pwc-archive/papers-with-abstracts huggingface dataset:
Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU
Authors: Zeyuan Allen-Zhu, Yuanzhi Li
Date: 2017-01-06 00:00:00
Proceeding: ICML 2017 8

The online problem of computing the top eigenvector is fundamental to machine
learning. In both adversarial and stochastic settings, previous results (such
as matrix multiplicative weight update, follow the regularized leader, follow
the compressed leader, block power method) either achieve optimal regret but
run slow, or run fast at the expense of loosing a $\sqrt{d}$ factor in total
regret where $d$ is the matrix dimension.
  We propose a $\textit{follow-the-compressed-leader (FTCL)}$ framework which
achieves optimal regret without sacrificing the running time. Our idea is to
"compress" the matrix strategy to dimension 3 in the adversarial setting, or
dimension 1 in the stochastic setting. These respectively resolve two open
questions regarding the design of optimal and efficient algorithms for the
online eigenvector problem.
Deep Fruit Detection in Orchards
Authors: Suchet Bargoti, James Underwood
Date: 2016-10-12 00:00:00
Tasks: Data Augmentation, object-detection, Object Detection

An accurate and reliable image based fruit detection system is critical for
supporting higher level agriculture tasks such as yield mapping and robotic
harvesting. This paper presents the use of a state-of-the-art object detection
framework, Faster R-CNN, in the context of fruit detection in orchards,
including mangoes, almonds and apples. Ablation studies are presented to better
understand the practical deployment of the detection network, including how
much training data is required to capture variability in the dataset. Data
augmentation techniques are shown to yield significant performance gains,
resulting in a greater than two-fold reduction in the number of training images
required. In contrast, transferring knowledge between orchards contributed to
negligible performance gain over initialising the Deep Convolutional Neural
Network directly from ImageNet features. Finally, to operate over orchard data
containing between 100-1000 fruit per image, a tiling approach is introduced
for the Faster R-CNN framework. The study has resulted in the best yet
detection performance for these orchards relative to previous works, with an
F1-score of >0.9 achieved for apples and mangoes.
Adversarial Dropout for Supervised and Semi-supervised Learning
Authors: Sungrae Park, Jun-Keon Park, Su-Jin Shin, Il-Chul Moon
Date: 2017-07-12 00:00:00

Recently, the training with adversarial examples, which are generated by
adding a small but worst-case perturbation on input examples, has been proved
to improve generalization performance of neural networks. In contrast to the
individually biased inputs to enhance the generality, this paper introduces
adversarial dropout, which is a minimal set of dropouts that maximize the
divergence between the outputs from the network with the dropouts and the
training supervisions. The identified adversarial dropout are used to
reconfigure the neural network to train, and we demonstrated that training on
the reconfigured sub-network improves the generalization performance of
supervised and semi-supervised learning tasks on MNIST and CIFAR-10. We
analyzed the trained model to reason the performance improvement, and we found
that adversarial dropout increases the sparsity of neural networks more than
the standard dropout does.
A Hierarchical Probabilistic Model for Facial Feature Detection
Authors: Yue Wu, Ziheng Wang, Qiang Ji
Date: 2017-09-18 00:00:00
Proceeding: CVPR 2014 6
Tasks: model, parameter estimation

Facial feature detection from facial images has attracted great attention in
the field of computer vision. It is a nontrivial task since the appearance and
shape of the face tend to change under different conditions. In this paper, we
propose a hierarchical probabilistic model that could infer the true locations
of facial features given the image measurements even if the face is with
significant facial expression and pose. The hierarchical model implicitly
captures the lower level shape variations of facial components using the
mixture model. Furthermore, in the higher level, it also learns the joint
relationship among facial components, the facial expression, and the pose
information through automatic structure learning and parameter estimation of
the probabilistic model. Experimental results on benchmark databases
demonstrate the effectiveness of the proposed hierarchical probabilistic model.
Facial Feature Tracking under Varying Facial Expressions and Face Poses based on Restricted Boltzmann Machines
Authors: Yue Wu, Zuoguan Wang, Qiang Ji
Date: 2017-09-18 00:00:00
Proceeding: CVPR 2013 6

Facial feature tracking is an active area in computer vision due to its
relevance to many applications. It is a nontrivial task, since faces may have
varying facial expressions, poses or occlusions. In this paper, we address this
problem by proposing a face shape prior model that is constructed based on the
Restricted Boltzmann Machines (RBM) and their variants. Specifically, we first
construct a model based on Deep Belief Networks to capture the face shape
variations due to varying facial expressions for near-frontal view. To handle
pose variations, the frontal face shape prior model is incorporated into a
3-way RBM model that could capture the relationship between frontal face shapes
and non-frontal face shapes. Finally, we introduce methods to systematically
combine the face shape prior models with image measurements of facial feature
points. Experiments on benchmark databases show that with the proposed method,
facial feature points can be tracked robustly and accurately even if faces have
significant facial expressions and poses.
Flexible Computing Services for Comparisons and Analyses of Classical Chinese Poetry
Authors: Chao-Lin Liu
Date: 2017-09-18 00:00:00

We collect nine corpora of representative Chinese poetry for the time span of
1046 BCE and 1644 CE for studying the history of Chinese words, collocations,
and patterns. By flexibly integrating our own tools, we are able to provide new
perspectives for approaching our goals. We illustrate the ideas with two
examples. The first example show a new way to compare word preferences of
poets, and the second example demonstrates how we can utilize our corpora in
historical studies of the Chinese words. We show the viability of the tools for
academic research, and we wish to make it helpful for enriching existing
Chinese dictionary as well.
FlashProfile: A Framework for Synthesizing Data Profiles
Authors: Saswat Padhi, Prateek Jain, Daniel Perelman, Oleksandr Polozov, Sumit Gulwani, Todd Millstein
Date: 2017-09-17 00:00:00
Tasks: Clustering

We address the problem of learning a syntactic profile for a collection of
strings, i.e. a set of regex-like patterns that succinctly describe the
syntactic variations in the strings. Real-world datasets, typically curated
from multiple sources, often contain data in various syntactic formats. Thus,
any data processing task is preceded by the critical step of data format
identification. However, manual inspection of data to identify the different
formats is infeasible in standard big-data scenarios.
  Prior techniques are restricted to a small set of pre-defined patterns (e.g.
digits, letters, words, etc.), and provide no control over granularity of
profiles. We define syntactic profiling as a problem of clustering strings
based on syntactic similarity, followed by identifying patterns that succinctly
describe each cluster. We present a technique for synthesizing such profiles
over a given language of patterns, that also allows for interactive refinement
by requesting a desired number of clusters.
  Using a state-of-the-art inductive synthesis framework, PROSE, we have
implemented our technique as FlashProfile. Across $153$ tasks over $75$ large
real datasets, we observe a median profiling time of only $\sim\,0.7\,$s.
Furthermore, we show that access to syntactic profiles may allow for more
accurate synthesis of programs, i.e. using fewer examples, in
programming-by-example (PBE) workflows such as FlashFill.
Beyond Evolutionary Algorithms for Search-based Software Engineering
Authors: Jianfeng Chen, Vivek Nair, Tim Menzies
Date: 2017-01-27 00:00:00
Tasks: Clustering, Evolutionary Algorithms

Context: Evolutionary algorithms typically require a large number of
evaluations (of solutions) to converge - which can be very slow and expensive
to evaluate.Objective: To solve search-based software engineering (SE)
problems, using fewer evaluations than evolutionary methods.Method: Instead of
mutating a small population, we build a very large initial population which is
then culled using a recursive bi-clustering chop approach. We evaluate this
approach on multiple SE models, unconstrained as well as constrained, and
compare its performance with standard evolutionary algorithms. Results: Using
just a few evaluations (under 100), we can obtain comparable results to
state-of-the-art evolutionary algorithms.Conclusion: Just because something
works, and is widespread use, does not necessarily mean that there is no value
in seeking methods to improve that method. Before undertaking search-based SE
optimization tasks using traditional EAs, it is recommended to try other
techniques, like those explored here, to obtain the same results with fewer
evaluations.
Nonparametric Shape-restricted Regression
Authors: Adityanand Guntuboyina, Bodhisattva Sen
Date: 2017-09-17 00:00:00
Tasks: regression

We consider the problem of nonparametric regression under shape constraints.
The main examples include isotonic regression (with respect to any partial
order), unimodal/convex regression, additive shape-restricted regression, and
constrained single index model. We review some of the theoretical properties of
the least squares estimator (LSE) in these problems, emphasizing on the
adaptive nature of the LSE. In particular, we study the behavior of the risk of
the LSE, and its pointwise limiting distribution theory, with special emphasis
to isotonic regression. We survey various methods for constructing pointwise
confidence intervals around these shape-restricted functions. We also briefly
discuss the computation of the LSE and indicate some open research problems and
future directions.
AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms
Authors: Kory Becker, Justin Gottschlich
Date: 2017-09-17 00:00:00

In this paper, we present the first-of-its-kind machine learning (ML) system,
called AI Programmer, that can automatically generate full software programs
requiring only minimal human guidance. At its core, AI Programmer uses genetic
algorithms (GA) coupled with a tightly constrained programming language that
minimizes the overhead of its ML search space. Part of AI Programmer's novelty
stems from (i) its unique system design, including an embedded, hand-crafted
interpreter for efficiency and security and (ii) its augmentation of GAs to
include instruction-gene randomization bindings and programming
language-specific genome construction and elimination techniques. We provide a
detailed examination of AI Programmer's system design, several examples
detailing how the system works, and experimental data demonstrating its
software generation capabilities and performance using only mainstream CPUs.
Morphology-based Entity and Relational Entity Extraction Framework for Arabic
Authors: Amin Jaber, Fadi A. Zaraket
Date: 2017-09-17 00:00:00
Tasks: Entity Extraction using GAN, Morphological Analysis, TAG

Rule-based techniques to extract relational entities from documents allow
users to specify desired entities with natural language questions, finite state
automata, regular expressions and structured query language. They require
linguistic and programming expertise and lack support for Arabic morphological
analysis. We present a morphology-based entity and relational entity extraction
framework for Arabic (MERF). MERF requires basic knowledge of linguistic
features and regular expressions, and provides the ability to interactively
specify Arabic morphological and synonymity features, tag types associated with
regular expressions, and relations and code actions defined over matches of
subexpressions. MERF constructs entities and relational entities from matches
of the specifications. We evaluated MERF with several case studies. The results
show that MERF requires shorter development time and effort compared to
existing application specific techniques and produces reasonably accurate
results within a reasonable overhead in run time.
Ranking Based Locality Sensitive Hashing Enabled Cancelable Biometrics: Index-of-Max Hashing
Authors: Zhe Jin, Yen-Lung Lai, Jung-Yeon Hwang, Soo-Hyung Kim, Andrew Beng Jin Teoh
Date: 2017-03-16 00:00:00

In this paper, we propose a ranking based locality sensitive hashing inspired
two-factor cancelable biometrics, dubbed "Index-of-Max" (IoM) hashing for
biometric template protection. With externally generated random parameters, IoM
hashing transforms a real-valued biometric feature vector into discrete index
(max ranked) hashed code. We demonstrate two realizations from IoM hashing
notion, namely Gaussian Random Projection based and Uniformly Random
Permutation based hashing schemes. The discrete indices representation nature
of IoM hashed codes enjoy serveral merits. Firstly, IoM hashing empowers strong
concealment to the biometric information. This contributes to the solid ground
of non-invertibility guarantee. Secondly, IoM hashing is insensitive to the
features magnitude, hence is more robust against biometric features variation.
Thirdly, the magnitude-independence trait of IoM hashing makes the hash codes
being scale-invariant, which is critical for matching and feature alignment.
The experimental results demonstrate favorable accuracy performance on
benchmark FVC2002 and FVC2004 fingerprint databases. The analyses justify its
resilience to the existing and newly introduced security and privacy attacks as
well as satisfy the revocability and unlinkability criteria of cancelable
biometrics.
A Categorical Approach for Recognizing Emotional Effects of Music
Authors: Mohsen Sahraei Ardakani, Ehsan Arbabi
Date: 2017-09-17 00:00:00
Tasks: Retrieval, TAG

Recently, digital music libraries have been developed and can be plainly
accessed. Latest research showed that current organization and retrieval of
music tracks based on album information are inefficient. Moreover, they
demonstrated that people use emotion tags for music tracks in order to search
and retrieve them. In this paper, we discuss separability of a set of emotional
labels, proposed in the categorical emotion expression, using Fisher's
separation theorem. We determine a set of adjectives to tag music parts: happy,
sad, relaxing, exciting, epic and thriller. Temporal, frequency and energy
features have been extracted from the music parts. It could be seen that the
maximum separability within the extracted features occurs between relaxing and
epic music parts. Finally, we have trained a classifier using Support Vector
Machines to automatically recognize and generate emotional labels for a music
part. Accuracy for recognizing each label has been calculated; where the
results show that epic music can be recognized more accurately (77.4%),
comparing to the other types of music.
Organizing Multimedia Data in Video Surveillance Systems Based on Face Verification with Convolutional Neural Networks
Authors: Anastasiia D. Sokolova, Angelina S. Kharchevnikova, Andrey V. Savchenko
Date: 2017-09-17 00:00:00
Tasks: Clustering, Face Verification

In this paper we propose the two-stage approach of organizing information in
video surveillance systems. At first, the faces are detected in each frame and
a video stream is split into sequences of frames with face region of one
person. Secondly, these sequences (tracks) that contain identical faces are
grouped using face verification algorithms and hierarchical agglomerative
clustering. Gender and age are estimated for each cluster (person) in order to
facilitate the usage of the organized video collection. The particular
attention is focused on the aggregation of features extracted from each frame
with the deep convolutional neural networks. The experimental results of the
proposed approach using YTF and IJB-A datasets demonstrated that the most
accurate and fast solution is achieved for matching of normalized average of
feature vectors of all frames in a track.
Neural Affine Grayscale Image Denoising
Authors: Sungmin Cha, Taesup Moon
Date: 2017-09-17 00:00:00
Tasks: Denoising, Grayscale Image Denoising, Image Denoising

We propose a new grayscale image denoiser, dubbed as Neural Affine Image
Denoiser (Neural AIDE), which utilizes neural network in a novel way. Unlike
other neural network based image denoising methods, which typically apply
simple supervised learning to learn a mapping from a noisy patch to a clean
patch, we formulate to train a neural network to learn an \emph{affine} mapping
that gets applied to a noisy pixel, based on its context. Our formulation
enables both supervised training of the network from the labeled training
dataset and adaptive fine-tuning of the network parameters using the given
noisy image subject to denoising. The key tool for devising Neural AIDE is to
devise an estimated loss function of the MSE of the affine mapping, solely
based on the noisy data. As a result, our algorithm can outperform most of the
recent state-of-the-art methods in the standard benchmark datasets. Moreover,
our fine-tuning method can nicely overcome one of the drawbacks of the
patch-level supervised learning methods in image denoising; namely, a
supervised trained model with a mismatched noise variance can be mostly
corrected as long as we have the matched noise variance during the fine-tuning
step.
An Improved Fatigue Detection System Based on Behavioral Characteristics of Driver
Authors: Rajat Gupta, Kanishk Aman, Nalin Shiva, Yadvendra Singh
Date: 2017-09-17 00:00:00

In recent years, road accidents have increased significantly. One of the
major reasons for these accidents, as reported is driver fatigue. Due to
continuous and longtime driving, the driver gets exhausted and drowsy which may
lead to an accident. Therefore, there is a need for a system to measure the
fatigue level of driver and alert him when he/she feels drowsy to avoid
accidents. Thus, we propose a system which comprises of a camera installed on
the car dashboard. The camera detect the driver's face and observe the
alteration in its facial features and uses these features to observe the
fatigue level. Facial features include eyes and mouth. Principle Component
Analysis is thus implemented to reduce the features while minimizing the amount
of information lost. The parameters thus obtained are processed through Support
Vector Classifier for classifying the fatigue level. After that classifier
output is sent to the alert unit.
Bayesian nonparametric Principal Component Analysis
Authors: Clément Elvira, Pierre Chainais, Nicolas Dobigeon
Date: 2017-09-17 00:00:00
Tasks: Clustering, Dimensionality Reduction

Principal component analysis (PCA) is very popular to perform dimension
reduction. The selection of the number of significant components is essential
but often based on some practical heuristics depending on the application. Only
few works have proposed a probabilistic approach able to infer the number of
significant components. To this purpose, this paper introduces a Bayesian
nonparametric principal component analysis (BNP-PCA). The proposed model
projects observations onto a random orthogonal basis which is assigned a prior
distribution defined on the Stiefel manifold. The prior on factor scores
involves an Indian buffet process to model the uncertainty related to the
number of components. The parameters of interest as well as the nuisance
parameters are finally inferred within a fully Bayesian framework via Monte
Carlo sampling. A study of the (in-)consistence of the marginal maximum a
posteriori estimator of the latent dimension is carried out. A new estimator of
the subspace dimension is proposed. Moreover, for sake of statistical
significance, a Kolmogorov-Smirnov test based on the posterior distribution of
the principal components is used to refine this estimate.
  The behaviour of the algorithm is first studied on various synthetic
examples. Finally, the proposed BNP dimension reduction approach is shown to be
easily yet efficiently coupled with clustering or latent factor models within a
unique framework.
On Inductive Abilities of Latent Factor Models for Relational Learning
Authors: Théo Trouillon, Éric Gaussier, Christopher R. Dance, Guillaume Bouchard
Date: 2017-09-17 00:00:00
Tasks: Knowledge Graphs, Relational Reasoning

Latent factor models are increasingly popular for modeling multi-relational
knowledge graphs. By their vectorial nature, it is not only hard to interpret
why this class of models works so well, but also to understand where they fail
and how they might be improved. We conduct an experimental survey of
state-of-the-art models, not towards a purely comparative end, but as a means
to get insight about their inductive abilities. To assess the strengths and
weaknesses of each model, we create simple tasks that exhibit first, atomic
properties of binary relations, and then, common inter-relational inference
through synthetic genealogies. Based on these experimental results, we propose
new research directions to improve on existing models.
Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation
Authors: Markus Wulfmeier, Alex Bewley, Ingmar Posner
Date: 2017-03-04 00:00:00
Tasks: Autonomous Driving, Domain Adaptation, Motion Planning, Unsupervised Domain Adaptation

Appearance changes due to weather and seasonal conditions represent a strong
impediment to the robust implementation of machine learning systems in outdoor
robotics. While supervised learning optimises a model for the training domain,
it will deliver degraded performance in application domains that underlie
distributional shifts caused by these changes. Traditionally, this problem has
been addressed via the collection of labelled data in multiple domains or by
imposing priors on the type of shift between both domains. We frame the problem
in the context of unsupervised domain adaptation and develop a framework for
applying adversarial techniques to adapt popular, state-of-the-art network
architectures with the additional objective to align features across domains.
Moreover, as adversarial training is notoriously unstable, we first perform an
extensive ablation study, adapting many techniques known to stabilise
generative adversarial networks, and evaluate on a surrogate classification
task with the same appearance change. The distilled insights are applied to the
problem of free-space segmentation for motion planning in autonomous driving.
Two-Dimensional Indirect Binary Search for the Positive One-in-Three Satisfiability Problem
Authors: Shunichi Matsubara
Date: 2017-08-28 00:00:00

In this paper, we propose an algorithm for the positive one-in-three
satisfiability problem (Pos1in3SAT). The proposed algorithm can efficiently
decide the existence of a satisfying assignment in all assignments for a given
formula by using a 2-dimensional binary search method without constructing an
exponential number of assignments.