how to relax
the mean-field approximation to allow arbitrary dependencies between global
parameters and local hidden variables, producing better parameter estimates by
reducing bias, sensitivity to local optima, and sensitivity to hyperparameters.
A Generative Product-of-Filters Model of Audio
Authors: Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore
Date: 2013-12-20 00:00:00
Tasks: model, Speaker Identification

We propose the product-of-filters (PoF) model, a generative model that
decomposes audio spectra as sparse linear combinations of "filters" in the
log-spectral domain. PoF makes similar assumptions to those used in the classic
homomorphic filtering approach to signal processing, but replaces hand-designed
decompositions built of basic signal processing operations with a learned
decomposition based on statistical inference. This paper formulates the PoF
model and derives a mean-field method for posterior inference and a variational
EM algorithm to estimate the model's free parameters. We demonstrate PoF's
potential for audio processing on a bandwidth expansion task, and show that PoF
can serve as an effective unsupervised feature extractor for a speaker
identification task.
Power-Law Graph Cuts
Authors: Xiangyang Zhou, Jiaxin Zhang, Brian Kulis
Date: 2014-10-29 00:00:00
Tasks: Clustering, Image Segmentation, Semantic Segmentation

Algorithms based on spectral graph cut objectives such as normalized cuts,
ratio cuts and ratio association have become popular in recent years because
they are widely applicable and simple to implement via standard eigenvector
computations. Despite strong performance for a number of clustering tasks,
spectral graph cut algorithms still suffer from several limitations: first,
they require the number of clusters to be known in advance, but this
information is often unknown a priori; second, they tend to produce clusters
with uniform sizes. In some cases, the true clusters exhibit a known size
distribution; in image segmentation, for instance, human-segmented images tend
to yield segment sizes that follow a power-law distribution. In this paper, we
propose a general framework of power-law graph cut algorithms that produce
clusters whose sizes are power-law distributed, and also does not fix the
number of clusters upfront. To achieve our goals, we treat the Pitman-Yor
exchangeable partition probability function (EPPF) as a regularizer to graph
cut objectives. Because the resulting objectives cannot be solved by relaxing
via eigenvectors, we derive a simple iterative algorithm to locally optimize
the objectives. Moreover, we show that our proposed algorithm can be viewed as
performing MAP inference on a particular Pitman-Yor mixture model. Our
experiments on various data sets show the effectiveness of our algorithms.
The Utility of Text: The Case of Amicus Briefs and the Supreme Court
Authors: Yanchuan Sim, Bryan Routledge, Noah A. Smith
Date: 2014-09-29 00:00:00
Tasks: counterfactual

We explore the idea that authoring a piece of text is an act of maximizing
one's expected utility. To make this idea concrete, we consider the societally
important decisions of the Supreme Court of the United States. Extensive past
work in quantitative political science provides a framework for empirically
modeling the decisions of justices and how they relate to text. We incorporate
into such a model texts authored by amici curiae ("friends of the court"
separate from the litigants) who seek to weigh in on the decision, then
explicitly model their goals in a random utility model. We demonstrate the
benefits of this approach in improved vote prediction and the ability to
perform counterfactual analysis.
Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data
Authors: Guy Van den Broeck, Karthika Mohan, Arthur Choi, Judea Pearl
Date: 2014-11-25 00:00:00

We propose an efficient family of algorithms to learn the parameters of a
Bayesian network from incomplete data. In contrast to textbook approaches such
as EM and the gradient method, our approach is non-iterative, yields closed
form parameter estimates, and eliminates the need for inference in a Bayesian
network. Our approach provides consistent parameter estimates for missing data
problems that are MCAR, MAR, and in some cases, MNAR. Empirically, our approach
is orders of magnitude faster than EM (as our approach requires no inference).
Given sufficient data, we learn parameters that can be orders of magnitude more
accurate.
PLUTO: Penalized Unbiased Logistic Regression Trees
Authors: Wenwen Zhang, Wei-Yin Loh
Date: 2014-11-25 00:00:00
Tasks: regression, Selection bias, Variable Selection

We propose a new algorithm called PLUTO for building logistic regression
trees to binary response data. PLUTO can capture the nonlinear and interaction
patterns in messy data by recursively partitioning the sample space. It fits a
simple or a multiple linear logistic regression model in each partition. PLUTO
employs the cyclical coordinate descent method for estimation of multiple
linear logistic regression models with elastic net penalties, which allows it
to deal with high-dimensional data efficiently. The tree structure comprises a
graphical description of the data. Together with the logistic regression
models, it provides an accurate classifier as well as a piecewise smooth
estimate of the probability of "success". PLUTO controls selection bias by: (1)
separating split variable selection from split point selection; (2) applying an
adjusted chi-squared test to find the split variable instead of exhaustive
search. A bootstrap calibration technique is employed to further correct
selection bias. Comparison on real datasets shows that on average, the multiple
linear PLUTO models predict more accurately than other algorithms.
Short-Term Memory Through Persistent Activity: Evolution of Self-Stopping and Self-Sustaining Activity in Spiking Neural Networks
Authors: Julien Hubert, Takashi Ikegami
Date: 2014-11-25 00:00:00

Memories in the brain are separated in two categories: short-term and
long-term memories. Long-term memories remain for a lifetime, while short-term
ones exist from a few milliseconds to a few minutes. Within short-term memory
studies, there is debate about what neural structure could implement it.
Indeed, mechanisms responsible for long-term memories appear inadequate for the
task. Instead, it has been proposed that short-term memories could be sustained
by the persistent activity of a group of neurons. In this work, we explore what
topology could sustain short-term memories, not by designing a model from
specific hypotheses, but through Darwinian evolution in order to obtain new
insights into its implementation. We evolved 10 networks capable of retaining
information for a fixed duration between 2 and 11s. Our main finding has been
that the evolution naturally created two functional modules in the network: one
which sustains the information containing primarily excitatory neurons, while
the other, which is responsible for forgetting, was composed mainly of
inhibitory neurons. This demonstrates how the balance between inhibition and
excitation plays an important role in cognition.
Image Classification and Retrieval from User-Supplied Tags
Authors: Hamid Izadinia, Ali Farhadi, Aaron Hertzmann, Matthew D. Hoffman
Date: 2014-11-25 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Retrieval, Robust classification, TAG

This paper proposes direct learning of image classification from
user-supplied tags, without filtering. Each tag is supplied by the user who
shared the image online. Enormous numbers of these tags are freely available
online, and they give insight about the image categories important to users and
to image classification. Our approach is complementary to the conventional
approach of manual annotation, which is extremely costly. We analyze of the
Flickr 100 Million Image dataset, making several useful observations about the
statistics of these tags. We introduce a large-scale robust classification
algorithm, in order to handle the inherent noise in these tags, and a
calibration procedure to better predict objective annotations. We show that
freely available, user-supplied tags can obtain similar or superior results to
large databases of costly manual annotations.
Learning Sets with Separating Kernels
Authors: Ernesto De Vito, Lorenzo Rosasco, Alessandro Toigo
Date: 2012-04-16 00:00:00

We consider the problem of learning a set from random samples. We show how
relevant geometric and topological properties of a set can be studied
analytically using concepts from the theory of reproducing kernel Hilbert
spaces. A new kind of reproducing kernel, that we call separating kernel, plays
a crucial role in our study and is analyzed in detail. We prove a new analytic
characterization of the support of a distribution, that naturally leads to a
family of provably consistent regularized learning algorithms and we discuss
the stability of these methods with respect to random sampling. Numerical
experiments show that the approach is competitive, and often better, than other
state of the art techniques.
Design, Implementation and Simulation of a Cloud Computing System for Enhancing Real-time Video Services by using VANET and Onboard Navigation Systems
Authors: Karim Hammoudi, Nabil Ajam, Mohamed Kasraoui, Fadi Dornaika, Karan Radhakrishnan, Karthik Bandi, Qing Cai, Sai Liu
Date: 2014-11-25 00:00:00
Tasks: Cloud Computing

In this paper, we propose a design for novel and experimental cloud computing
systems. The proposed system aims at enhancing computational, communicational
and annalistic capabilities of road navigation services by merging several
independent technologies, namely vision-based embedded navigation systems,
prominent Cloud Computing Systems (CCSs) and Vehicular Ad-hoc NETwork (VANET).
This work presents our initial investigations by describing the design of a
global generic system. The designed system has been experimented with various
scenarios of video-based road services. Moreover, the associated architecture
has been implemented on a small-scale simulator of an in-vehicle embedded
system. The implemented architecture has been experimented in the case of a
simulated road service to aid the police agency. The goal of this service is to
recognize and track searched individuals and vehicles in a real-time monitoring
system remotely connected to moving cars. The presented work demonstrates the
potential of our system for efficiently enhancing and diversifying real-time
video services in road environments.
Similarity- based approach for outlier detection
Authors: Amina Dik, Khalid Jebari, Abdelaziz Bouroumi, Aziz Ettouhami
Date: 2014-11-25 00:00:00
Tasks: Outlier Detection

This paper presents a new approach for detecting outliers by introducing the
notion of object's proximity. The main idea is that normal point has similar
characteristics with several neighbors. So the point in not an outlier if it
has a high degree of proximity and its neighbors are several. The performance
of this approach is illustrated through real datasets
Orientation covariant aggregation of local descriptors with embeddings
Authors: Giorgos Tolias, Teddy Furon, Hervé Jégou
Date: 2014-07-08 00:00:00
Tasks: Image Retrieval, Retrieval

Image search systems based on local descriptors typically achieve orientation
invariance by aligning the patches on their dominant orientations. Albeit
successful, this choice introduces too much invariance because it does not
guarantee that the patches are rotated consistently. This paper introduces an
aggregation strategy of local descriptors that achieves this covariance
property by jointly encoding the angle in the aggregation stage in a continuous
manner. It is combined with an efficient monomial embedding to provide a
codebook-free method to aggregate local descriptors into a single vector
representation. Our strategy is also compatible and employed with several
popular encoding methods, in particular bag-of-words, VLAD and the Fisher
vector. Our geometric-aware aggregation strategy is effective for image search,
as shown by experiments performed on standard benchmarks for image and
particular object retrieval, namely Holidays and Oxford buildings.
Some Reflections on the Set-based and the Conditional-based Interpretations of Statements in Syllogistic Reasoning
Authors: M. Pereira-Fariña
Date: 2014-11-25 00:00:00

Two interpretations about syllogistic statements are described in this paper.
One is the so-called set-based interpretation, which assumes that quantified
statements and syllogisms talk about quantity-relationships between sets. The
other one, the so-called conditional interpretation, assumes that quantified
propositions talk about conditional propositions and how strong are the links
between the antecedent and the consequent. Both interpretations are compared
attending to three different questions (existential import, singular statements
and non-proportional quantifiers) from the point of view of their impact on the
further development of this type of reasoning.
Hypotheses of neural code and the information model of the neuron-detector
Authors: Yuri Parzhin
Date: 2014-11-25 00:00:00

This paper deals with the problem of neural code solving. On the basis of the
formulated hypotheses the information model of a neuron-detector is suggested,
the detector being one of the basic elements of an artificial neural network
(ANN). The paper subjects the connectionist paradigm of ANN building to
criticism and suggests a new presentation paradigm for ANN building and
neuroelements (NE) learning. The adequacy of the suggested model is proved by
the fact that is does not contradict the modern propositions of neuropsychology
and neurophysiology.
Hardware-Amenable Structural Learning for Spike-based Pattern Classification using a Simple Model of Active Dendrites
Authors: Shaista Hussain, Shih-Chii Liu, Arindam Basu
Date: 2014-11-20 00:00:00
Tasks: Binary Classification, General Classification

This paper presents a spike-based model which employs neurons with
functionally distinct dendritic compartments for classifying high dimensional
binary patterns. The synaptic inputs arriving on each dendritic subunit are
nonlinearly processed before being linearly integrated at the soma, giving the
neuron a capacity to perform a large number of input-output mappings. The model
utilizes sparse synaptic connectivity; where each synapse takes a binary value.
The optimal connection pattern of a neuron is learned by using a simple
hardware-friendly, margin enhancing learning algorithm inspired by the
mechanism of structural plasticity in biological neurons. The learning
algorithm groups correlated synaptic inputs on the same dendritic branch. Since
the learning results in modified connection patterns, it can be incorporated
into current event-based neuromorphic systems with little overhead. This work
also presents a branch-specific spike-based version of this structural
plasticity rule. The proposed model is evaluated on benchmark binary
classification problems and its performance is compared against that achieved
using Support Vector Machine (SVM) and Extreme Learning Machine (ELM)
techniques. Our proposed method attains comparable performance while utilizing
10 to 50% less computational resources than the other reported techniques.
HCRS: A hybrid clothes recommender system based on user ratings and product features
Authors: Xiaosong Hu, Wen Zhu, Qing Li
Date: 2014-11-25 00:00:00
Tasks: Recommendation Systems

Nowadays, online clothes-selling business has become popular and extremely
attractive because of its convenience and cheap-and-fine price. Good examples
of these successful Web sites include Yintai.com, Vancl.com and
Shop.vipshop.com which provide thousands of clothes for online shoppers. The
challenge for online shoppers lies on how to find a good product from lots of
options. In this article, we propose a collaborative clothes recommender for
easy shopping. One of the unique features of this system is the ability to
recommend clothes in terms of both user ratings and clothing attributes.
Experiments in our simulation environment show that the proposed recommender
can better satisfy the needs of users.
Accelerated Parallel Optimization Methods for Large Scale Machine Learning
Authors: Haipeng Luo, Patrick Haffner, Jean-Francois Paiement
Date: 2014-11-25 00:00:00
Tasks: BIG-bench Machine Learning

The growing amount of high dimensional data in different machine learning
applications requires more efficient and scalable optimization algorithms. In
this work, we consider combining two techniques, parallelism and Nesterov's
acceleration, to design faster algorithms for L1-regularized loss. We first
simplify BOOM, a variant of gradient descent, and study it in a unified
framework, which allows us to not only propose a refined measurement of
sparsity to improve BOOM, but also show that BOOM is provably slower than
FISTA. Moving on to parallel coordinate descent methods, we then propose an
efficient accelerated version of Shotgun, improving the convergence rate from
$O(1/t)$ to $O(1/t^2)$. Our algorithm enjoys a concise form and analysis
compared to previous work, and also allows one to study several connected work
in a unified way.
Detecting fraudulent activity in a cloud using privacy-friendly data aggregates
Authors: Marc Solanas, Julio Hernandez-Castro, Debojyoti Dutta
Date: 2014-11-25 00:00:00

More users and companies make use of cloud services every day. They all
expect a perfect performance and any issue to remain transparent to them. This
last statement is very challenging to perform. A user's activities in our cloud
can affect the overall performance of our servers, having an impact on other
resources. We can consider these kind of activities as fraudulent. They can be
either illegal activities, such as launching a DDoS attack or just activities
which are undesired by the cloud provider, such as Bitcoin mining, which uses
substantial power, reduces the life of the hardware and can possibly slow down
other user's activities. This article discusses a method to detect such
activities by using non-intrusive, privacy-friendly data: billing data. We use
OpenStack as an example with data provided by Telemetry, the component in
charge of measuring resource usage for billing purposes. Results will be shown
proving the efficiency of this method and ways to improve it will be provided
as well as its advantages and disadvantages.