pwc-archive/papers-with-abstracts huggingface dataset:
Setpoint Tracking with Partially Observed Loads
Authors: Antoine Lesage-Landry, Joshua A. Taylor
Date: 2017-09-12 00:00:00

We use online convex optimization (OCO) for setpoint tracking with uncertain,
flexible loads. We consider full feedback from the loads, bandit feedback, and
two intermediate types of feedback: partial bandit where a subset of the loads
are individually observed and the rest are observed in aggregate, and Bernoulli
feedback where in each round the aggregator receives either full or bandit
feedback according to a known probability. We give sublinear regret bounds in
all cases. We numerically evaluate our algorithms on examples with
thermostatically controlled loads and electric vehicles.
Accurate Genomic Prediction Of Human Height
Authors: Louis Lello, Steven G. Avery, Laurent Tellier, Ana Vazquez, Gustavo de los Campos, Stephen D. H. Hsu
Date: 2017-09-19 00:00:00
Tasks: Prediction

We construct genomic predictors for heritable and extremely complex human
quantitative traits (height, heel bone density, and educational attainment)
using modern methods in high dimensional statistics (i.e., machine learning).
Replication tests show that these predictors capture, respectively, $\sim$40,
20, and 9 percent of total variance for the three traits. For example,
predicted heights correlate $\sim$0.65 with actual height; actual heights of
most individuals in validation samples are within a few cm of the prediction.
The variance captured for height is comparable to the estimated SNP
heritability from GCTA (GREML) analysis, and seems to be close to its
asymptotic value (i.e., as sample size goes to infinity), suggesting that we
have captured most of the heritability for the SNPs used. Thus, our results
resolve the common SNP portion of the "missing heritability" problem -- i.e.,
the gap between prediction R-squared and SNP heritability. The $\sim$20k
activated SNPs in our height predictor reveal the genetic architecture of human
height, at least for common SNPs. Our primary dataset is the UK Biobank cohort,
comprised of almost 500k individual genotypes with multiple phenotypes. We also
use other datasets and SNPs found in earlier GWAS for out-of-sample validation
of our results.
Image operator learning coupled with CNN classification and its application to staff line removal
Authors: Frank D. Julca-Aguilar, Nina S. T. Hirata
Date: 2017-09-19 00:00:00
Tasks: General Classification, Operator learning

Many image transformations can be modeled by image operators that are
characterized by pixel-wise local functions defined on a finite support window.
In image operator learning, these functions are estimated from training data
using machine learning techniques. Input size is usually a critical issue when
using learning algorithms, and it limits the size of practicable windows. We
propose the use of convolutional neural networks (CNNs) to overcome this
limitation. The problem of removing staff-lines in music score images is chosen
to evaluate the effects of window and convolutional mask sizes on the learned
image operator performance. Results show that the CNN based solution
outperforms previous ones obtained using conventional learning algorithms or
heuristic algorithms, indicating the potential of CNNs as base classifiers in
image operator learning. The implementations will be made available on the
TRIOSlib project site.
Automated Cloud Provisioning on AWS using Deep Reinforcement Learning
Authors: Zhiguang Wang, Chul Gwon, Tim Oates, Adam Iezzi
Date: 2017-09-13 00:00:00
Tasks: Cloud Computing, Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

As the use of cloud computing continues to rise, controlling cost becomes
increasingly important. Yet there is evidence that 30\% - 45\% of cloud spend
is wasted. Existing tools for cloud provisioning typically rely on highly
trained human experts to specify what to monitor, thresholds for triggering
action, and actions. In this paper we explore the use of reinforcement learning
(RL) to acquire policies to balance performance and spend, allowing humans to
specify what they want as opposed to how to do it, minimizing the need for
cloud expertise. Empirical results with tabular, deep, and dueling double deep
Q-learning with the CloudSim simulator show the utility of RL and the relative
merits of the approaches. We also demonstrate effective policy transfer
learning from an extremely simple simulator to CloudSim, with the next step
being transfer from CloudSim to an Amazon Web Services physical environment.
Listen to Your Face: Inferring Facial Action Units from Audio Channel
Authors: Zibo Meng, Shizhong Han, Yan Tong
Date: 2017-06-23 00:00:00

Extensive efforts have been devoted to recognizing facial action units (AUs).
However, it is still challenging to recognize AUs from spontaneous facial
displays especially when they are accompanied with speech. Different from all
prior work that utilized visual observations for facial AU recognition, this
paper presents a novel approach that recognizes speech-related AUs exclusively
from audio signals based on the fact that facial activities are highly
correlated with voice during speech. Specifically, dynamic and physiological
relationships between AUs and phonemes are modeled through a continuous time
Bayesian network (CTBN); then AU recognition is performed by probabilistic
inference via the CTBN model.
  A pilot audiovisual AU-coded database has been constructed to evaluate the
proposed audio-based AU recognition framework. The database consists of a
"clean" subset with frontal and neutral faces and a challenging subset
collected with large head movements and occlusions. Experimental results on
this database show that the proposed CTBN model achieves promising recognition
performance for 7 speech-related AUs and outperforms the state-of-the-art
visual-based methods especially for those AUs that are activated at low
intensities or "hardly visible" in the visual channel. Furthermore, the CTBN
model yields more impressive recognition performance on the challenging subset,
where the visual-based approaches suffer significantly.
3D Reconstruction with Low Resolution, Small Baseline and High Radial Distortion Stereo Images
Authors: Tiago Dias, Helder Araujo, Pedro Miraldo
Date: 2017-09-19 00:00:00
Tasks: 3D Reconstruction

In this paper we analyze and compare approaches for 3D reconstruction from
low-resolution (250x250), high radial distortion stereo images, which are
acquired with small baseline (approximately 1mm). These images are acquired
with the system NanEye Stereo manufactured by CMOSIS/AWAIBA. These stereo
cameras have also small apertures, which means that high levels of illumination
are required. The goal was to develop an approach yielding accurate
reconstructions, with a low computational cost, i.e., avoiding non-linear
numerical optimization algorithms. In particular we focused on the analysis and
comparison of radial distortion models. To perform the analysis and comparison,
we defined a baseline method based on available software and methods, such as
the Bouguet toolbox [2] or the Computer Vision Toolbox from Matlab. The
approaches tested were based on the use of the polynomial model of radial
distortion, and on the application of the division model. The issue of the
center of distortion was also addressed within the framework of the application
of the division model. We concluded that the division model with a single
radial distortion parameter has limitations.
Human Activity Recognition Using Robust Adaptive Privileged Probabilistic Learning
Authors: Michalis Vrigkas, Evangelos Kazakos, Christophoros Nikou, Ioannis A. Kakadiaris
Date: 2017-09-19 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

In this work, a novel method based on the learning using privileged
information (LUPI) paradigm for recognizing complex human activities is
proposed that handles missing information during testing. We present a
supervised probabilistic approach that integrates LUPI into a hidden
conditional random field (HCRF) model. The proposed model is called HCRF+ and
may be trained using both maximum likelihood and maximum margin approaches. It
employs a self-training technique for automatic estimation of the
regularization parameters of the objective functions. Moreover, the method
provides robustness to outliers (such as noise or missing data) by modeling the
conditional distribution of the privileged information by a Student's
\textit{t}-density function, which is naturally integrated into the HCRF+
framework. Different forms of privileged information were investigated. The
proposed method was evaluated using four challenging publicly available
datasets and the experimental results demonstrate its effectiveness with
respect to the-state-of-the-art in the LUPI framework using both hand-crafted
features and features extracted from a convolutional neural network.
Scalable Support Vector Clustering Using Budget
Authors: Tung Pham, Trung Le, Hang Dang
Date: 2017-09-19 00:00:00
Tasks: Clustering, Outlier Detection

Owing to its application in solving the difficult and diverse clustering or
outlier detection problem, support-based clustering has recently drawn plenty
of attention. Support-based clustering method always undergoes two phases:
finding the domain of novelty and performing clustering assignment. To find the
domain of novelty, the training time given by the current solvers is typically
over-quadratic in the training size, and hence precluding the usage of
support-based clustering method for large-scale datasets. In this paper, we
propose applying Stochastic Gradient Descent (SGD) framework to the first phase
of support-based clustering for finding the domain of novelty and a new
strategy to perform the clustering assignment. However, the direct application
of SGD to the first phase of support-based clustering is vulnerable to the
curse of kernelization, that is, the model size linearly grows up with the data
size accumulated overtime. To address this issue, we invoke the budget approach
which allows us to restrict the model size to a small budget. Our new strategy
for clustering assignment enables a fast computation by means of reducing the
task of clustering assignment on the full training set to the same task on a
significantly smaller set. We also provide a rigorous theoretical analysis
about the convergence rate for the proposed method. Finally, we validate our
proposed method on the well-known datasets for clustering to show that the
proposed method offers a comparable clustering quality while simultaneously
achieving significant speedup in comparison with the baselines.
Online Maximum Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes
Authors: Simone Carlo Surace, Jean-Pascal Pfister
Date: 2016-11-01 00:00:00
Tasks: parameter estimation

We revisit the problem of estimating the parameters of a partially observed
diffusion process, consisting of a hidden state process and an observed
process, with a continuous time parameter. The estimation is to be done online,
i.e. the parameter estimate should be updated recursively based on the
observation filtration. We provide a theoretical analysis of the stochastic
gradient ascent algorithm on the incomplete-data log-likelihood. The
convergence of the algorithm is proved under suitable conditions regarding the
ergodicity of the process consisting of state, filter, and tangent filter.
Additionally, our parameter estimation is shown numerically to have the
potential of improving suboptimal filters, and can be applied even when the
system is not identifiable due to parameter redundancies. Online parameter
estimation is a challenging problem that is ubiquitous in fields such as
robotics, neuroscience, or finance in order to design adaptive filters and
optimal controllers for unknown or changing systems. Despite this, theoretical
analysis of convergence is currently lacking for most of these algorithms. This
article sheds new light on the theory of convergence in continuous time.
Automatic Leaf Extraction from Outdoor Images
Authors: N. Anantrasirichai, Sion Hannuna, Nishan Canagarajah
Date: 2017-09-19 00:00:00

Automatic plant recognition and disease analysis may be streamlined by an
image of a complete, isolated leaf as an initial input. Segmenting leaves from
natural images is a hard problem. Cluttered and complex backgrounds: often
composed of other leaves are commonplace. Furthermore, their appearance is
highly dependent upon illumination and viewing perspective. In order to address
these issues we propose a methodology which exploits the leaves venous systems
in tandem with other low level features. Background and leaf markers are
created using colour, intensity and texture. Two approaches are investigated:
watershed and graph-cut and results compared. Primary-secondary vein detection
and a protrusion-notch removal are applied to refine the extracted leaf. The
efficacy of our approach is demonstrated against existing work.
Language Modeling with Highway LSTM
Authors: Gakuto Kurata, Bhuvana Ramabhadran, George Saon, Abhinav Sethy
Date: 2017-09-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Language models (LMs) based on Long Short Term Memory (LSTM) have shown good
gains in many automatic speech recognition tasks. In this paper, we extend an
LSTM by adding highway networks inside an LSTM and use the resulting Highway
LSTM (HW-LSTM) model for language modeling. The added highway networks increase
the depth in the time dimension. Since a typical LSTM has two internal states,
a memory cell and a hidden state, we compare various types of HW-LSTM by adding
highway networks onto the memory cell and/or the hidden state. Experimental
results on English broadcast news and conversational telephone speech
recognition show that the proposed HW-LSTM LM improves speech recognition
accuracy on top of a strong LSTM LM baseline. We report 5.1% and 9.9% on the
Switchboard and CallHome subsets of the Hub5 2000 evaluation, which reaches the
best performance numbers reported on these tasks to date.
Deep Learning Prototype Domains for Person Re-Identification
Authors: Arne Schumann, Shaogang Gong, Tobias Schuchert
Date: 2016-10-17 00:00:00
Tasks: Deep Learning, Domain Adaptation, Person Re-Identification, Unsupervised Domain Adaptation

Person re-identification (re-id) is the task of matching multiple occurrences
of the same person from different cameras, poses, lighting conditions, and a
multitude of other factors which alter the visual appearance. Typically, this
is achieved by learning either optimal features or matching metrics which are
adapted to specific pairs of camera views dictated by the pairwise labelled
training datasets. In this work, we formulate a deep learning based novel
approach to automatic prototype-domain discovery for domain perceptive
(adaptive) person re-id (rather than camera pair specific learning) for any
camera views scalable to new unseen scenes without training data. We learn a
separate re-id model for each of the discovered prototype-domains and during
model deployment, use the person probe image to select automatically the model
of the closest prototype domain. Our approach requires neither supervised nor
unsupervised domain adaptation learning, i.e. no data available from the target
domains. We evaluate extensively our model under realistic re-id conditions
using automatically detected bounding boxes with low-resolution and partial
occlusion. We show that our approach outperforms most of the state-of-the-art
supervised and unsupervised methods on the latest CUHK-SYSU and PRW benchmarks.
Neural Networks for Text Correction and Completion in Keyboard Decoding
Authors: Shaona Ghosh, Per Ola Kristensson
Date: 2017-09-19 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, Natural Language Understanding

Despite the ubiquity of mobile and wearable text messaging applications, the
problem of keyboard text decoding is not tackled sufficiently in the light of
the enormous success of the deep learning Recurrent Neural Network (RNN) and
Convolutional Neural Networks (CNN) for natural language understanding. In
particular, considering that the keyboard decoders should operate on devices
with memory and processor resource constraints, makes it challenging to deploy
industrial scale deep neural network (DNN) models. This paper proposes a
sequence-to-sequence neural attention network system for automatic text
correction and completion. Given an erroneous sequence, our model encodes
character level hidden representations and then decodes the revised sequence
thus enabling auto-correction and completion. We achieve this by a combination
of character level CNN and gated recurrent unit (GRU) encoder along with and a
word level gated recurrent unit (GRU) attention decoder. Unlike traditional
language models that learn from billions of words, our corpus size is only 12
million words; an order of magnitude smaller. The memory footprint of our
learnt model for inference and prediction is also an order of magnitude smaller
than the conventional language model based text decoders. We report baseline
performance for neural keyboard decoders in such limited domain. Our models
achieve a word level accuracy of $90\%$ and a character error rate CER of
$2.4\%$ over the Twitter typo dataset. We present a novel dataset of noisy to
corrected mappings by inducing the noise distribution from the Twitter data
over the OpenSubtitles 2009 dataset; on which our model predicts with a word
level accuracy of $98\%$ and sequence accuracy of $68.9\%$. In our user study,
our model achieved an average CER of $2.6\%$ with the state-of-the-art
non-neural touch-screen keyboard decoder at CER of $1.6\%$.
Product recognition in store shelves as a sub-graph isomorphism problem
Authors: Alessio Tonioni, Luigi Di Stefano
Date: 2017-07-26 00:00:00

The arrangement of products in store shelves is carefully planned to maximize
sales and keep customers happy. However, verifying compliance of real shelves
to the ideal layout is a costly task routinely performed by the store
personnel. In this paper, we propose a computer vision pipeline to recognize
products on shelves and verify compliance to the planned layout. We deploy
local invariant features together with a novel formulation of the product
recognition problem as a sub-graph isomorphism between the items appearing in
the given image and the ideal layout. This allows for auto-localizing the given
image within the aisle or store and improving recognition dramatically.
Arguments for the Effectiveness of Human Problem Solving
Authors: Frantisek Duris
Date: 2015-06-09 00:00:00

The question of how humans solve problem has been addressed extensively.
However, the direct study of the effectiveness of this process seems to be
overlooked. In this paper, we address the issue of the effectiveness of human
problem solving: we analyze where this effectiveness comes from and what
cognitive mechanisms or heuristics are involved. Our results are based on the
optimal probabilistic problem solving strategy that appeared in Solomonoff
paper on general problem solving system. We provide arguments that a certain
set of cognitive mechanisms or heuristics drive human problem solving in the
similar manner as the optimal Solomonoff strategy. The results presented in
this paper can serve both cognitive psychology in better understanding of human
problem solving processes as well as artificial intelligence in designing more
human-like agents.
Interactive Music Generation with Positional Constraints using Anticipation-RNNs
Authors: Gaëtan Hadjeres, Frank Nielsen
Date: 2017-09-19 00:00:00
Tasks: Music Generation

Recurrent Neural Networks (RNNS) are now widely used on sequence generation
tasks due to their ability to learn long-range dependencies and to generate
sequences of arbitrary length. However, their left-to-right generation
procedure only allows a limited control from a potential user which makes them
unsuitable for interactive and creative usages such as interactive music
generation. This paper introduces a novel architecture called Anticipation-RNN
which possesses the assets of the RNN-based generative models while allowing to
enforce user-defined positional constraints. We demonstrate its efficiency on
the task of generating melodies satisfying positional constraints in the style
of the soprano parts of the J.S. Bach chorale harmonizations. Sampling using
the Anticipation-RNN is of the same order of complexity than sampling from the
traditional RNN model. This fast and interactive generation of musical
sequences opens ways to devise real-time systems that could be used for
creative purposes.
Human Action Forecasting by Learning Task Grammars
Authors: Tengda Han, Jue Wang, Anoop Cherian, Stephen Gould
Date: 2017-09-19 00:00:00
Tasks: Action Recognition, Temporal Action Localization

For effective human-robot interaction, it is important that a robotic
assistant can forecast the next action a human will consider in a given task.
Unfortunately, real-world tasks are often very long, complex, and repetitive;
as a result forecasting is not trivial. In this paper, we propose a novel deep
recurrent architecture that takes as input features from a two-stream Residual
action recognition framework, and learns to estimate the progress of human
activities from video sequences -- this surrogate progress estimation task
implicitly learns a temporal task grammar with respect to which activities can
be localized and forecasted. To learn the task grammar, we propose a stacked
LSTM based multi-granularity progress estimation framework that uses a novel
cumulative Euclidean loss as objective. To demonstrate the effectiveness of our
proposed architecture, we showcase experiments on two challenging robotic
assistive tasks, namely (i) assembling an Ikea table from its constituents, and
(ii) changing the tires of a car. Our results demonstrate that learning task
grammars offers highly discriminative cues improving the forecasting accuracy
by more than 9% over the baseline two-stream forecasting model, while also
outperforming other competitive schemes.
Analogical-based Bayesian Optimization
Authors: Trung Le, Khanh Nguyen, Tu Dinh Nguyen, Dinh Phung
Date: 2017-09-19 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes

Some real-world problems revolve to solve the optimization problem
\max_{x\in\mathcal{X}}f\left(x\right) where f\left(.\right) is a black-box
function and X might be the set of non-vectorial objects (e.g., distributions)
where we can only define a symmetric and non-negative similarity score on it.
This setting requires a novel view for the standard framework of Bayesian
Optimization that generalizes the core insightful spirit of this framework.
With this spirit, in this paper, we propose Analogical-based Bayesian
Optimization that can maximize black-box function over a domain where only a
similarity score can be defined. Our pathway is as follows: we first base on
the geometric view of Gaussian Processes (GP) to define the concept of
influence level that allows us to analytically represent predictive means and
variances of GP posteriors and base on that view to enable replacing kernel
similarity by a more genetic similarity score. Furthermore, we also propose two
strategies to find a batch of query points that can efficiently handle high
dimensional data.