pwc-archive/papers-with-abstracts huggingface dataset:
A new humanlike facial attractiveness predictor with cascaded fine-tuning deep learning model
Authors: Jie Xu, Lianwen Jin, Lingyu Liang, Ziyong Feng, Duorui Xie
Date: 2015-11-08 00:00:00
Tasks: Facial Beauty Prediction, Prediction

This paper proposes a deep leaning method to address the challenging facial
attractiveness prediction problem. The method constructs a convolutional neural
network of facial beauty prediction using a new deep cascaded fine-turning
scheme with various face inputting channels, such as the original RGB face
image, the detail layer image, and the lighting layer image. With a carefully
designed CNN model of deep structure, large input size and small convolutional
kernels, we have achieved a high prediction correlation of 0.88. This result
convinces us that the problem of facial attractiveness prediction can be solved
by deep learning approach, and it also shows the important roles of the facial
smoothness, lightness, and color information that were involved in facial
beauty perception, which is consistent with the result of recent psychology
studies. Furthermore, we analyze the high-level features learnt by CNN through
visualization of its hidden layers, and some interesting phenomena were
observed. It is found that the contours and appearance of facial features,
especially eyes and moth, are the most significant facial attributes for facial
attractiveness prediction, which is also consistent with the visual perception
intuition of human.
SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception
Authors: Duorui Xie, Lingyu Liang, Lianwen Jin, Jie Xu, Mengru Li
Date: 2015-11-08 00:00:00
Tasks: Deep Learning

In this paper, a novel face dataset with attractiveness ratings, namely, the
SCUT-FBP dataset, is developed for automatic facial beauty perception. This
dataset provides a benchmark to evaluate the performance of different methods
for facial attractiveness prediction, including the state-of-the-art deep
learning method. The SCUT-FBP dataset contains face portraits of 500 Asian
female subjects with attractiveness ratings, all of which have been verified in
terms of rating distribution, standard deviation, consistency, and
self-consistency. Benchmark evaluations for facial attractiveness prediction
were performed with different combinations of facial geometrical features and
texture features using classical statistical learning methods and the deep
learning method. The best Pearson correlation (0.8187) was achieved by the CNN
model. Thus, the results of our experiments indicate that the SCUT-FBP dataset
provides a reliable benchmark for facial beauty perception.
Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random Forests
Authors: Noam Segev, Maayan Harel, Shie Mannor, Koby Crammer, Ran El-Yaniv
Date: 2015-11-04 00:00:00
Tasks: Transfer Learning

We propose novel model transfer-learning methods that refine a decision
forest model M learned within a "source" domain using a training set sampled
from a "target" domain, assumed to be a variation of the source. We present two
random forest transfer algorithms. The first algorithm searches greedily for
locally optimal modifications of each tree structure by trying to locally
expand or reduce the tree around individual nodes. The second algorithm does
not modify structure, but only the parameter (thresholds) associated with
decision nodes. We also propose to combine both methods by considering an
ensemble that contains the union of the two forests. The proposed methods
exhibit impressive experimental results over a range of problems.
A Chinese POS Decision Method Using Korean Translation Information
Authors: Son-Il Kwak, O-Chol Kown, Chang-Sin Kim, Yong-Il Pak, Gum-Chol Son, Chol-Jun Hwang, Hyon-Chol Kim, Hyok-Chol Sin, Gyong-Il Hyon, Sok-Min Han
Date: 2015-11-08 00:00:00
Tasks: POS, POS Tagging, Translation

In this paper we propose a method that imitates a translation expert using
the Korean translation information and analyse the performance. Korean is good
at tagging than Chinese, so we can use this property in Chinese POS tagging.
A Study of an Modeling Method of T-S fuzzy System Based on Moving Fuzzy Reasoning and Its Application
Authors: Son-Il Kwak, Gang Choe, In-Song Kim, Gyong-Ho Jo, Chol-Jun Hwang
Date: 2015-11-08 00:00:00

To improve the effectiveness of the fuzzy identification, a structure
identification method based on moving rate is proposed for T-S fuzzy model. The
proposed method is called "T-S modeling (or T-S fuzzy identification method)
based on moving rate". First, to improve the shortcomings of existing fuzzy
reasoning methods based on matching degree, the moving rates for s-type, z-type
and trapezoidal membership functions of T-S fuzzy model were defined. Then, the
differences between proposed moving rate and existing matching degree were
explained. Next, the identification method based on moving rate is proposed for
T-S model. Finally, the proposed identification method is applied to the fuzzy
modeling for the precipitation forecast and security situation prediction. Test
results show that the proposed method significantly improves the effectiveness
of fuzzy identification.
A Winner-Take-All Approach to Emotional Neural Networks with Universal Approximation Property
Authors: E. Lotfi
Date: 2015-11-08 00:00:00
Tasks: All, General Classification, Time Series, Time Series Analysis

Here, we propose a brain-inspired winner-take-all emotional neural network
(WTAENN) and prove the universal approximation property for the novel
architecture. WTAENN is a single layered feedforward neural network that
benefits from the excitatory, inhibitory, and expandatory neural connections as
well as the winner-take-all (WTA) competitions in the human brain s nervous
system. The WTA competition increases the information capacity of the model
without adding hidden neurons. The universal approximation capability of the
proposed architecture is illustrated on two example functions, trained by a
genetic algorithm, and then applied to several competing recent and benchmark
problems such as in curve fitting, pattern recognition, classification and
prediction. In particular, it is tested on twelve UCI classification datasets,
a facial recognition problem, three real world prediction problems (2 chaotic
time series of geomagnetic activity indices and wind farm power generation
data), two synthetic case studies with constant and nonconstant noise variance
as well as k-selector and linear programming problems. Results indicate the
general applicability and often superiority of the approach in terms of higher
accuracy and lower model complexity, especially where low computational
complexity is imperative.
Design of an Alarm System for Isfahan Ozone Level based on Artificial Intelligence Predictor Models
Authors: Ehsan Lotfi
Date: 2015-11-08 00:00:00

The ozone level prediction is an important task of air quality agencies of
modern cities. In this paper, we design an ozone level alarm system (OLP) for
Isfahan city and test it through the real word data from 1-1-2000 to 7-6-2011.
We propose a computer based system with three inputs and single output. The
inputs include three sensors of solar ultraviolet (UV), total solar radiation
(TSR) and total ozone (O3). And the output of the system is the predicted O3 of
the next day and the alarm massages. A developed artificial intelligence (AI)
algorithm is applied to determine the output, based on the inputs variables.
For this issue, AI models, including supervised brain emotional learning (BEL),
adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks
(ANNs), are compared in order to find the best model. The simulation of the
proposed system shows that it can be used successfully in prediction of major
cities ozone level.
Max-Sum Diversification, Monotone Submodular Functions and Semi-metric Spaces
Authors: Sepehr Abbasi Zadeh, Mehrdad Ghadiri
Date: 2015-11-07 00:00:00
Tasks: Diversity, Document Summarization

In many applications such as web-based search, document summarization,
facility location and other applications, the results are preferable to be both
representative and diversified subsets of documents. The goal of this study is
to select a good "quality", bounded-size subset of a given set of items, while
maintaining their diversity relative to a semi-metric distance function. This
problem was first studied by Borodin et al\cite{borodin}, but a crucial
property used throughout their proof is the triangle inequality. In this
modified proof, we want to relax the triangle inequality and relate the
approximation ratio of max-sum diversification problem to the parameter of the
relaxed triangle inequality in the normal form of the problem (i.e., a uniform
matroid) and also in an arbitrary matroid.
Symmetry-invariant optimization in deep networks
Authors: Vijay Badrinarayanan, Bamdev Mishra, Roberto Cipolla
Date: 2015-11-05 00:00:00
Tasks: Computational Efficiency, Image Segmentation, Semantic Segmentation

Recent works have highlighted scale invariance or symmetry that is present in
the weight space of a typical deep network and the adverse effect that it has
on the Euclidean gradient based stochastic gradient descent optimization. In
this work, we show that these and other commonly used deep networks, such as
those which use a max-pooling and sub-sampling layer, possess more complex
forms of symmetry arising from scaling based reparameterization of the network
weights. We then propose two symmetry-invariant gradient based weight updates
for stochastic gradient descent based learning. Our empirical evidence based on
the MNIST dataset shows that these updates improve the test performance without
sacrificing the computational efficiency of the weight updates. We also show
the results of training with one of the proposed weight updates on an image
segmentation problem.
Review-Level Sentiment Classification with Sentence-Level Polarity Correction
Authors: Sylvester Olubolu Orimaye, Saadat M. Alhashmi, Eu-Gene Siew, Sang Jung Kang
Date: 2015-11-07 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Sentence, Sentiment Analysis, Sentiment Classification

We propose an effective technique to solving review-level sentiment
classification problem by using sentence-level polarity correction. Our
polarity correction technique takes into account the consistency of the
polarities (positive and negative) of sentences within each product review
before performing the actual machine learning task. While sentences with
inconsistent polarities are removed, sentences with consistent polarities are
used to learn state-of-the-art classifiers. The technique achieved better
results on different types of products reviews and outperforms baseline models
without the correction technique. Experimental results show an average of 82%
F-measure on four different product review domains.
Review of Person Re-identification Techniques
Authors: Mohammad Ali Saghafi, Aini Hussain, Halimah Badioze Zaman, Mohamad Hanif Md Saad
Date: 2015-11-07 00:00:00
Tasks: Person Re-Identification

Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.
A Sparse and Adaptive Prior for Time-Dependent Model Parameters
Authors: Dani Yogatama, Bryan R. Routledge, Noah A. Smith
Date: 2013-10-09 00:00:00
Tasks: Variational Inference

We consider the scenario where the parameters of a probabilistic model are
expected to vary over time. We construct a novel prior distribution that
promotes sparsity and adapts the strength of correlation between parameters at
successive timesteps, based on the data. We derive approximate variational
inference procedures for learning and prediction with this prior. We test the
approach on two tasks: forecasting financial quantities from relevant text, and
modeling language contingent on time-varying financial measurements.
Combining Privileged Information to Improve Context-Aware Recommender Systems
Authors: Camila V. Sundermann, Marcos A. Domingues, Ricardo M. Marcacini, Solange O. Rezende
Date: 2015-11-07 00:00:00
Tasks: Clustering, Recommendation Systems

A recommender system is an information filtering technology which can be used
to predict preference ratings of items (products, services, movies, etc) and/or
to output a ranking of items that are likely to be of interest to the user.
Context-aware recommender systems (CARS) learn and predict the tastes and
preferences of users by incorporating available contextual information in the
recommendation process. One of the major challenges in context-aware
recommender systems research is the lack of automatic methods to obtain
contextual information for these systems. Considering this scenario, in this
paper, we propose to use contextual information from topic hierarchies of the
items (web pages) to improve the performance of context-aware recommender
systems. The topic hierarchies are constructed by an extension of the
LUPI-based Incremental Hierarchical Clustering method that considers three
types of information: traditional bag-of-words (technical information), and the
combination of named entities (privileged information I) with domain terms
(privileged information II). We evaluated the contextual information in four
context-aware recommender systems. Different weights were assigned to each type
of information. The empirical results demonstrated that topic hierarchies with
the combination of the two kinds of privileged information can provide better
recommendations.
Fingertip in the Eye: A cascaded CNN pipeline for the real-time fingertip detection in egocentric videos
Authors: Xiaorui Liu, Yichao Huang, Xin Zhang, Lianwen Jin
Date: 2015-11-07 00:00:00
Tasks: Fingertip Detection

We introduce a new pipeline for hand localization and fingertip detection.
For RGB images captured from an egocentric vision mobile camera, hand and
fingertip detection remains a challenging problem due to factors like
background complexity and hand shape variety. To address these issues
accurately and robustly, we build a large scale dataset named Ego-Fingertip and
propose a bi-level cascaded pipeline of convolutional neural networks, namely,
Attention-based Hand Detector as well as Multi-point Fingertip Detector. The
proposed method significantly tackles challenges and achieves satisfactorily
accurate prediction and real-time performance compared to previous hand and
fingertip detection methods.
Bayesian Dark Knowledge
Authors: Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling
Date: 2015-06-14 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Active Learning, parameter estimation

We consider the problem of Bayesian parameter estimation for deep neural
networks, which is important in problem settings where we may have little data,
and/ or where we need accurate posterior predictive densities, e.g., for
applications involving bandits or active learning. One simple approach to this
is to use online Monte Carlo methods, such as SGLD (stochastic gradient
Langevin dynamics). Unfortunately, such a method needs to store many copies of
the parameters (which wastes memory), and needs to make predictions using many
versions of the model (which wastes time).
  We describe a method for "distilling" a Monte Carlo approximation to the
posterior predictive density into a more compact form, namely a single deep
neural network. We compare to two very recent approaches to Bayesian neural
networks, namely an approach based on expectation propagation [Hernandez-Lobato
and Adams, 2015] and an approach based on variational Bayes [Blundell et al.,
2015]. Our method performs better than both of these, is much simpler to
implement, and uses less computation at test time.
Active Perceptual Similarity Modeling with Auxiliary Information
Authors: Eric Heim, Matthew Berger, Lee Seversky, Milos Hauskrecht
Date: 2015-11-06 00:00:00
Tasks: Active Learning, Triplet

Learning a model of perceptual similarity from a collection of objects is a
fundamental task in machine learning underlying numerous applications. A common
way to learn such a model is from relative comparisons in the form of triplets:
responses to queries of the form "Is object a more similar to b than it is to
c?". If no consideration is made in the determination of which queries to ask,
existing similarity learning methods can require a prohibitively large number
of responses. In this work, we consider the problem of actively learning from
triplets -finding which queries are most useful for learning. Different from
previous active triplet learning approaches, we incorporate auxiliary
information into our similarity model and introduce an active learning scheme
to find queries that are informative for quickly learning both the relevant
aspects of auxiliary data and the directly-learned similarity components.
Compared to prior approaches, we show that we can learn just as effectively
with much fewer queries. For evaluation, we introduce a new dataset of
exhaustive triplet comparisons obtained from humans and demonstrate improved
performance for different types of auxiliary information.
Learning Visual Features from Large Weakly Supervised Data
Authors: Armand Joulin, Laurens van der Maaten, Allan Jabri, Nicolas Vasilache
Date: 2015-11-06 00:00:00
Tasks: Representation Learning, Word Similarity

Convolutional networks trained on large supervised dataset produce visual
features which form the basis for the state-of-the-art in many computer-vision
problems. Further improvements of these visual features will likely require
even larger manually labeled data sets, which severely limits the pace at which
progress can be made. In this paper, we explore the potential of leveraging
massive, weakly-labeled image collections for learning good visual features. We
train convolutional networks on a dataset of 100 million Flickr photos and
captions, and show that these networks produce features that perform well in a
range of vision problems. We also show that the networks appropriately capture
word similarity, and learn correspondences between different languages.
Seven ways to improve example-based single image super resolution
Authors: Radu Timofte, Rasmus Rothe, Luc van Gool
Date: 2015-11-06 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Super-Resolution, Super-Resolution

In this paper we present seven techniques that everybody should know to
improve example-based single image super resolution (SR): 1) augmentation of
data, 2) use of large dictionaries with efficient search structures, 3)
cascading, 4) image self-similarities, 5) back projection refinement, 6)
enhanced prediction by consistency check, and 7) context reasoning. We validate
our seven techniques on standard SR benchmarks (i.e. Set5, Set14, B100) and
methods (i.e. A+, SRCNN, ANR, Zeyde, Yang) and achieve substantial
improvements.The techniques are widely applicable and require no changes or
only minor adjustments of the SR methods. Moreover, our Improved A+ (IA) method
sets new state-of-the-art results outperforming A+ by up to 0.9dB on average
PSNR whilst maintaining a low time complexity.
Deep Kernel Learning
Authors: Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing
Date: 2015-11-06 00:00:00
Tasks: Gaussian Processes

We introduce scalable deep kernels, which combine the structural properties
of deep learning architectures with the non-parametric flexibility of kernel
methods. Specifically, we transform the inputs of a spectral mixture base
kernel with a deep architecture, using local kernel interpolation, inducing
points, and structure exploiting (Kronecker and Toeplitz) algebra for a
scalable kernel representation. These closed-form kernels can be used as
drop-in replacements for standard kernels, with benefits in expressive power
and scalability. We jointly learn the properties of these kernels through the
marginal likelihood of a Gaussian process. Inference and learning cost $O(n)$
for $n$ training points, and predictions cost $O(1)$ per test point. On a large
and diverse collection of applications, including a dataset with 2 million
examples, we show improved performance over scalable Gaussian processes with
flexible kernel learning models, and stand-alone deep architectures.
Learning Optimized Or's of And's
Authors: Tong Wang, Cynthia Rudin
Date: 2015-11-06 00:00:00
Tasks: Diagnostic

Or's of And's (OA) models are comprised of a small number of disjunctions of
conjunctions, also called disjunctive normal form. An example of an OA model is
as follows: If ($x_1 = $ `blue' AND $x_2=$ `middle') OR ($x_1 = $ `yellow'),
then predict $Y=1$, else predict $Y=0$. Or's of And's models have the advantage
of being interpretable to human experts, since they are a set of conditions
that concisely capture the characteristics of a specific subset of data. We
present two optimization-based machine learning frameworks for constructing OA
models, Optimized OA (OOA) and its faster version, Optimized OA with
Approximations (OOAx). We prove theoretical bounds on the properties of
patterns in an OA model. We build OA models as a diagnostic screening tool for
obstructive sleep apnea, that achieves high accuracy with a substantial gain in
interpretability over other methods.
A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas
Authors: Haohan Wang, Bhiksha Raj
Date: 2015-10-16 00:00:00
Tasks: Deep Learning, Time Series, Time Series Analysis

This report will show the history of deep learning evolves. It will trace
back as far as the initial belief of connectionism modelling of brain, and come
back to look at its early stage realization: neural networks. With the
background of neural network, we will gradually introduce how convolutional
neural network, as a representative of deep discriminative models, is developed
from neural networks, together with many practical techniques that can help in
optimization of neural networks. On the other hand, we will also trace back to
see the evolution history of deep generative models, to see how researchers
balance the representation power and computation complexity to reach Restricted
Boltzmann Machine and eventually reach Deep Belief Nets. Further, we will also
look into the development history of modelling time series data with neural
networks. We start with Time Delay Neural Networks and move further to
currently famous model named Recurrent Neural Network and its extension Long
Short Term Memory. We will also briefly look into how to construct deep
recurrent neural networks. Finally, we will conclude this report with some
interesting open-ended questions of deep neural networks.