pwc-archive/papers-with-abstracts huggingface dataset:
3D Scanning System for Automatic High-Resolution Plant Phenotyping
Authors: Chuong V. Nguyen, Jurgen Fripp, David R. Lovell, Robert Furbank, Peter Kuffner, Helen Daily, Xavier Sirault
Date: 2017-02-26 00:00:00
Tasks: 3D Reconstruction, Camera Calibration, Plant Phenotyping, Vocal Bursts Intensity Prediction

Thin leaves, fine stems, self-occlusion, non-rigid and slowly changing
structures make plants difficult for three-dimensional (3D) scanning and
reconstruction -- two critical steps in automated visual phenotyping. Many
current solutions such as laser scanning, structured light, and multiview
stereo can struggle to acquire usable 3D models because of limitations in
scanning resolution and calibration accuracy. In response, we have developed a
fast, low-cost, 3D scanning platform to image plants on a rotating stage with
two tilting DSLR cameras centred on the plant. This uses new methods of camera
calibration and background removal to achieve high-accuracy 3D reconstruction.
We assessed the system's accuracy using a 3D visual hull reconstruction
algorithm applied on 2 plastic models of dicotyledonous plants, 2 sorghum
plants and 2 wheat plants across different sets of tilt angles. Scan times
ranged from 3 minutes (to capture 72 images using 2 tilt angles), to 30 minutes
(to capture 360 images using 10 tilt angles). The leaf lengths, widths, areas
and perimeters of the plastic models were measured manually and compared to
measurements from the scanning system: results were within 3-4% of each other.
The 3D reconstructions obtained with the scanning system show excellent
geometric agreement with all six plant specimens, even plants with thin leaves
and fine stems.
Horn: A System for Parallel Training and Regularizing of Large-Scale Neural Networks
Authors: Edward J. Yoon
Date: 2016-08-02 00:00:00
Tasks: Distributed Computing, General Classification

I introduce a new distributed system for effective training and regularizing
of Large-Scale Neural Networks on distributed computing architectures. The
experiments demonstrate the effectiveness of flexible model partitioning and
parallelization strategies based on neuron-centric computation model, with an
implementation of the collective and parallel dropout neural networks training.
Experiments are performed on MNIST handwritten digits classification including
results.
Selection of training populations (and other subset selection problems) with an accelerated genetic algorithm (STPGA: An R-package for selection of training populations with a genetic algorithm)
Authors: Deniz Akdemir
Date: 2017-02-26 00:00:00
Tasks: regression, Variable Selection

Optimal subset selection is an important task that has numerous algorithms
designed for it and has many application areas. STPGA contains a special
genetic algorithm supplemented with a tabu memory property (that keeps track of
previously tried solutions and their fitness for a number of iterations), and
with a regression of the fitness of the solutions on their coding that is used
to form the ideal estimated solution (look ahead property) to search for
solutions of generic optimal subset selection problems. I have initially
developed the programs for the specific problem of selecting training
populations for genomic prediction or association problems, therefore I give
discussion of the theory behind optimal design of experiments to explain the
default optimization criteria in STPGA, and illustrate the use of the programs
in this endeavor. Nevertheless, I have picked a few other areas of application:
supervised and unsupervised variable selection based on kernel alignment,
supervised variable selection with design criteria, influential observation
identification for regression, solving mixed integer quadratic optimization
problems, balancing gains and inbreeding in a breeding population. Some of
these illustrations pertain new statistical approaches.
Understanding deep learning requires rethinking generalization
Authors: Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals
Date: 2016-11-10 00:00:00
Tasks: Deep Learning, image-classification, Image Classification

Despite their massive size, successful deep artificial neural networks can
exhibit a remarkably small difference between training and test performance.
Conventional wisdom attributes small generalization error either to properties
of the model family, or to the regularization techniques used during training.
  Through extensive systematic experiments, we show how these traditional
approaches fail to explain why large neural networks generalize well in
practice. Specifically, our experiments establish that state-of-the-art
convolutional networks for image classification trained with stochastic
gradient methods easily fit a random labeling of the training data. This
phenomenon is qualitatively unaffected by explicit regularization, and occurs
even if we replace the true images by completely unstructured random noise. We
corroborate these experimental findings with a theoretical construction showing
that simple depth two neural networks already have perfect finite sample
expressivity as soon as the number of parameters exceeds the number of data
points as it usually does in practice.
  We interpret our experimental findings by comparison with traditional models.
Context-Aware Bandits
Authors: Shuai Li, Purushottam Kar
Date: 2015-10-12 00:00:00
Tasks: Clustering, Multi-Armed Bandits

We propose an efficient Context-Aware clustering of Bandits (CAB) algorithm,
which can capture collaborative effects. CAB can be easily deployed in a
real-world recommendation system, where multi-armed bandits have been shown to
perform well in particular with respect to the cold-start problem. CAB utilizes
a context-aware clustering augmented by exploration-exploitation strategies.
CAB dynamically clusters the users based on the content universe under
consideration. We give a theoretical analysis in the standard stochastic
multi-armed bandits setting. We show the efficiency of our approach on
production and real-world datasets, demonstrate the scalability, and, more
importantly, the significant increased prediction performance against several
state-of-the-art methods.
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
Authors: Edward Choi, Mohammad Taha Bahadori, Joshua A. Kulas, Andy Schuetz, Walter F. Stewart, Jimeng Sun
Date: 2016-08-19 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Disease Trajectory Forecasting

Accuracy and interpretability are two dominant features of successful
predictive models. Typically, a choice must be made in favor of complex black
box models such as recurrent neural networks (RNN) for accuracy versus less
accurate but more interpretable traditional models such as logistic regression.
This tradeoff poses challenges in medicine where both accuracy and
interpretability are important. We addressed this challenge by developing the
REverse Time AttentIoN model (RETAIN) for application to Electronic Health
Records (EHR) data. RETAIN achieves high accuracy while remaining clinically
interpretable and is based on a two-level neural attention model that detects
influential past visits and significant clinical variables within those visits
(e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR
data in a reverse time order so that recent clinical visits are likely to
receive higher attention. RETAIN was tested on a large health system EHR
dataset with 14 million visits completed by 263K patients over an 8 year period
and demonstrated predictive accuracy and computational scalability comparable
to state-of-the-art methods such as RNN, and ease of interpretability
comparable to traditional models.
Recurrent Neural Networks With Limited Numerical Precision
Authors: Joachim Ott, Zhouhan Lin, Ying Zhang, Shih-Chii Liu, Yoshua Bengio
Date: 2016-11-21 00:00:00
Tasks: Quantization

Recurrent Neural Networks (RNNs) produce state-of-art performance on many
machine learning tasks but their demand on resources in terms of memory and
computational power are often high. Therefore, there is a great interest in
optimizing the computations performed with these models especially when
considering development of specialized low-power hardware for deep networks.
One way of reducing the computational needs is to limit the numerical precision
of the network weights and biases, and this will be addressed for the case of
RNNs. We present results from the use of different stochastic and deterministic
reduced precision training methods applied to two major RNN types, which are
then tested on three datasets. The results show that the stochastic and
deterministic ternarization, pow2- ternarization, and exponential quantization
methods gave rise to low-precision RNNs that produce similar and even higher
accuracy on certain datasets, therefore providing a path towards training more
efficient implementations of RNNs in specialized hardware.
Recurrent Neural Networks With Limited Numerical Precision
Authors: Joachim Ott, Zhouhan Lin, Ying Zhang, Shih-Chii Liu, Yoshua Bengio
Date: 2016-08-24 00:00:00
Tasks: Binarization

Recurrent Neural Networks (RNNs) produce state-of-art performance on many
machine learning tasks but their demand on resources in terms of memory and
computational power are often high. Therefore, there is a great interest in
optimizing the computations performed with these models especially when
considering development of specialized low-power hardware for deep networks.
One way of reducing the computational needs is to limit the numerical precision
of the network weights and biases. This has led to different proposed rounding
methods which have been applied so far to only Convolutional Neural Networks
and Fully-Connected Networks. This paper addresses the question of how to best
reduce weight precision during training in the case of RNNs. We present results
from the use of different stochastic and deterministic reduced precision
training methods applied to three major RNN types which are then tested on
several datasets. The results show that the weight binarization methods do not
work with the RNNs. However, the stochastic and deterministic ternarization,
and pow2-ternarization methods gave rise to low-precision RNNs that produce
similar and even higher accuracy on certain datasets therefore providing a path
towards training more efficient implementations of RNNs in specialized
hardware.
Friends and Enemies of Clinton and Trump: Using Context for Detecting Stance in Political Tweets
Authors: Mirko Lai, Delia Irazú Hernández Farías, Viviana Patti, Paolo Rosso
Date: 2017-02-26 00:00:00
Tasks: Stance Detection

Stance detection, the task of identifying the speaker's opinion towards a
particular target, has attracted the attention of researchers. This paper
describes a novel approach for detecting stance in Twitter. We define a set of
features in order to consider the context surrounding a target of interest with
the final aim of training a model for predicting the stance towards the
mentioned targets. In particular, we are interested in investigating political
debates in social media. For this reason we evaluated our approach focusing on
two targets of the SemEval-2016 Task6 on Detecting stance in tweets, which are
related to the political campaign for the 2016 U.S. presidential elections:
Hillary Clinton vs. Donald Trump. For the sake of comparison with the state of
the art, we evaluated our model against the dataset released in the
SemEval-2016 Task 6 shared task competition. Our results outperform the best
ones obtained by participating teams, and show that information about enemies
and friends of politicians help in detecting stance towards them.
Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis
Authors: B. M. Pavlyshenko
Date: 2017-02-26 00:00:00
Tasks: Bayesian Inference, BIG-bench Machine Learning, Time Series, Time Series Analysis

In this paper we study different approaches for time series modeling. The
forecasting approaches using linear models, ARIMA alpgorithm, XGBoost machine
learning algorithm are described. Results of different model combinations are
shown. For probabilistic modeling the approaches using copulas and Bayesian
inference are considered.
Support vector machine and its bias correction in high-dimension, low-sample-size settings
Authors: Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima
Date: 2017-02-26 00:00:00

In this paper, we consider asymptotic properties of the support vector
machine (SVM) in high-dimension, low-sample-size (HDLSS) settings. We show that
the hard-margin linear SVM holds a consistency property in which
misclassification rates tend to zero as the dimension goes to infinity under
certain severe conditions. We show that the SVM is very biased in HDLSS
settings and its performance is affected by the bias directly. In order to
overcome such difficulties, we propose a bias-corrected SVM (BC-SVM). We show
that the BC-SVM gives preferable performances in HDLSS settings. We also
discuss the SVMs in multiclass HDLSS settings. Finally, we check the
performance of the classifiers in actual data analyses.
Adversarial Networks for the Detection of Aggressive Prostate Cancer
Authors: Simon Kohl, David Bonekamp, Heinz-Peter Schlemmer, Kaneschka Yaqubi, Markus Hohenfellner, Boris Hadaschik, Jan-Philipp Radtke, Klaus Maier-Hein
Date: 2017-02-26 00:00:00
Tasks: Segmentation, Semantic Segmentation

Semantic segmentation constitutes an integral part of medical image analyses
for which breakthroughs in the field of deep learning were of high relevance.
The large number of trainable parameters of deep neural networks however
renders them inherently data hungry, a characteristic that heavily challenges
the medical imaging community. Though interestingly, with the de facto standard
training of fully convolutional networks (FCNs) for semantic segmentation being
agnostic towards the `structure' of the predicted label maps, valuable
complementary information about the global quality of the segmentation lies
idle. In order to tap into this potential, we propose utilizing an adversarial
network which discriminates between expert and generated annotations in order
to train FCNs for semantic segmentation. Because the adversary constitutes a
learned parametrization of what makes a good segmentation at a global level, we
hypothesize that the method holds particular advantages for segmentation tasks
on complex structured, small datasets. This holds true in our experiments: We
learn to segment aggressive prostate cancer utilizing MRI images of 152
patients and show that the proposed scheme is superior over the de facto
standard in terms of the detection sensitivity and the dice-score for
aggressive prostate cancer. The achieved relative gains are shown to be
particularly pronounced in the small dataset limit.
Ternary Neural Networks for Resource-Efficient AI Applications
Authors: Hande Alemdar, Vincent Leroy, Adrien Prost-Boucle, Frédéric Pétrot
Date: 2016-09-01 00:00:00

The computation and storage requirements for Deep Neural Networks (DNNs) are
usually high. This issue limits their deployability on ubiquitous computing
devices such as smart phones, wearables and autonomous drones. In this paper,
we propose ternary neural networks (TNNs) in order to make deep learning more
resource-efficient. We train these TNNs using a teacher-student approach based
on a novel, layer-wise greedy methodology. Thanks to our two-stage training
procedure, the teacher network is still able to use state-of-the-art methods
such as dropout and batch normalization to increase accuracy and reduce
training time. Using only ternary weights and activations, the student ternary
network learns to mimic the behavior of its teacher network without using any
multiplication. Unlike its -1,1 binary counterparts, a ternary neural network
inherently prunes the smaller weights by setting them to zero during training.
This makes them sparser and thus more energy-efficient. We design a
purpose-built hardware architecture for TNNs and implement it on FPGA and ASIC.
We evaluate TNNs on several benchmark datasets and demonstrate up to 3.1x
better energy efficiency with respect to the state of the art while also
improving accuracy.
Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation
Authors: Omid Hosseini Jafari, Oliver Groth, Alexander Kirillov, Michael Ying Yang, Carsten Rother
Date: 2017-02-26 00:00:00
Tasks: Depth Estimation, Depth Prediction, Semantic Segmentation

This paper addresses the task of designing a modular neural network
architecture that jointly solves different tasks. As an example we use the
tasks of depth estimation and semantic segmentation given a single RGB image.
The main focus of this work is to analyze the cross-modality influence between
depth and semantic prediction maps on their joint refinement. While most
previous works solely focus on measuring improvements in accuracy, we propose a
way to quantify the cross-modality influence. We show that there is a
relationship between final accuracy and cross-modality influence, although not
a simple linear one. Hence a larger cross-modality influence does not
necessarily translate into an improved accuracy. We find that a beneficial
balance between the cross-modality influences can be achieved by network
architecture and conjecture that this relationship can be utilized to
understand different network design choices. Towards this end we propose a
Convolutional Neural Network (CNN) architecture that fuses the state of the
state-of-the-art results for depth estimation and semantic labeling. By
balancing the cross-modality influences between depth and semantic prediction,
we achieve improved results for both tasks using the NYU-Depth v2 benchmark.
Bayesian Nonparametric Unmixing of Hyperspectral Images
Authors: Jürgen Hahn, Abdelhak M. Zoubir
Date: 2017-02-26 00:00:00

Hyperspectral imaging is an important tool in remote sensing, allowing for
accurate analysis of vast areas. Due to a low spatial resolution, a pixel of a
hyperspectral image rarely represents a single material, but rather a mixture
of different spectra. HSU aims at estimating the pure spectra present in the
scene of interest, referred to as endmembers, and their fractions in each
pixel, referred to as abundances. Today, many HSU algorithms have been
proposed, based either on a geometrical or statistical model. While most
methods assume that the number of endmembers present in the scene is known,
there is only little work about estimating this number from the observed data.
In this work, we propose a Bayesian nonparametric framework that jointly
estimates the number of endmembers, the endmembers itself, and their
abundances, by making use of the Indian Buffet Process as a prior for the
endmembers. Simulation results and experiments on real data demonstrate the
effectiveness of the proposed algorithm, yielding results comparable with
state-of-the-art methods while being able to reliably infer the number of
endmembers. In scenarios with strong noise, where other algorithms provide only
poor results, the proposed approach tends to overestimate the number of
endmembers slightly. The additional endmembers, however, often simply represent
noisy replicas of present endmembers and could easily be merged in a
post-processing step.
Bayesian Nonparametric Feature and Policy Learning for Decision-Making
Authors: Jürgen Hahn, Abdelhak M. Zoubir
Date: 2017-02-26 00:00:00
Tasks: Decision Making

Learning from demonstrations has gained increasing interest in the recent
past, enabling an agent to learn how to make decisions by observing an
experienced teacher. While many approaches have been proposed to solve this
problem, there is only little work that focuses on reasoning about the observed
behavior. We assume that, in many practical problems, an agent makes its
decision based on latent features, indicating a certain action. Therefore, we
propose a generative model for the states and actions. Inference reveals the
number of features, the features, and the policies, allowing us to learn and to
analyze the underlying structure of the observed behavior. Further, our
approach enables prediction of actions for new states. Simulations are used to
assess the performance of the algorithm based upon this model. Moreover, the
problem of learning a driver's behavior is investigated, demonstrating the
performance of the proposed model in a real-world scenario.
Detecting (Un)Important Content for Single-Document News Summarization
Authors: Yinfei Yang, Forrest Sheng Bao, Ani Nenkova
Date: 2017-02-26 00:00:00
Proceeding: EACL 2017 4
Tasks: Document Summarization, News Summarization, Sentence

We present a robust approach for detecting intrinsic sentence importance in
news, by training on two corpora of document-summary pairs. When used for
single-document summarization, our approach, combined with the "beginning of
document" heuristic, outperforms a state-of-the-art summarizer and the
beginning-of-article baseline in both automatic and manual evaluations. These
results represent an important advance because in the absence of cross-document
repetition, single document summarizers for news have not been able to
consistently outperform the strong beginning-of-article baseline.
3D tracking of water hazards with polarized stereo cameras
Authors: Chuong V. Nguyen, Michael Milford, Robert Mahony
Date: 2017-01-16 00:00:00

Current self-driving car systems operate well in sunny weather but struggle
in adverse conditions. One of the most commonly encountered adverse conditions
involves water on the road caused by rain, sleet, melting snow or flooding.
While some advances have been made in using conventional RGB camera and LIDAR
technology for detecting water hazards, other sources of information such as
polarization offer a promising and potentially superior approach to this
problem in terms of performance and cost. In this paper, we present a novel
stereo-polarization system for detecting and tracking water hazards based on
polarization and color variation of reflected light, with consideration of the
effect of polarized light from sky as function of reflection and azimuth
angles. To evaluate this system, we present a new large `water on road'
datasets spanning approximately 2 km of driving in various on-road and off-road
conditions and demonstrate for the first time reliable water detection and
tracking over a wide range of realistic car driving water conditions using
polarized vision as the primary sensing modality. Our system successfully
detects water hazards up to more than 100m. Finally, we discuss several
interesting challenges and propose future research directions for further
improving robust autonomous car perception in hazardous wet conditions using
polarization sensors.
Stochastic Averaging for Constrained Optimization with Application to Online Resource Allocation
Authors: Tianyi Chen, Aryan Mokhtari, Xin Wang, Alejandro Ribeiro, Georgios B. Giannakis
Date: 2016-10-07 00:00:00

Existing approaches to resource allocation for nowadays stochastic networks
are challenged to meet fast convergence and tolerable delay requirements. The
present paper leverages online learning advances to facilitate stochastic
resource allocation tasks. By recognizing the central role of Lagrange
multipliers, the underlying constrained optimization problem is formulated as a
machine learning task involving both training and operational modes, with the
goal of learning the sought multipliers in a fast and efficient manner. To this
end, an order-optimal offline learning approach is developed first for batch
training, and it is then generalized to the online setting with a procedure
termed learn-and-adapt. The novel resource allocation protocol permeates
benefits of stochastic approximation and statistical learning to obtain
low-complexity online updates with learning errors close to the statistical
accuracy limits, while still preserving adaptation performance, which in the
stochastic network optimization context guarantees queue stability. Analysis
and simulated tests demonstrate that the proposed data-driven approach improves
the delay and convergence performance of existing resource allocation schemes.