pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Nonparametric Modeling of Heterogeneous Groups of Censored Data
Authors: Alexandre Piché, Russell Steele, Ian Shrier, Stephanie Long
Date: 2016-10-24 00:00:00

Datasets containing large samples of time-to-event data arising from several
small heterogeneous groups are commonly encountered in statistics. This
presents problems as they cannot be pooled directly due to their heterogeneity
or analyzed individually because of their small sample size. Bayesian
nonparametric modelling approaches can be used to model such datasets given
their ability to flexibly share information across groups. In this paper, we
will compare three popular Bayesian nonparametric methods for modelling the
survival functions of heterogeneous groups. Specifically, we will first compare
the modelling accuracy of the Dirichlet process, the hierarchical Dirichlet
process, and the nested Dirichlet process on simulated datasets of different
sizes, where group survival curves differ in shape or in expectation. We, then,
will compare the models on a real-world injury dataset.
Control Matching via Discharge Code Sequences
Authors: Dang Nguyen, Wei Luo, Dinh Phung, Svetha Venkatesh
Date: 2016-12-02 00:00:00

In this paper, we consider the patient similarity matching problem over a
cancer cohort of more than 220,000 patients. Our approach first leverages on
Word2Vec framework to embed ICD codes into vector-valued representation. We
then propose a sequential algorithm for case-control matching on this
representation space of diagnosis codes. The novel practice of applying the
sequential matching on the vector representation lifted the matching accuracy
measured through multiple clinical outcomes. We reported the results on a
large-scale dataset to demonstrate the effectiveness of our method. For such a
large dataset where most clinical information has been codified, the new method
is particularly relevant.
Transfer Learning via Latent Factor Modeling to Improve Prediction of Surgical Complications
Authors: Elizabeth C Lorenzi, Zhifei Sun, Erich Huang, Ricardo Henao, Katherine A. Heller
Date: 2016-12-02 00:00:00
Tasks: Transfer Learning

We aim to create a framework for transfer learning using latent factor models
to learn the dependence structure between a larger source dataset and a target
dataset. The methodology is motivated by our goal of building a risk-assessment
model for surgery patients, using both institutional and national surgical
outcomes data. The national surgical outcomes data is collected through NSQIP
(National Surgery Quality Improvement Program), a database housing almost 4
million patients from over 700 different hospitals. We build a latent factor
model with a hierarchical prior on the loadings matrix to appropriately account
for the different covariance structure in our data. We extend this model to
handle more complex relationships between the populations by deriving a scale
mixture formulation using stick-breaking properties. Our model provides a
transfer learning framework that utilizes all information from both the source
and target data, while modeling the underlying inherent differences between
them.
Higher Order Mutual Information Approximation for Feature Selection
Authors: Jilin Wu, Soumyajit Gupta, Chandrajit Bajaj
Date: 2016-12-02 00:00:00
Tasks: feature selection

Feature selection is a process of choosing a subset of relevant features so
that the quality of prediction models can be improved. An extensive body of
work exists on information-theoretic feature selection, based on maximizing
Mutual Information (MI) between subsets of features and class labels. The prior
methods use a lower order approximation, by treating the joint entropy as a
summation of several single variable entropies. This leads to locally optimal
selections and misses multi-way feature combinations. We present a higher order
MI based approximation technique called Higher Order Feature Selection (HOFS).
Instead of producing a single list of features, our method produces a ranked
collection of feature subsets that maximizes MI, giving better comprehension
(feature ranking) as to which features work best together when selected, due to
their underlying interdependent structure. Our experiments demonstrate that the
proposed method performs better than existing feature selection approaches
while keeping similar running times and computational complexity.
Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks
Authors: Daniel Lévy, Arzav Jain
Date: 2016-12-02 00:00:00
Tasks: Classification, Data Augmentation, General Classification, Transfer Learning

Mammography is the most widely used method to screen breast cancer. Because
of its mostly manual nature, variability in mass appearance, and low
signal-to-noise ratio, a significant number of breast masses are missed or
misdiagnosed. In this work, we present how Convolutional Neural Networks can be
used to directly classify pre-segmented breast masses in mammograms as benign
or malignant, using a combination of transfer learning, careful pre-processing
and data augmentation to overcome limited training data. We achieve
state-of-the-art results on the DDSM dataset, surpassing human performance, and
show interpretability of our model.
On Robustness of Kernel Clustering
Authors: Bowei Yan, Purnamrita Sarkar
Date: 2016-06-06 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Clustering

Clustering is one of the most important unsupervised problems in machine
learning and statistics. Among many existing algorithms, kernel k-means has
drawn much research attention due to its ability to find non-linear cluster
boundaries and its inherent simplicity. There are two main approaches for
kernel k-means: SVD of the kernel matrix and convex relaxations. Despite the
attention kernel clustering has received both from theoretical and applied
quarters, not much is known about robustness of the methods. In this paper we
first introduce a semidefinite programming relaxation for the kernel clustering
problem, then prove that under a suitable model specification, both the K-SVD
and SDP approaches are consistent in the limit, albeit SDP is strongly
consistent, i.e. achieves exact recovery, whereas K-SVD is weakly consistent,
i.e. the fraction of misclassified nodes vanish.
Photorealistic Facial Texture Inference Using Deep Neural Networks
Authors: Shunsuke Saito, Lingyu Wei, Liwen Hu, Koki Nagano, Hao Li
Date: 2016-12-02 00:00:00
Proceeding: CVPR 2017 7
Tasks: Face Model

We present a data-driven inference method that can synthesize a
photorealistic texture map of a complete 3D face model given a partial 2D view
of a person in the wild. After an initial estimation of shape and low-frequency
albedo, we compute a high-frequency partial texture map, without the shading
component, of the visible face area. To extract the fine appearance details
from this incomplete input, we introduce a multi-scale detail analysis
technique based on mid-layer feature correlations extracted from a deep
convolutional neural network. We demonstrate that fitting a convex combination
of feature correlations from a high-resolution face database can yield a
semantically plausible facial detail description of the entire face. A complete
and photorealistic texture map can then be synthesized by iteratively
optimizing for the reconstructed feature correlations. Using these
high-resolution textures and a commercial rendering framework, we can produce
high-fidelity 3D renderings that are visually comparable to those obtained with
state-of-the-art multi-view face capture systems. We demonstrate successful
face reconstructions from a wide range of low resolution input images,
including those of historical figures. In addition to extensive evaluations, we
validate the realism of our results using a crowdsourced user study.
A Visual Representation for Editing Face Images
Authors: Jiajun Lu, Kalyan Sunkavalli, Nathan Carr, Sunil Hadap, David Forsyth
Date: 2016-12-02 00:00:00
Tasks: Face Model

We propose a new approach for editing face images, which enables numerous
exciting applications including face relighting, makeup transfer and face
detail editing. Our face edits are based on a visual representation, which
includes geometry, face segmentation, albedo, illumination and detail map. To
recover our visual representation, we start by estimating geometry using a
morphable face model, then decompose the face image to recover the albedo, and
then shade the geometry with the albedo and illumination. The residual between
our shaded geometry and the input image produces our detail map, which carries
high frequency information that is either insufficiently or incorrectly
captured by our shading process. By manipulating the detail map, we can edit
face images with reality and identity preserved. Our representation allows
various applications. First, it allows a user to directly manipulate various
illumination. Second, it allows non-parametric makeup transfer with input
face's distinctive identity features preserved. Third, it allows non-parametric
modifications to the face appearance by transferring details. For face
relighting and detail editing, we evaluate via a user study and our method
outperforms other methods. For makeup transfer, we evaluate via an online
attractiveness evaluation system, and can reliably make people look younger and
more attractive. We also show extensive qualitative comparisons to existing
methods, and have significant improvements over previous techniques.
Multivariate Spearman's rho for aggregating ranks using copulas
Authors: Justin Bedo, Cheng Soon Ong
Date: 2014-10-16 00:00:00
Tasks: Learning-To-Rank

We study the problem of rank aggregation: given a set of ranked lists, we
want to form a consensus ranking. Furthermore, we consider the case of extreme
lists: i.e., only the rank of the best or worst elements are known. We impute
missing ranks by the average value and generalise Spearman's \rho to extreme
ranks. Our main contribution is the derivation of a non-parametric estimator
for rank aggregation based on multivariate extensions of Spearman's \rho, which
measures correlation between a set of ranked lists. Multivariate Spearman's
\rho is defined using copulas, and we show that the geometric mean of
normalised ranks maximises multivariate correlation. Motivated by this, we
propose a weighted geometric mean approach for learning to rank which has a
closed form least squares solution. When only the best or worst elements of a
ranked list are known, we impute the missing ranks by the average value,
allowing us to apply Spearman's \rho. Finally, we demonstrate good performance
on the rank aggregation benchmarks MQ2007 and MQ2008.
Object-Centric Representation Learning from Unlabeled Videos
Authors: Ruohan Gao, Dinesh Jayaraman, Kristen Grauman
Date: 2016-12-01 00:00:00
Tasks: image-classification, Image Classification, Object, Representation Learning, Retrieval, Triplet

Supervised (pre-)training currently yields state-of-the-art performance for
representation learning for visual recognition, yet it comes at the cost of (1)
intensive manual annotations and (2) an inherent restriction in the scope of
data relevant for learning. In this work, we explore unsupervised feature
learning from unlabeled video. We introduce a novel object-centric approach to
temporal coherence that encourages similar representations to be learned for
object-like regions segmented from nearby frames. Our framework relies on a
Siamese-triplet network to train a deep convolutional neural network (CNN)
representation. Compared to existing temporal coherence methods, our idea has
the advantage of lightweight preprocessing of the unlabeled video (no tracking
required) while still being able to extract object-level regions from which to
learn invariances. Furthermore, as we show in results on several standard
datasets, our method typically achieves substantial accuracy gains over
competing unsupervised methods for image classification and retrieval tasks.
Nonparametric Regression with Adaptive Truncation via a Convex Hierarchical Penalty
Authors: Asad Haris, Ali Shojaie, Noah Simon
Date: 2016-11-30 00:00:00
Tasks: Additive models, regression

We consider the problem of non-parametric regression with a potentially large number of covariates. We propose a convex, penalized estimation framework that is particularly well-suited for high-dimensional sparse additive models. The proposed approach combines appealing features of finite basis representation and smoothing penalties for non-parametric estimation. In particular, in the case of additive models, a finite basis representation provides a parsimonious representation for fitted functions but is not adaptive when component functions posses different levels of complexity. On the other hand, a smoothing spline type penalty on the component functions is adaptive but does not offer a parsimonious representation of the estimated function. The proposed approach simultaneously achieves parsimony and adaptivity in a computationally efficient framework. We demonstrate these properties through empirical studies on both real and simulated datasets. We show that our estimator converges at the minimax rate for functions within a hierarchical class. We further establish minimax rates for a large class of sparse additive models. The proposed method is implemented using an efficient algorithm that scales similarly to the Lasso with the number of covariates and samples size.
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
Authors: Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre, Kees Vissers
Date: 2016-12-01 00:00:00
Tasks: General Classification

Research has shown that convolutional neural networks contain significant
redundancy, and high classification accuracy can be obtained even when weights
and activations are reduced from floating point to binary values. In this
paper, we present FINN, a framework for building fast and flexible FPGA
accelerators using a flexible heterogeneous streaming architecture. By
utilizing a novel set of optimizations that enable efficient mapping of
binarized neural networks to hardware, we implement fully connected,
convolutional and pooling layers, with per-layer compute resources being
tailored to user-provided throughput requirements. On a ZC706 embedded FPGA
platform drawing less than 25 W total system power, we demonstrate up to 12.3
million image classifications per second with 0.31 {\mu}s latency on the MNIST
dataset with 95.8% accuracy, and 21906 image classifications per second with
283 {\mu}s latency on the CIFAR-10 and SVHN datasets with respectively 80.1%
and 94.9% accuracy. To the best of our knowledge, ours are the fastest
classification rates reported to date on these benchmarks.
In Teacher We Trust: Learning Compressed Models for Pedestrian Detection
Authors: Jonathan Shen, Noranart Vesdapunt, Vishnu N. Boddeti, Kris M. Kitani
Date: 2016-12-01 00:00:00
Tasks: Knowledge Distillation, Pedestrian Detection

Deep convolutional neural networks continue to advance the state-of-the-art
in many domains as they grow bigger and more complex. It has been observed that
many of the parameters of a large network are redundant, allowing for the
possibility of learning a smaller network that mimics the outputs of the large
network through a process called Knowledge Distillation. We show, however, that
standard Knowledge Distillation is not effective for learning small models for
the task of pedestrian detection. To improve this process, we introduce a
higher-dimensional hint layer to increase information flow. We also estimate
the variance in the outputs of the large network and propose a loss function to
incorporate this uncertainty. Finally, we attempt to boost the complexity of
the small network without increasing its size by using as input hand-designed
features that have been demonstrated to be effective for pedestrian detection.
We succeed in training a model that contains $400\times$ fewer parameters than
the large network while outperforming AlexNet on the Caltech Pedestrian
Dataset.
Incremental Sequence Learning
Authors: Edwin D. de Jong
Date: 2016-11-09 00:00:00
Tasks: General Classification, Incremental Learning, Transfer Learning

Deep learning research over the past years has shown that by increasing the
scope or difficulty of the learning problem over time, increasingly complex
learning problems can be addressed. We study incremental learning in the
context of sequence learning, using generative RNNs in the form of multi-layer
recurrent Mixture Density Networks. While the potential of incremental or
curriculum learning to enhance learning is known, indiscriminate application of
the principle does not necessarily lead to improvement, and it is essential
therefore to know which forms of incremental or curriculum learning have a
positive effect. This research contributes to that aim by comparing three
instantiations of incremental or curriculum learning.
  We introduce Incremental Sequence Learning, a simple incremental approach to
sequence learning. Incremental Sequence Learning starts out by using only the
first few steps of each sequence as training data. Each time a performance
criterion has been reached, the length of the parts of the sequences used for
training is increased.
  We introduce and make available a novel sequence learning task and data set:
predicting and classifying MNIST pen stroke sequences. We find that Incremental
Sequence Learning greatly speeds up sequence learning and reaches the best test
performance level of regular sequence learning 20 times faster, reduces the
test error by 74%, and in general performs more robustly; it displays lower
variance and achieves sustained progress after all three comparison methods
have stopped improving. The other instantiations of curriculum learning do not
result in any noticeable improvement. A trained sequence prediction model is
also used in transfer learning to the task of sequence classification, where it
is found that transfer learning realizes improved classification performance
compared to methods that learn to classify from scratch.
Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes
Authors: Taylor Killian, George Konidaris, Finale Doshi-Velez
Date: 2016-12-01 00:00:00
Tasks: Transfer Learning

Due to physiological variation, patients diagnosed with the same condition
may exhibit divergent, but related, responses to the same treatments. Hidden
Parameter Markov Decision Processes (HiP-MDPs) tackle this transfer-learning
problem by embedding these tasks into a low-dimensional space. However, the
original formulation of HiP-MDP had a critical flaw: the embedding uncertainty
was modeled independently of the agent's state uncertainty, requiring an
unnatural training procedure in which all tasks visited every part of the state
space---possible for robots that can be moved to a particular location,
impossible for human patients. We update the HiP-MDP framework and extend it to
more robustly develop personalized medicine strategies for HIV treatment.
Neural Document Embeddings for Intensive Care Patient Mortality Prediction
Authors: Paulina Grnarova, Florian Schmidt, Stephanie L. Hyland, Carsten Eickhoff
Date: 2016-12-01 00:00:00
Tasks: Document Embedding, Mortality Prediction, Prediction

We present an automatic mortality prediction scheme based on the unstructured
textual content of clinical notes. Proposing a convolutional document embedding
approach, our empirical investigation using the MIMIC-III intensive care
database shows significant performance gains compared to previously employed
methods such as latent topic distributions or generic doc2vec embeddings. These
improvements are especially pronounced for the difficult problem of
post-discharge mortality prediction.
Efficient Pose and Cell Segmentation using Column Generation
Authors: Shaofei Wang, Chong Zhang, Miguel A. Gonzalez-Ballester, Julian Yarkony
Date: 2016-12-01 00:00:00
Tasks: Cell Segmentation, Efficient Exploration, Instance Segmentation, Segmentation, Semantic Segmentation

We study the problems of multi-person pose segmentation in natural images and
instance segmentation in biological images with crowded cells. We formulate
these distinct tasks as integer programs where variables correspond to
poses/cells. To optimize, we propose a generic relaxation scheme for solving
these combinatorial problems using a column generation formulation where the
program for generating a column is solved via exact optimization of very small
scale integer programs. This results in efficient exploration of the spaces of
poses and cells.
TorontoCity: Seeing the World with a Million Eyes
Authors: Shenlong Wang, Min Bai, Gellert Mattyus, Hang Chu, Wenjie Luo, Bin Yang, Justin Liang, Joel Cheverie, Sanja Fidler, Raquel Urtasun
Date: 2016-12-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Instance Segmentation, Semantic Segmentation

In this paper we introduce the TorontoCity benchmark, which covers the full
greater Toronto area (GTA) with 712.5 $km^2$ of land, 8439 $km$ of road and
around 400,000 buildings. Our benchmark provides different perspectives of the
world captured from airplanes, drones and cars driving around the city.
Manually labeling such a large scale dataset is infeasible. Instead, we propose
to utilize different sources of high-precision maps to create our ground truth.
Towards this goal, we develop algorithms that allow us to align all data
sources with the maps while requiring minimal human supervision. We have
designed a wide variety of tasks including building height estimation
(reconstruction), road centerline and curb extraction, building instance
segmentation, building contour extraction (reorganization), semantic labeling
and scene type classification (recognition). Our pilot study shows that most of
these tasks are still difficult for modern convolutional neural networks.
On Regret-Optimal Learning in Decentralized Multi-player Multi-armed Bandits
Authors: Naumaan Nayyar, Dileep Kalathil, Rahul Jain
Date: 2015-05-04 00:00:00
Tasks: Multi-Armed Bandits

We consider the problem of learning in single-player and multiplayer
multiarmed bandit models. Bandit problems are classes of online learning
problems that capture exploration versus exploitation tradeoffs. In a
multiarmed bandit model, players can pick among many arms, and each play of an
arm generates an i.i.d. reward from an unknown distribution. The objective is
to design a policy that maximizes the expected reward over a time horizon for a
single player setting and the sum of expected rewards for the multiplayer
setting. In the multiplayer setting, arms may give different rewards to
different players. There is no separate channel for coordination among the
players. Any attempt at communication is costly and adds to regret. We propose
two decentralizable policies, $\tt E^3$ ($\tt E$-$\tt cubed$) and $\tt
E^3$-$\tt TS$, that can be used in both single player and multiplayer settings.
These policies are shown to yield expected regret that grows at most as
O($\log^{1+\epsilon} T$). It is well known that $\log T$ is the lower bound on
the rate of growth of regret even in a centralized case. The proposed
algorithms improve on prior work where regret grew at O($\log^2 T$). More
fundamentally, these policies address the question of additional cost incurred
in decentralized online learning, suggesting that there is at most an
$\epsilon$-factor cost in terms of order of regret. This solves a problem of
relevance in many domains and had been open for a while.