pwc-archive/papers-with-abstracts huggingface dataset:
The Price of Quota-based Diversity in Assignment Problems
Authors: Nawal Benabbou, Mithun Chakraborty, Vinh Ho Xuan, Jakub Sliwinski, Yair Zick
Date: 2017-11-28 00:00:00
Tasks: Diversity

We introduce and analyze an extension to the matching problem on a weighted bipartite graph: Assignment with Type Constraints. The two parts of the graph are partitioned into subsets called types and blocks; we seek a matching with the largest sum of weights under the constraint that there is a pre-specified cap on the number of vertices matched in every type-block pair. Our primary motivation stems from the public housing program of Singapore, accounting for over 70% of its residential real estate. To promote ethnic diversity within its housing projects, Singapore imposes ethnicity quotas: each new housing development comprises blocks of flats and each ethnicity-based group in the population must not own more than a certain percentage of flats in a block. Other domains using similar hard capacity constraints include matching prospective students to schools or medical residents to hospitals. Limiting agents' choices for ensuring diversity in this manner naturally entails some welfare loss. One of our goals is to study the trade-off between diversity and social welfare in such settings. We first show that, while the classic assignment program is polynomial-time computable, adding diversity constraints makes it computationally intractable; however, we identify a $\tfrac{1}{2}$-approximation algorithm, as well as reasonable assumptions on the weights that permit poly-time algorithms. Next, we provide two upper bounds on the price of diversity -- a measure of the loss in welfare incurred by imposing diversity constraints -- as functions of natural problem parameters. We conclude the paper with simulations based on publicly available data from two diversity-constrained allocation problems -- Singapore Public Housing and Chicago School Choice -- which shed light on how the constrained maximization as well as lottery-based variants perform in practice.
Relationship between Variants of One-Class Nearest Neighbours and Creating their Accurate Ensembles
Authors: Shehroz S. Khan, Amir Ahmad
Date: 2016-04-06 00:00:00
Tasks: One-Class Classification

In one-class classification problems, only the data for the target class is
available, whereas the data for the non-target class may be completely absent.
In this paper, we study one-class nearest neighbour (OCNN) classifiers and
their different variants. We present a theoretical analysis to show the
relationships among different variants of OCNN that may use different
neighbours or thresholds to identify unseen examples of the non-target class.
We also present a method based on inter-quartile range for optimising
parameters used in OCNN in the absence of non-target data during training.
Then, we propose two ensemble approaches based on random subspace and random
projection methods to create accurate OCNN ensembles. We tested the proposed
methods on 15 benchmark and real world domain-specific datasets and show that
random-projection ensembles of OCNN perform best.
Classifying X-ray Binaries: A Probabilistic Approach
Authors: Giri Gopalan, Saeqa Dil Vrtilek, Luke Bornn
Date: 2015-07-13 00:00:00
Tasks: Object

In X-ray binary star systems consisting of a compact object that accretes
material from an orbiting secondary star, there is no straightforward means to
decide if the compact object is a black hole or a neutron star. To assist this
classification, we develop a Bayesian statistical model that makes use of the
fact that X-ray binary systems appear to cluster based on their compact object
type when viewed from a 3-dimensional coordinate system derived from X-ray
spectral data. The first coordinate of this data is the ratio of counts in mid
to low energy band (color 1), the second coordinate is the ratio of counts in
high to low energy band (color 2), and the third coordinate is the sum of
counts in all three bands. We use this model to estimate the probabilities that
an X-ray binary system contains a black hole, non-pulsing neutron star, or
pulsing neutron star. In particular, we utilize a latent variable model in
which the latent variables follow a Gaussian process prior distribution, and
hence we are able to induce the spatial correlation we believe exists between
systems of the same type. The utility of this approach is evidenced by the
accurate prediction of system types using Rossi X-ray Timing Explorer All Sky
Monitor data, but it is not flawless. In particular, non-pulsing neutron
systems containing "bursters" that are close to the boundary demarcating
systems containing black holes tend to be classified as black hole systems. As
a byproduct of our analyses, we provide the astronomer with public R code that
can be used to predict the compact object type of X-ray binaries given training
data.
Deep Architectures for Automated Seizure Detection in Scalp EEGs
Authors: Meysam Golmohammadi, Saeedeh Ziyabari, Vinit Shah, Silvia Lopez de Diego, Iyad Obeid, Joseph Picone
Date: 2017-12-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Seizure Detection

Automated seizure detection using clinical electroencephalograms is a
challenging machine learning problem because the multichannel signal often has
an extremely low signal to noise ratio. Events of interest such as seizures are
easily confused with signal artifacts (e.g, eye movements) or benign variants
(e.g., slowing). Commercially available systems suffer from unacceptably high
false alarm rates. Deep learning algorithms that employ high dimensional models
have not previously been effective due to the lack of big data resources. In
this paper, we use the TUH EEG Seizure Corpus to evaluate a variety of hybrid
deep structures including Convolutional Neural Networks and Long Short-Term
Memory Networks. We introduce a novel recurrent convolutional architecture that
delivers 30% sensitivity at 7 false alarms per 24 hours. We have also evaluated
our system on a held-out evaluation set based on the Duke University Seizure
Corpus and demonstrate that performance trends are similar to the TUH EEG
Seizure Corpus. This is a significant finding because the Duke corpus was
collected with different instrumentation and at different hospitals. Our work
shows that deep learning architectures that integrate spatial and temporal
contexts are critical to achieving state of the art performance and will enable
a new generation of clinically-acceptable technology.
An Ontological Architecture for Orbital Debris Data
Authors: Robert J. Rovetto
Date: 2017-04-01 00:00:00

The orbital debris problem presents an opportunity for inter-agency and international cooperation toward the mutually beneficial goals of debris prevention, mitigation, remediation, and improved space situational awareness (SSA). Achieving these goals requires sharing orbital debris and other SSA data. Toward this, I present an ontological architecture for the orbital debris domain, taking steps in the creation of an orbital debris ontology (ODO). The purpose of this ontological system is to (I) represent general orbital debris and SSA domain knowledge, (II) structure, and standardize where needed, orbital data and terminology, and (III) foster semantic interoperability and data-sharing. In doing so I hope to (IV) contribute to solving the orbital debris problem, improving peaceful global SSA, and ensuring safe space travel for future generations.
Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures
Authors: Meysam Golmohammadi, Amir Hossein Harati Nejad Torbati, Silvia Lopez de Diego, Iyad Obeid, Joseph Picone
Date: 2017-12-28 00:00:00
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG), Event Detection, Hybrid Machine Learning, Sensitivity, Specificity

Objective: A clinical decision support tool that automatically interprets
EEGs can reduce time to diagnosis and enhance real-time applications such as
ICU monitoring. Clinicians have indicated that a sensitivity of 95% with a
specificity below 5% was the minimum requirement for clinical acceptance. We
propose a highperformance classification system based on principles of big data
and machine learning. Methods: A hybrid machine learning system that uses
hidden Markov models (HMM) for sequential decoding and deep learning networks
for postprocessing is proposed. These algorithms were trained and evaluated
using the TUH EEG Corpus, which is the world's largest publicly available
database of clinical EEG data. Results: Our approach delivers a sensitivity
above 90% while maintaining a specificity below 5%. This system detects three
events of clinical interest: (1) spike and/or sharp waves, (2) periodic
lateralized epileptiform discharges, (3) generalized periodic epileptiform
discharges. It also detects three events used to model background noise: (1)
artifacts, (2) eye movement (3) background. Conclusions: A hybrid HMM/deep
learning system can deliver a low false alarm rate on EEG event detection,
making automated analysis a viable option for clinicians. Significance: The TUH
EEG Corpus enables application of highly data consumptive machine learning
algorithms to EEG analysis. Performance is approaching clinical acceptance for
real-time applications.
PixelSNAIL: An Improved Autoregressive Generative Model
Authors: Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel
Date: 2017-12-28 00:00:00
Proceeding: ICML 2018 7
Tasks: Density Estimation, Image Generation, Meta Reinforcement Learning, model, Reinforcement Learning

Autoregressive generative models consistently achieve the best results in
density estimation tasks involving high dimensional data, such as images or
audio. They pose density estimation as a sequence modeling task, where a
recurrent neural network (RNN) models the conditional distribution over the
next element conditioned on all previous elements. In this paradigm, the
bottleneck is the extent to which the RNN can model long-range dependencies,
and the most successful approaches rely on causal convolutions, which offer
better access to earlier parts of the sequence than conventional RNNs. Taking
inspiration from recent work in meta reinforcement learning, where dealing with
long-range dependencies is also essential, we introduce a new generative model
architecture that combines causal convolutions with self attention. In this
note, we describe the resulting model and present state-of-the-art
log-likelihood results on CIFAR-10 (2.85 bits per dim) and $32 \times 32$
ImageNet (3.80 bits per dim). Our implementation is available at
https://github.com/neocxi/pixelsnail-public
2D-3D Pose Consistency-based Conditional Random Fields for 3D Human Pose Estimation
Authors: Ju Yong Chang, Kyoung Mu Lee
Date: 2017-04-13 00:00:00
Tasks: 2D Human Pose Estimation, 3D Human Pose Estimation, Pose Estimation, regression

This study considers the 3D human pose estimation problem in a single RGB
image by proposing a conditional random field (CRF) model over 2D poses, in
which the 3D pose is obtained as a byproduct of the inference process. The
unary term of the proposed CRF model is defined based on a powerful heat-map
regression network, which has been proposed for 2D human pose estimation. This
study also presents a regression network for lifting the 2D pose to 3D pose and
proposes the prior term based on the consistency between the estimated 3D pose
and the 2D pose. To obtain the approximate solution of the proposed CRF model,
the N-best strategy is adopted. The proposed inference algorithm can be viewed
as sequential processes of bottom-up generation of 2D and 3D pose proposals
from the input 2D image based on deep networks and top-down verification of
such proposals by checking their consistencies. To evaluate the proposed
method, we use two large-scale datasets: Human3.6M and HumanEva. Experimental
results show that the proposed method achieves the state-of-the-art 3D human
pose estimation performance.
Directional Statistics and Filtering Using libDirectional
Authors: Gerhard Kurz, Igor Gilitschenski, Florian Pfaff, Lukas Drude, Uwe D. Hanebeck, Reinhold Haeb-Umbach, Roland Y. Siegwart
Date: 2017-12-28 00:00:00

In this paper, we present libDirectional, a MATLAB library for directional
statistics and directional estimation. It supports a variety of commonly used
distributions on the unit circle, such as the von Mises, wrapped normal, and
wrapped Cauchy distributions. Furthermore, various distributions on
higher-dimensional manifolds such as the unit hypersphere and the hypertorus
are available. Based on these distributions, several recursive filtering
algorithms in libDirectional allow estimation on these manifolds. The
functionality is implemented in a clear, well-documented, and object-oriented
structure that is both easy to use and easy to extend.
An Online Learning Approach to Buying and Selling Demand Response
Authors: Kia Khezeli, Eilyan Bitar
Date: 2017-07-23 00:00:00

We adopt the perspective of an aggregator, which seeks to coordinate its
purchase of demand reductions from a fixed group of residential electricity
customers, with its sale of the aggregate demand reduction in a two-settlement
wholesale energy market. The aggregator procures reductions in demand by
offering its customers a uniform price for reductions in consumption relative
to their predetermined baselines. Prior to its realization of the aggregate
demand reduction, the aggregator must also determine how much energy to sell
into the two-settlement energy market. In the day-ahead market, the aggregator
commits to a forward contract, which calls for the delivery of energy in the
real-time market. The underlying aggregate demand curve, which relates the
aggregate demand reduction to the aggregator's offered price, is assumed to be
affine and subject to unobservable, random shocks. Assuming that both the
parameters of the demand curve and the distribution of the random shocks are
initially unknown to the aggregator, we investigate the extent to which the
aggregator might dynamically adapt its offered prices and forward contracts to
maximize its expected profit over a time window of $T$ days. Specifically, we
design a dynamic pricing and contract offering policy that resolves the
aggregator's need to learn the unknown demand model with its desire to maximize
its cumulative expected profit over time. In particular, the proposed pricing
policy is proven to incur a regret over $T$ days that is no greater than
$O(\log(T)\sqrt{T})$.
Extrapolating Expected Accuracies for Large Multi-Class Problems
Authors: Charles Zheng, Rakesh Achanta, Yuval Benjamini
Date: 2017-12-27 00:00:00
Tasks: General Classification, Multi-class Classification

The difficulty of multi-class classification generally increases with the
number of classes. Using data from a subset of the classes, can we predict how
well a classifier will scale with an increased number of classes? Under the
assumptions that the classes are sampled identically and independently from a
population, and that the classifier is based on independently learned scoring
functions, we show that the expected accuracy when the classifier is trained on
k classes is the (k-1)st moment of a certain distribution that can be estimated
from data. We present an unbiased estimation method based on the theory, and
demonstrate its application on a facial recognition example.
On the estimation of correlation in a binary sequence model
Authors: Haolei Weng, Yang Feng
Date: 2017-12-27 00:00:00
Tasks: parameter estimation

We consider a binary sequence generated by thresholding a hidden continuous sequence. The hidden variables are assumed to have a compound symmetry covariance structure with a single parameter characterizing the common correlation. We study the parameter estimation problem under such one-parameter models. We demonstrate that maximizing the likelihood function does not yield consistent estimates for the correlation. We then formally prove the nonestimability of the parameter by deriving a non-vanishing minimax lower bound. This counter-intuitive phenomenon provides an interesting insight that one-bit information of each latent variable is not sufficient to consistently recover their common correlation. On the other hand, we further show that trinary data generated from the hidden variables can consistently estimate the correlation with parametric convergence rate. Thus we reveal a phase transition phenomenon regarding the discretization of latent continuous variables while preserving the estimability of the correlation. Numerical experiments are performed to validate the conclusions.
Learning Adaptive Parameter Tuning for Image Processing
Authors: Jingming Dong, Iuri Frosio, Jan Kautz
Date: 2016-10-28 00:00:00
Tasks: Deblurring, Demosaicking, Denoising

The non-stationary nature of image characteristics calls for adaptive
processing, based on the local image content. We propose a simple and flexible
method to learn local tuning of parameters in adaptive image processing: we
extract simple local features from an image and learn the relation between
these features and the optimal filtering parameters. Learning is performed by
optimizing a user defined cost function (any image quality metric) on a
training set. We apply our method to three classical problems (denoising,
demosaicing and deblurring) and we show the effectiveness of the learned
parameter modulation strategies. We also show that these strategies are
consistent with theoretical results from the literature.
Combining Representation Learning with Logic for Language Processing
Authors: Tim Rocktäschel
Date: 2017-12-27 00:00:00
Tasks: Formal Logic, Knowledge Base Completion, Representation Learning

The current state-of-the-art in many natural language processing and
automated knowledge base completion tasks is held by representation learning
methods which learn distributed vector representations of symbols via
gradient-based optimization. They require little or no hand-crafted features,
thus avoiding the need for most preprocessing steps and task-specific
assumptions. However, in many cases representation learning requires a large
amount of annotated training data to generalize well to unseen data. Such
labeled training data is provided by human annotators who often use formal
logic as the language for specifying annotations. This thesis investigates
different combinations of representation learning methods with logic for
reducing the need for annotated training data, and for improving
generalization.
Neural network augmented inverse problems for PDEs
Authors: Jens Berg, Kaj Nyström
Date: 2017-12-27 00:00:00

In this paper we show how to augment classical methods for inverse problems
with artificial neural networks. The neural network acts as a prior for the
coefficient to be estimated from noisy data. Neural networks are global, smooth
function approximators and as such they do not require explicit regularization
of the error functional to recover smooth solutions and coefficients. We give
detailed examples using the Poisson equation in 1, 2, and 3 space dimensions
and show that the neural network augmentation is robust with respect to noisy
and incomplete data, mesh, and geometry.
Geometry Processing of Conventionally Produced Mouse Brain Slice Images
Authors: Nitin Agarwal, Xiangmin Xu, Gopi Meenakshisundaram
Date: 2017-12-27 00:00:00
Tasks: 3D Reconstruction

Brain mapping research in most neuroanatomical laboratories relies on
conventional processing techniques, which often introduce histological
artifacts such as tissue tears and tissue loss. In this paper we present
techniques and algorithms for automatic registration and 3D reconstruction of
conventionally produced mouse brain slices in a standardized atlas space. This
is achieved first by constructing a virtual 3D mouse brain model from annotated
slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed
model generates ARA-based slice images corresponding to the microscopic images
of histological brain sections. These image pairs are aligned using a geometric
approach through contour images. Histological artifacts in the microscopic
images are detected and removed using Constrained Delaunay Triangulation before
performing global alignment. Finally, non-linear registration is performed by
solving Laplace's equation with Dirichlet boundary conditions. Our methods
provide significant improvements over previously reported registration
techniques for the tested slices in 3D space, especially on slices with
significant histological artifacts. Further, as an application we count the
number of neurons in various anatomical regions using a dataset of 51
microscopic slices from a single mouse brain. This work represents a
significant contribution to this subfield of neuroscience as it provides tools
to neuroanatomist for analyzing and processing histological data.
TensorFlow-Serving: Flexible, High-Performance ML Serving
Authors: Christopher Olston, Noah Fiedel, Kiril Gorovoy, Jeremiah Harmsen, Li Lao, Fangwei Li, Vinu Rajashekhar, Sukriti Ramesh, Jordan Soyke
Date: 2017-12-17 00:00:00
Tasks: BIG-bench Machine Learning, Vocal Bursts Intensity Prediction

We describe TensorFlow-Serving, a system to serve machine learning models
inside Google which is also available in the cloud and via open-source. It is
extremely flexible in terms of the types of ML platforms it supports, and ways
to integrate with systems that convey new models and updated versions from
training to serving. At the same time, the core code paths around model lookup
and inference have been carefully optimized to avoid performance pitfalls
observed in naive implementations. Google uses it in many production
deployments, including a multi-tenant model hosting service called TFS^2.
Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval
Authors: Yu-An Chung, Wei-Hung Weng
Date: 2017-11-22 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Medical Image Retrieval, Retrieval

Deep neural networks have been investigated in learning latent
representations of medical images, yet most of the studies limit their approach
in a single supervised convolutional neural network (CNN), which usually rely
heavily on a large scale annotated dataset for training. To learn image
representations with less supervision involved, we propose a deep Siamese CNN
(SCNN) architecture that can be trained with only binary image pair
information. We evaluated the learned image representations on a task of
content-based medical image retrieval using a publicly available multiclass
diabetic retinopathy fundus image dataset. The experimental results show that
our proposed deep SCNN is comparable to the state-of-the-art single supervised
CNN, and requires much less supervision for training.
CNN Is All You Need
Authors: Qiming Chen, Ren Wu
Date: 2017-12-27 00:00:00
Tasks: All, Decoder, Position, Sensitivity, Translation

The Convolution Neural Network (CNN) has demonstrated the unique advantage in
audio, image and text learning; recently it has also challenged Recurrent
Neural Networks (RNNs) with long short-term memory cells (LSTM) in
sequence-to-sequence learning, since the computations involved in CNN are
easily parallelizable whereas those involved in RNN are mostly sequential,
leading to a performance bottleneck. However, unlike RNN, the native CNN lacks
the history sensitivity required for sequence transformation; therefore
enhancing the sequential order awareness, or position-sensitivity, becomes the
key to make CNN the general deep learning model. In this work we introduce an
extended CNN model with strengthen position-sensitivity, called PoseNet. A
notable feature of PoseNet is the asymmetric treatment of position information
in the encoder and the decoder. Experiments shows that PoseNet allows us to
improve the accuracy of CNN based sequence-to-sequence learning significantly,
achieving around 33-36 BLEU scores on the WMT 2014 English-to-German
translation task, and around 44-46 BLEU scores on the English-to-French
translation task.