pwc-archive/papers-with-abstracts huggingface dataset:
Knowledge reduction of dynamic covering decision information systems with varying attribute values
Authors: Mingjie Cai
Date: 2015-04-12 00:00:00
Tasks: Attribute

Knowledge reduction of dynamic covering information systems involves with the
time in practical situations. In this paper, we provide incremental approaches
to computing the type-1 and type-2 characteristic matrices of dynamic coverings
because of varying attribute values. Then we present incremental algorithms of
constructing the second and sixth approximations of sets by using
characteristic matrices. We employ experimental results to illustrate that the
incremental approaches are effective to calculate approximations of sets in
dynamic covering information systems. Finally, we perform knowledge reduction
of dynamic covering information systems with the incremental approaches.
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
Authors: Yu-Xiang Wang, Stephen E. Fienberg, Alex Smola
Date: 2015-02-26 00:00:00

We consider the problem of Bayesian learning on sensitive datasets and
present two simple but somewhat surprising results that connect Bayesian
learning to "differential privacy:, a cryptographic approach to protect
individual-level privacy while permiting database-level utility. Specifically,
we show that that under standard assumptions, getting one single sample from a
posterior distribution is differentially private "for free". We will see that
estimator is statistically consistent, near optimal and computationally
tractable whenever the Bayesian model of interest is consistent, optimal and
tractable. Similarly but separately, we show that a recent line of works that
use stochastic gradient for Hybrid Monte Carlo (HMC) sampling also preserve
differentially privacy with minor or no modifications of the algorithmic
procedure at all, these observations lead to an "anytime" algorithm for
Bayesian learning under privacy constraint. We demonstrate that it performs
much better than the state-of-the-art differential private methods on synthetic
and real datasets.
The entropic barrier: a simple and optimal universal self-concordant barrier
Authors: Sébastien Bubeck, Ronen Eldan
Date: 2014-12-04 00:00:00

We prove that the Cram\'er transform of the uniform measure on a convex body
in $\mathbb{R}^n$ is a $(1+o(1)) n$-self-concordant barrier, improving a
seminal result of Nesterov and Nemirovski. This gives the first explicit
construction of a universal barrier for convex bodies with optimal
self-concordance parameter. The proof is based on basic geometry of log-concave
distributions, and elementary duality in exponential families.
Automatic Discovery and Optimization of Parts for Image Classification
Authors: Sobhan Naderi Parizi, Andrea Vedaldi, Andrew Zisserman, Pedro Felzenszwalb
Date: 2014-12-20 00:00:00
Tasks: Classification, Diversity, General Classification, image-classification, Image Classification, L2 Regularization

Part-based representations have been shown to be very useful for image
classification. Learning part-based models is often viewed as a two-stage
problem. First, a collection of informative parts is discovered, using
heuristics that promote part distinctiveness and diversity, and then
classifiers are trained on the vector of part responses. In this paper we unify
the two stages and learn the image classifiers and a set of shared parts
jointly. We generate an initial pool of parts by randomly sampling part
candidates and selecting a good subset using L1/L2 regularization. All steps
are driven "directly" by the same objective namely the classification loss on a
training set. This lets us do away with engineered heuristics. We also
introduce the notion of "negative parts", intended as parts that are negatively
correlated with one or more classes. Negative parts are complementary to the
parts discovered by other methods, which look only for positive correlations.
Gradual Training Method for Denoising Auto Encoders
Authors: Alexander Kalmanovich, Gal Chechik
Date: 2015-04-11 00:00:00
Tasks: Denoising, General Classification

Stacked denoising auto encoders (DAEs) are well known to learn useful deep
representations, which can be used to improve supervised training by
initializing a deep network. We investigate a training scheme of a deep DAE,
where DAE layers are gradually added and keep adapting as additional layers are
added. We show that in the regime of mid-sized datasets, this gradual training
provides a small but consistent improvement over stacked training in both
reconstruction quality and classification error over stacked training on MNIST
and CIFAR datasets.
On the Convergence Properties of Optimal AdaBoost
Authors: Joshua Belanich, Luis E. Ortiz
Date: 2012-12-05 00:00:00
Tasks: BIG-bench Machine Learning

AdaBoost is one of the most popular ML algorithms. It is simple to implement and often found very effective by practitioners, while still being mathematically elegant and theoretically sound. AdaBoost's interesting behavior in practice still puzzles the ML community. We address the algorithm's stability and establish multiple convergence properties of "Optimal AdaBoost," a term coined by Rudin, Daubechies, and Schapire in 2004. We prove, in a reasonably strong computational sense, the almost universal existence of time averages, and with that, the convergence of the classifier itself, its generalization error, and its resulting margins, among many other objects, for fixed data sets under arguably reasonable conditions. Specifically, we frame Optimal AdaBoost as a dynamical system and, employing tools from ergodic theory, prove that, under a condition that Optimal AdaBoost does not have ties for best weak classifier eventually, a condition for which we provide empirical evidence from high dimensional real-world datasets, the algorithm's update behaves like a continuous map. We provide constructive proofs of several arbitrarily accurate approximations of Optimal AdaBoost; prove that they exhibit certain cycling behavior in finite time, and that the resulting dynamical system is ergodic; and establish sufficient conditions for the same to hold for the actual Optimal-AdaBoost update. We believe that our results provide reasonably strong evidence for the affirmative answer to two open conjectures, at least from a broad computational-theory perspective: AdaBoost always cycles and is an ergodic dynamical system. We present empirical evidence that cycles are hard to detect while time averages stabilize quickly. Our results ground future convergence-rate analysis and may help optimize generalization ability and alleviate a practitioner's burden of deciding how long to run the algorithm.
Prediction of Search Targets From Fixations in Open-World Settings
Authors: Hosnieh Sattar, Sabine Müller, Mario Fritz, Andreas Bulling
Date: 2015-02-18 00:00:00
Proceeding: CVPR 2015 6

Previous work on predicting the target of visual search from human fixations
only considered closed-world settings in which training labels are available
and predictions are performed for a known set of potential targets. In this
work we go beyond the state of the art by studying search target prediction in
an open-world setting in which we no longer assume that we have fixation data
to train for the search targets. We present a dataset containing fixation data
of 18 users searching for natural images from three image categories within
synthesised image collages of about 80 images. In a closed-world baseline
experiment we show that we can predict the correct target image out of a
candidate set of five images. We then present a new problem formulation for
search target prediction in the open-world setting that is based on learning
compatibilities between fixations and potential targets.
Quantitative Analysis of Whether Machine Intelligence Can Surpass Human Intelligence
Authors: Feng Liu, Yong Shi
Date: 2015-04-11 00:00:00

Whether the machine intelligence can surpass the human intelligence is a
controversial issue. On the basis of traditional IQ, this article presents the
Universal IQ test method suitable for both the machine intelligence and the
human intelligence. With the method, machine and human intelligences were
divided into 4 major categories and 15 subcategories. A total of 50 search
engines across the world and 150 persons at different ages were subject to the
relevant test. And then, the Universal IQ ranking list of 2014 for the test
objects was obtained.
End-to-End Photo-Sketch Generation via Fully Convolutional Representation Learning
Authors: Liliang Zhang, Liang Lin, Xian Wu, Shengyong Ding, Lei Zhang
Date: 2015-01-28 00:00:00
Tasks: Face Recognition, Representation Learning

Sketch-based face recognition is an interesting task in vision and multimedia
research, yet it is quite challenging due to the great difference between face
photos and sketches. In this paper, we propose a novel approach for
photo-sketch generation, aiming to automatically transform face photos into
detail-preserving personal sketches. Unlike the traditional models synthesizing
sketches based on a dictionary of exemplars, we develop a fully convolutional
network to learn the end-to-end photo-sketch mapping. Our approach takes whole
face photos as inputs and directly generates the corresponding sketch images
with efficient inference and learning, in which the architecture are stacked by
only convolutional kernels of very small sizes. To well capture the person
identity during the photo-sketch transformation, we define our optimization
objective in the form of joint generative-discriminative minimization. In
particular, a discriminative regularization term is incorporated into the
photo-sketch generation, enhancing the discriminability of the generated person
sketches against other individuals. Extensive experiments on several standard
benchmarks suggest that our approach outperforms other state-of-the-art methods
in both photo-sketch generation and face sketch verification.
Data Science and Ebola
Authors: Aske Plaat
Date: 2015-04-11 00:00:00

Data Science---Today, everybody and everything produces data. People produce
large amounts of data in social networks and in commercial transactions.
Medical, corporate, and government databases continue to grow. Sensors continue
to get cheaper and are increasingly connected, creating an Internet of Things,
and generating even more data. In every discipline, large, diverse, and rich
data sets are emerging, from astrophysics, to the life sciences, to the
behavioral sciences, to finance and commerce, to the humanities and to the
arts. In every discipline people want to organize, analyze, optimize and
understand their data to answer questions and to deepen insights. The science
that is transforming this ocean of data into a sea of knowledge is called data
science. This lecture will discuss how data science has changed the way in
which one of the most visible challenges to public health is handled, the 2014
Ebola outbreak in West Africa.
Towards radio astronomical imaging using an arbitrary basis
Authors: Matthias Petschow
Date: 2015-03-14 00:00:00
Tasks: Image Reconstruction

The new generation of radio telescopes, such as the Square Kilometer Array
(SKA), requires dramatic advances in computer hardware and software, in order
to process the large amounts of produced data efficiently. In this document, we
explore a new approach to wide-field imaging. By generalizing the image
reconstruction, which is performed by an inverse Fourier transform, to
arbitrary transformations, we gain enormous new possibilities. In particular,
we outline an approach that might allow to obtain a sky image of size P times Q
in (optimal) O(PQ) time. This could be a step in the direction of real-time,
wide-field sky imaging for future telescopes.
Quick sensitivity analysis for incremental data modification and its application to leave-one-out CV in linear classification problems
Authors: Shota Okumura, Yoshiki Suzuki, Ichiro Takeuchi
Date: 2015-04-11 00:00:00
Tasks: General Classification, Incremental Learning, Sensitivity

We introduce a novel sensitivity analysis framework for large scale
classification problems that can be used when a small number of instances are
incrementally added or removed. For quickly updating the classifier in such a
situation, incremental learning algorithms have been intensively studied in the
literature. Although they are much more efficient than solving the optimization
problem from scratch, their computational complexity yet depends on the entire
training set size. It means that, if the original training set is large,
completely solving an incremental learning problem might be still rather
expensive. To circumvent this computational issue, we propose a novel framework
that allows us to make an inference about the updated classifier without
actually re-optimizing it. Specifically, the proposed framework can quickly
provide a lower and an upper bounds of a quantity on the unknown updated
classifier. The main advantage of the proposed framework is that the
computational cost of computing these bounds depends only on the number of
updated instances. This property is quite advantageous in a typical sensitivity
analysis task where only a small number of instances are updated. In this paper
we demonstrate that the proposed framework is applicable to various practical
sensitivity analysis tasks, and the bounds provided by the framework are often
sufficiently tight for making desired inferences.
Appearance-Based Gaze Estimation in the Wild
Authors: Xucong Zhang, Yusuke Sugano, Mario Fritz, Andreas Bulling
Date: 2015-04-11 00:00:00
Proceeding: CVPR 2015 6
Tasks: Gaze Estimation

Appearance-based gaze estimation is believed to work well in real-world
settings, but existing datasets have been collected under controlled laboratory
conditions and methods have been not evaluated across multiple datasets. In
this work we study appearance-based gaze estimation in the wild. We present the
MPIIGaze dataset that contains 213,659 images we collected from 15 participants
during natural everyday laptop use over more than three months. Our dataset is
significantly more variable than existing ones with respect to appearance and
illumination. We also present a method for in-the-wild appearance-based gaze
estimation using multimodal convolutional neural networks that significantly
outperforms state-of-the art methods in the most challenging cross-dataset
evaluation. We present an extensive evaluation of several state-of-the-art
image-based gaze estimation algorithms on three current datasets, including our
own. This evaluation provides clear insights and allows us to identify key
research challenges of gaze estimation in the wild.
High Density Noise Removal by Cascading Algorithms
Authors: Arabinda Dash, Sujaya Kumar Sathua
Date: 2015-04-11 00:00:00
Tasks: Image Enhancement, Vocal Bursts Intensity Prediction

An advanced non-linear cascading filter algorithm for the removal of high
density salt and pepper noise from the digital images is proposed. The proposed
method consists of two stages. The first stage Decision base Median Filter
(DMF) acts as the preliminary noise removal algorithm. The second stage is
either Modified Decision Base Partial Trimmed Global Mean Filter (MDBPTGMF) or
Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) which is
used to remove the remaining noise and enhance the image quality. The DMF
algorithm performs well at low noise density but it fails to remove the noise
at medium and high level. The MDBPTGMF and MDUTMF have excellent performance at
low, medium and high noise density but these reduce the image quality and blur
the image at high noise level. So the basic idea behind this paper is to
combine the advantages of the filters used in both the stages to remove the
Salt and Pepper noise and enhance the image quality at all the noise density
level. The proposed method is tested against different gray scale images and it
gives better Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR) and
Image Enhancement Factor (IEF) than the Adaptive Median Filter (AMF), Decision
Base Unsymmetric Trimmed Median Filter (DBUTMF), Modified Decision Base
Unsymmetric Trimmed Median Filter (MDBUTMF) and Decision Base Partial Trimmed
Global Mean Filter (DBPTGMF).
siftservice.com - Turning a Computer Vision algorithm into a World Wide Web Service
Authors: Ahmad Pahlavan Tafti, Hamid Hassannia, Zeyun Yu
Date: 2015-04-11 00:00:00
Tasks: Image Forgery Detection, Image Registration, Object Tracking, Surface Reconstruction

Image features detection and description is a longstanding topic in computer
vision and pattern recognition areas. The Scale Invariant Feature Transform
(SIFT) is probably the most popular and widely demanded feature descriptor
which facilitates a variety of computer vision applications such as image
registration, object tracking, image forgery detection, and 3D surface
reconstruction. This work introduces a Software as a Service (SaaS) based
implementation of the SIFT algorithm which is freely available at
http://siftservice.com for any academic, educational and research purposes. The
service provides application-to-application interaction and aims Rapid
Application Development (RAD) and also fast prototyping for computer vision
students and researchers all around the world. An Internet connection is all
they need!
Simple Image Description Generator via a Linear Phrase-Based Approach
Authors: Remi Lebret, Pedro O. Pinheiro, Ronan Collobert
Date: 2014-12-29 00:00:00
Tasks: Descriptive, Image Description, Language Modeling, Language Modelling

Generating a novel textual description of an image is an interesting problem
that connects computer vision and natural language processing. In this paper,
we present a simple model that is able to generate descriptive sentences given
a sample image. This model has a strong focus on the syntax of the
descriptions. We train a purely bilinear model that learns a metric between an
image representation (generated from a previously trained Convolutional Neural
Network) and phrases that are used to described them. The system is then able
to infer phrases from a given image sample. Based on caption syntax statistics,
we propose a simple language model that can produce relevant descriptions for a
given test image using the phrases inferred. Our approach, which is
considerably simpler than state-of-the-art models, achieves comparable results
on the recently release Microsoft COCO dataset.
Attention for Fine-Grained Categorization
Authors: Pierre Sermanet, Andrea Frome, Esteban Real
Date: 2014-12-22 00:00:00

This paper presents experiments extending the work of Ba et al. (2014) on
recurrent neural models for attention into less constrained visual
environments, specifically fine-grained categorization on the Stanford Dogs
data set. In this work we use an RNN of the same structure but substitute a
more powerful visual network and perform large-scale pre-training of the visual
network outside of the attention RNN. Most work in attention models to date
focuses on tasks with toy or more constrained visual environments, whereas we
present results for fine-grained categorization better than the
state-of-the-art GoogLeNet classification model. We show that our model learns
to direct high resolution attention to the most discriminative regions without
any spatial supervision such as bounding boxes, and it is able to discriminate
fine-grained dog breeds moderately well even when given only an initial
low-resolution context image and narrow, inexpensive glimpses at faces and fur
patterns. This and similar attention models have the major advantage of being
trained end-to-end, as opposed to other current detection and recognition
pipelines with hand-engineered components where information is lost. While our
model is state-of-the-art, further work is needed to fully leverage the
sequential input.
Joint RNN-Based Greedy Parsing and Word Composition
Authors: Joël Legrand, Ronan Collobert
Date: 2014-12-22 00:00:00
Tasks: TAG

This paper introduces a greedy parser based on neural networks, which
leverages a new compositional sub-tree representation. The greedy parser and
the compositional procedure are jointly trained, and tightly depends on
each-other. The composition procedure outputs a vector representation which
summarizes syntactically (parsing tags) and semantically (words) sub-trees.
Composition and tagging is achieved over continuous (word or tag)
representations, and recurrent neural networks. We reach F1 performance on par
with well-known existing parsers, while having the advantage of speed, thanks
to the greedy nature of the parser. We provide a fully functional
implementation of the method described in this paper.
High-Dimensional Classification for Brain Decoding
Authors: Nicole Croteau, Farouk S. Nathoo, Jiguo Cao, Ryan Budney
Date: 2015-04-10 00:00:00
Tasks: Brain Decoding, Classification, General Classification, Vocal Bursts Intensity Prediction

Brain decoding involves the determination of a subject's cognitive state or
an associated stimulus from functional neuroimaging data measuring brain
activity. In this setting the cognitive state is typically characterized by an
element of a finite set, and the neuroimaging data comprise voluminous amounts
of spatiotemporal data measuring some aspect of the neural signal. The
associated statistical problem is one of classification from high-dimensional
data. We explore the use of functional principal component analysis, mutual
information networks, and persistent homology for examining the data through
exploratory analysis and for constructing features characterizing the neural
signal for brain decoding. We review each approach from this perspective, and
we incorporate the features into a classifier based on symmetric multinomial
logistic regression with elastic net regularization. The approaches are
illustrated in an application where the task is to infer, from brain activity
measured with magnetoencephalography (MEG), the type of video stimulus shown to
a subject.
An Analysis of Unsupervised Pre-training in Light of Recent Advances
Authors: Tom Le Paine, Pooya Khorrami, Wei Han, Thomas S. Huang
Date: 2014-12-20 00:00:00
Tasks: Data Augmentation, Image Classification, Object Recognition, Unsupervised Pre-training

Convolutional neural networks perform well on object recognition because of a
number of recent advances: rectified linear units (ReLUs), data augmentation,
dropout, and large labelled datasets. Unsupervised data has been proposed as
another way to improve performance. Unfortunately, unsupervised pre-training is
not used by state-of-the-art methods leading to the following question: Is
unsupervised pre-training still useful given recent advances? If so, when? We
answer this in three parts: we 1) develop an unsupervised method that
incorporates ReLUs and recent unsupervised regularization techniques, 2)
analyze the benefits of unsupervised pre-training compared to data augmentation
and dropout on CIFAR-10 while varying the ratio of unsupervised to supervised
samples, 3) verify our findings on STL-10. We discover unsupervised
pre-training, as expected, helps when the ratio of unsupervised to supervised
samples is high, and surprisingly, hurts when the ratio is low. We also use
unsupervised pre-training with additional color augmentation to achieve near
state-of-the-art performance on STL-10.