pwc-archive/papers-with-abstracts huggingface dataset:
A global feature extraction model for the effective computer aided diagnosis of mild cognitive impairment using structural MRI images
Authors: Chen Fang, Panuwat Janwattanapong, Chunfei Li, Malek Adjouadi
Date: 2017-12-02 00:00:00
Tasks: Binary Classification, General Classification, Specificity

Multiple modalities of biomarkers have been proved to be very sensitive in
assessing the progression of Alzheimer's disease (AD), and using these
modalities and machine learning algorithms, several approaches have been
proposed to assist in the early diagnosis of AD. Among the recent investigated
state-of-the-art approaches, Gaussian discriminant analysis (GDA)-based
approaches have been demonstrated to be more effective and accurate in the
classification of AD, especially for delineating its prodromal stage of mild
cognitive impairment (MCI). Moreover, among those binary classification
investigations, the local feature extraction methods were mostly used, which
made them hardly be applied to a practical computer aided diagnosis system.
Therefore, this study presents a novel global feature extraction model taking
advantage of the recent proposed GDA-based dual high-dimensional decision
spaces, which can significantly improve the early diagnosis performance
comparing to those local feature extraction methods. In the true test using 20%
held-out data, for discriminating the most challenging MCI group from the
cognitively normal control (CN) group, an F1 score of 91.06%, an accuracy of
88.78%, a sensitivity of 91.80%, and a specificity of 83.78% were achieved that
can be considered as the best performance obtained so far.
Where and Who? Automatic Semantic-Aware Person Composition
Authors: Fuwen Tan, Crispin Bernier, Benjamin Cohen, Vicente Ordonez, Connelly Barnes
Date: 2017-06-04 00:00:00

Image compositing is a method used to generate realistic yet fake imagery by
inserting contents from one image to another. Previous work in compositing has
focused on improving appearance compatibility of a user selected foreground
segment and a background image (i.e. color and illumination consistency). In
this work, we instead develop a fully automated compositing model that
additionally learns to select and transform compatible foreground segments from
a large collection given only an input image background. To simplify the task,
we restrict our problem by focusing on human instance composition, because
human segments exhibit strong correlations with their background and because of
the availability of large annotated data. We develop a novel branching
Convolutional Neural Network (CNN) that jointly predicts candidate person
locations given a background image. We then use pre-trained deep feature
representations to retrieve person instances from a large segment database.
Experimental results show that our model can generate composite images that
look visually convincing. We also develop a user interface to demonstrate the
potential application of our method.
Compatibility Family Learning for Item Recommendation and Generation
Authors: Yong-Siang Shih, Kai-Yueh Chang, Hsuan-Tien Lin, Min Sun
Date: 2017-12-02 00:00:00
Tasks: Generative Adversarial Network

Compatibility between items, such as clothes and shoes, is a major factor
among customer's purchasing decisions. However, learning "compatibility" is
challenging due to (1) broader notions of compatibility than those of
similarity, (2) the asymmetric nature of compatibility, and (3) only a small
set of compatible and incompatible items are observed. We propose an end-to-end
trainable system to embed each item into a latent vector and project a query
item into K compatible prototypes in the same space. These prototypes reflect
the broad notions of compatibility. We refer to both the embedding and
prototypes as "Compatibility Family". In our learned space, we introduce a
novel Projected Compatibility Distance (PCD) function which is differentiable
and ensures diversity by aiming for at least one prototype to be close to a
compatible item, whereas none of the prototypes are close to an incompatible
item. We evaluate our system on a toy dataset, two Amazon product datasets, and
Polyvore outfit dataset. Our method consistently achieves state-of-the-art
performance. Finally, we show that we can visualize the candidate compatible
prototypes using a Metric-regularized Conditional Generative Adversarial
Network (MrCGAN), where the input is a projected prototype and the output is a
generated image of a compatible item. We ask human evaluators to judge the
relative compatibility between our generated images and images generated by
CGANs conditioned directly on query items. Our generated images are
significantly preferred, with roughly twice the number of votes as others.
Improved Stability of Whole Brain Surface Parcellation with Multi-Atlas Segmentation
Authors: Yuankai Huo, Shunxing Bao, Prasanna Parvathaneni, Bennett A. Landman
Date: 2017-12-02 00:00:00
Tasks: Brain Segmentation, Segmentation, Surface Reconstruction

Whole brain segmentation and cortical surface parcellation are essential in
understanding the anatomical-functional relationships of the brain. Multi-atlas
segmentation has been regarded as one of the leading segmentation methods for
the whole brain segmentation. In our recent work, the multi-atlas technique has
been adapted to surface reconstruction using a method called Multi-atlas CRUISE
(MaCRUISE). The MaCRUISE method not only performed consistent volume-surface
analyses but also showed advantages on robustness compared with the FreeSurfer
method. However, a detailed surface parcellation was not provided by MaCRUISE,
which hindered the region of interest (ROI) based analyses on surfaces. Herein,
the MaCRUISE surface parcellation (MaCRUISEsp) method is proposed to perform
the surface parcellation upon the inner, central and outer surfaces that are
reconstructed from MaCRUISE. MaCRUISEsp parcellates inner, central and outer
surfaces with 98 cortical labels respectively using a volume segmentation based
surface parcellation (VSBSP), following a topological correction step. To
validate the performance of MaCRUISEsp, 21 scan-rescan magnetic resonance
imaging (MRI) T1 volume pairs from the Kirby21 dataset were used to perform a
reproducibility analyses. MaCRUISEsp achieved 0.948 on median Dice Similarity
Coefficient (DSC) for central surfaces. Meanwhile, FreeSurfer achieved 0.905
DSC for inner surfaces and 0.881 DSC for outer surfaces, while the proposed
method achieved 0.929 DSC for inner surfaces and 0.835 DSC for outer surfaces.
Qualitatively, the results are encouraging, but are not directly comparable as
the two approaches use different definitions of cortical labels.
Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks
Authors: Yuankai Huo, Zhoubing Xu, Shunxing Bao, Camilo Bermudez, Andrew J. Plassard, Jiaqi Liu, Yuang Yao, Albert Assad, Richard G. Abramson, Bennett A. Landman
Date: 2017-12-02 00:00:00
Tasks: Image Segmentation, Organ Segmentation, Segmentation, Semantic Segmentation

Spleen volume estimation using automated image segmentation technique may be
used to detect splenomegaly (abnormally enlarged spleen) on Magnetic Resonance
Imaging (MRI) scans. In recent years, Deep Convolutional Neural Networks (DCNN)
segmentation methods have demonstrated advantages for abdominal organ
segmentation. However, variations in both size and shape of the spleen on MRI
images may result in large false positive and false negative labeling when
deploying DCNN based methods. In this paper, we propose the Splenomegaly
Segmentation Network (SSNet) to address spatial variations when segmenting
extraordinarily large spleens. SSNet was designed based on the framework of
image-to-image conditional generative adversarial networks (cGAN).
Specifically, the Global Convolutional Network (GCN) was used as the generator
to reduce false negatives, while the Markovian discriminator (PatchGAN) was
used to alleviate false positives. A cohort of clinically acquired 3D MRI scans
(both T1 weighted and T2 weighted) from patients with splenomegaly were used to
train and test the networks. The experimental results demonstrated that a mean
Dice coefficient of 0.9260 and a median Dice coefficient of 0.9262 using SSNet
on independently tested MRI volumes of patients with splenomegaly.
Motif-based Rule Discovery for Predicting Real-valued Time Series
Authors: Yuanduo He, Xu Chu, Juguang Peng, Jingyue Gao, Yasha Wang
Date: 2017-09-14 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction, valid

Time series prediction is of great significance in many applications and has
attracted extensive attention from the data mining community. Existing work
suggests that for many problems, the shape in the current time series may
correlate an upcoming shape in the same or another series. Therefore, it is a
promising strategy to associate two recurring patterns as a rule's antecedent
and consequent: the occurrence of the antecedent can foretell the occurrence of
the consequent, and the learned shape of consequent will give accurate
predictions. Earlier work employs symbolization methods, but the symbolized
representation maintains too little information of the original series to mine
valid rules. The state-of-the-art work, though directly manipulating the
series, fails to segment the series precisely for seeking
antecedents/consequents, resulting in inaccurate rules in common scenarios. In
this paper, we propose a novel motif-based rule discovery method, which
utilizes motif discovery to accurately extract frequently occurring consecutive
subsequences, i.e. motifs, as antecedents/consequents. It then investigates the
underlying relationships between motifs by matching motifs as rule candidates
and ranking them based on the similarities. Experimental results on real open
datasets show that the proposed approach outperforms the baseline method by
23.9%. Furthermore, it extends the applicability from single time series to
multiple ones.
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
Authors: Anusha Nagabandi, Gregory Kahn, Ronald S. Fearing, Sergey Levine
Date: 2017-08-08 00:00:00
Tasks: Deep Reinforcement Learning, model, Model-based Reinforcement Learning, Model Predictive Control, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-free deep reinforcement learning algorithms have been shown to be
capable of learning a wide range of robotic skills, but typically require a
very large number of samples to achieve good performance. Model-based
algorithms, in principle, can provide for much more efficient learning, but
have proven difficult to extend to expressive, high-capacity models such as
deep neural networks. In this work, we demonstrate that medium-sized neural
network models can in fact be combined with model predictive control (MPC) to
achieve excellent sample complexity in a model-based reinforcement learning
algorithm, producing stable and plausible gaits to accomplish various complex
locomotion tasks. We also propose using deep neural network dynamics models to
initialize a model-free learner, in order to combine the sample efficiency of
model-based approaches with the high task-specific performance of model-free
methods. We empirically demonstrate on MuJoCo locomotion tasks that our pure
model-based approach trained on just random action data can follow arbitrary
trajectories with excellent sample efficiency, and that our hybrid algorithm
can accelerate model-free learning on high-speed benchmark tasks, achieving
sample efficiency gains of 3-5x on swimmer, cheetah, hopper, and ant agents.
Videos can be found at https://sites.google.com/view/mbmf
Adaptive Sampled Softmax with Kernel Based Sampling
Authors: Guy Blanc, Steffen Rendle
Date: 2017-12-02 00:00:00
Proceeding: ICML 2018 7

Softmax is the most commonly used output function for multiclass problems and
is widely used in areas such as vision, natural language processing, and
recommendation. A softmax model has linear costs in the number of classes which
makes it too expensive for many real-world problems. A common approach to speed
up training involves sampling only some of the classes at each training step.
It is known that this method is biased and that the bias increases the more the
sampling distribution deviates from the output distribution. Nevertheless,
almost any recent work uses simple sampling distributions that require a large
sample size to mitigate the bias. In this work, we propose a new class of
kernel based sampling methods and develop an efficient sampling algorithm.
Kernel based sampling adapts to the model as it is trained, thus resulting in
low bias. Kernel based sampling can be easily applied to many models because it
relies only on the model's last hidden layer. We empirically study the
trade-off of bias, sampling distribution and sample size and show that kernel
based sampling results in low bias with few samples.
Improvised Comedy as a Turing Test
Authors: Kory Wallace Mathewson, Piotr Mirowski
Date: 2017-11-23 00:00:00

The best improvisational theatre actors can make any scene partner, of any
skill level or ability, appear talented and proficient in the art form, and
thus "make them shine". To challenge this improvisational paradigm, we built an
artificial intelligence (AI) trained to perform live shows alongside human
actors for human audiences. Over the course of 30 performances to a combined
audience of almost 3000 people, we have refined theatrical games which involve
combinations of human and (at times, adversarial) AI actors. We have developed
specific scene structures to include audience participants in interesting ways.
Finally, we developed a complete show structure that submitted the audience to
a Turing test and observed their suspension of disbelief, which we believe is
key for human/non-human theatre co-creation.
Variational Encoding of Complex Dynamics
Authors: Carlos X. Hernández, Hannah K. Wayment-Steele, Mohammad M. Sultan, Brooke E. Husic, Vijay S. Pande
Date: 2017-11-23 00:00:00
Tasks: Protein Folding, Time Series, Time Series Analysis

Often the analysis of time-dependent chemical and biophysical systems
produces high-dimensional time-series data for which it can be difficult to
interpret which individual features are most salient. While recent work from
our group and others has demonstrated the utility of time-lagged co-variate
models to study such systems, linearity assumptions can limit the compression
of inherently nonlinear dynamics into just a few characteristic components.
Recent work in the field of deep learning has led to the development of
variational autoencoders (VAE), which are able to compress complex datasets
into simpler manifolds. We present the use of a time-lagged VAE, or variational
dynamics encoder (VDE), to reduce complex, nonlinear processes to a single
embedding with high fidelity to the underlying dynamics. We demonstrate how the
VDE is able to capture nontrivial dynamics in a variety of examples, including
Brownian dynamics and atomistic protein folding. Additionally, we demonstrate a
method for analyzing the VDE model, inspired by saliency mapping, to determine
what features are selected by the VDE model to describe dynamics. The VDE
presents an important step in applying techniques from deep learning to more
accurately model and interpret complex biophysics.
Towards understanding feedback from supermassive black holes using convolutional neural networks
Authors: Stanislav Fort
Date: 2017-12-02 00:00:00

Supermassive black holes at centers of clusters of galaxies strongly interact
with their host environment via AGN feedback. Key tracers of such activity are
X-ray cavities -- regions of lower X-ray brightness within the cluster. We
present an automatic method for detecting, and characterizing X-ray cavities in
noisy, low-resolution X-ray images. We simulate clusters of galaxies, insert
cavities into them, and produce realistic low-quality images comparable to
observations at high redshifts. We then train a custom-built convolutional
neural network to generate pixel-wise analysis of presence of cavities in a
cluster. A ResNet architecture is then used to decode radii of cavities from
the pixel-wise predictions. We surpass the accuracy, stability, and speed of
current visual inspection based methods on simulated data.
Fine-grained Pattern Matching Over Streaming Time Series
Authors: Rong Kang, Chen Wang, Peng Wang, Yuting Ding, Jian-Min Wang
Date: 2017-10-27 00:00:00
Tasks: Time Series, Time Series Analysis

Pattern matching of streaming time series with lower latency under limited
computing resource comes to a critical problem, especially as the growth of
Industry 4.0 and Industry Internet of Things. However, against traditional
single pattern matching problem, a pattern may contain multiple segments
representing different statistical properties or physical meanings for more
precise and expressive matching in real world. Hence, we formulate a new
problem, called "fine-grained pattern matching", which allows users to specify
varied granularities of matching deviation to different segments of a given
pattern, and fuzzy regions for adaptive breakpoints determination between
consecutive segments. In this paper, we propose a novel two-phase approach. In
the pruning phase, we introduce Equal-Length Block (ELB) representation
together with Block-Skipping Pruning (BSP) policy, which guarantees low cost
feature calculation, effective pruning and no false dismissals. In the
post-processing phase, a delta-function is proposed to enable us to conduct
exact matching in linear complexity. Extensive experiments are conducted to
evaluate on synthetic and real-world datasets, which illustrates that our
algorithm outperforms the brute-force method and MSM, a multi-step filter
mechanism over the multi-scaled representation.
Bayesian Semi-nonnegative Tri-matrix Factorization to Identify Pathways Associated with Cancer Types
Authors: Sunho Park, Tae Hyun Hwang
Date: 2017-12-01 00:00:00

Identifying altered pathways that are associated with specific cancer types
can potentially bring a significant impact on cancer patient treatment.
Accurate identification of such key altered pathways information can be used to
develop novel therapeutic agents as well as to understand the molecular
mechanisms of various types of cancers better. Tri-matrix factorization is an
efficient tool to learn associations between two different entities (e.g.,
cancer types and pathways in our case) from data. To successfully apply
tri-matrix factorization methods to biomedical problems, biological prior
knowledge such as pathway databases or protein-protein interaction (PPI)
networks, should be taken into account in the factorization model. However, it
is not straightforward in the Bayesian setting even though Bayesian methods are
more appealing than point estimate methods, such as a maximum likelihood or a
maximum posterior method, in the sense that they calculate distributions over
variables and are robust against overfitting. We propose a Bayesian
(semi-)nonnegative matrix factorization model for human cancer genomic data,
where the biological prior knowledge represented by a pathway database and a
PPI network is taken into account in the factorization model through a finite
dependent Beta-Bernoulli prior. We tested our method on The Cancer Genome Atlas
(TCGA) dataset and found that the pathways identified by our method can be used
as a prognostic biomarkers for patient subgroup identification.
Multi-Content GAN for Few-Shot Font Style Transfer
Authors: Samaneh Azadi, Matthew Fisher, Vladimir Kim, Zhaowen Wang, Eli Shechtman, Trevor Darrell
Date: 2017-12-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Font Style Transfer, Style Transfer

In this work, we focus on the challenge of taking partial observations of
highly-stylized text and generalizing the observations to generate unobserved
glyphs in the ornamented typeface. To generate a set of multi-content images
following a consistent style from very few examples, we propose an end-to-end
stacked conditional GAN model considering content along channels and style
along network layers. Our proposed network transfers the style of given glyphs
to the contents of unseen ones, capturing highly stylized fonts found in the
real-world such as those on movie posters or infographics. We seek to transfer
both the typographic stylization (ex. serifs and ears) as well as the textual
stylization (ex. color gradients and effects.) We base our experiments on our
collected data set including 10,000 fonts with different styles and demonstrate
effective generalization from a very small number of observed glyphs.
Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks
Authors: Jumana Dakka, Pouya Bashivan, Mina Gheiratmand, Irina Rish, Shantenu Jha, Russell Greiner
Date: 2017-12-01 00:00:00
Tasks: Functional Connectivity

Smart systems that can accurately diagnose patients with mental disorders and
identify effective treatments based on brain functional imaging data are of
great applicability and are gaining much attention. Most previous machine
learning studies use hand-designed features, such as functional connectivity,
which does not maintain the potential useful information in the spatial
relationship between brain regions and the temporal profile of the signal in
each region. Here we propose a new method based on recurrent-convolutional
neural networks to automatically learn useful representations from segments of
4-D fMRI recordings. Our goal is to exploit both spatial and temporal
information in the functional MRI movie (at the whole-brain voxel level) for
identifying patients with schizophrenia.
Prediction-Constrained Topic Models for Antidepressant Recommendation
Authors: Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy, Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez
Date: 2017-12-01 00:00:00
Tasks: Prediction, Topic Models

Supervisory signals can help topic models discover low-dimensional data
representations that are more interpretable for clinical tasks. We propose a
framework for training supervised latent Dirichlet allocation that balances two
goals: faithful generative explanations of high-dimensional data and accurate
prediction of associated class labels. Existing approaches fail to balance
these goals by not properly handling a fundamental asymmetry: the intended task
is always predicting labels from data, not data from labels. Our new
prediction-constrained objective trains models that predict labels from heldout
data well while also producing good generative likelihoods and interpretable
topic-word parameters. In a case study on predicting depression medications
from electronic health records, we demonstrate improved recommendations
compared to previous supervised topic models and high- dimensional logistic
regression from words alone.
Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection
Authors: Onur Ozdemir, Benjamin Woodward, Andrew A. Berlin
Date: 2017-12-01 00:00:00
Tasks: Deep Learning

Motivated by the problem of computer-aided detection (CAD) of pulmonary
nodules, we introduce methods to propagate and fuse uncertainty information in
a multi-stage Bayesian convolutional neural network (CNN) architecture. The
question we seek to answer is "can we take advantage of the model uncertainty
provided by one deep learning model to improve the performance of the
subsequent deep learning models and ultimately of the overall performance in a
multi-stage Bayesian deep learning architecture?". Our experiments show that
propagating uncertainty through the pipeline enables us to improve the overall
performance in terms of both final prediction accuracy and model confidence.