pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Keyboard Layout Design for Low-Resource Latin-Script Languages
Authors: Theresa Breiner, Chieu Nguyen, Daan van Esch, Jeremy O'Brien
Date: 2019-01-18 00:00:00
Tasks: Diversity, Layout Design

We present our approach to automatically designing and implementing keyboard
layouts on mobile devices for typing low-resource languages written in the
Latin script. For many speakers, one of the barriers in accessing and creating
text content on the web is the absence of input tools for their language. Ease
in typing in these languages would lower technological barriers to online
communication and collaboration, likely leading to the creation of more web
content. Unfortunately, it can be time-consuming to develop layouts manually
even for language communities that use a keyboard layout very similar to
English; starting from scratch requires many configuration files to describe
multiple possible behaviors for each key. With our approach, we only need a
small amount of data in each language to generate keyboard layouts with very
little human effort. This process can help serve speakers of low-resource
languages in a scalable way, allowing us to develop input tools for more
languages. Having input tools that reflect the linguistic diversity of the
world will let as many people as possible use technology to learn, communicate,
and express themselves in their own native languages.
High-speed Video from Asynchronous Camera Array
Authors: Si Lu
Date: 2019-01-17 00:00:00
Tasks: Novel View Synthesis, Vocal Bursts Intensity Prediction

This paper presents a method for capturing high-speed video using an
asynchronous camera array. Our method sequentially fires each sensor in a
camera array with a small time offset and assembles captured frames into a
high-speed video according to the time stamps. The resulting video, however,
suffers from parallax jittering caused by the viewpoint difference among
sensors in the camera array. To address this problem, we develop a dedicated
novel view synthesis algorithm that transforms the video frames as if they were
captured by a single reference sensor. Specifically, for any frame from a
non-reference sensor, we find the two temporally neighboring frames captured by
the reference sensor. Using these three frames, we render a new frame with the
same time stamp as the non-reference frame but from the viewpoint of the
reference sensor. Specifically, we segment these frames into super-pixels and
then apply local content-preserving warping to warp them to form the new frame.
We employ a multi-label Markov Random Field method to blend these warped
frames. Our experiments show that our method can produce high-quality and
high-speed video of a wide variety of scenes with large parallax, scene
dynamics, and camera motion and outperforms several baseline and
state-of-the-art approaches.
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
Authors: Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh
Date: 2019-01-17 00:00:00
Proceeding: NeurIPS 2019 12

The variational auto-encoder (VAE) is a popular method for learning a generative model and embeddings of the data. Many real datasets are hierarchically structured. However, traditional VAEs map data in a Euclidean latent space which cannot efficiently embed tree-like structures. Hyperbolic spaces with negative curvature can. We therefore endow VAEs with a Poincar\'e ball model of hyperbolic geometry as a latent space and rigorously derive the necessary methods to work with two main Gaussian generalisations on that space. We empirically show better generalisation to unseen data than the Euclidean counterpart, and can qualitatively and quantitatively better recover hierarchical structures.
A Survey of the Recent Architectures of Deep Convolutional Neural Networks
Authors: Asifullah Khan, Anabia Sohail, Umme Zahoora, Aqsa Saeed Qureshi
Date: 2019-01-17 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection, speech-recognition, Speech Recognition

Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition. The powerful learning ability of deep CNN is primarily due to the use of multiple feature extraction stages that can automatically learn representations from the data. The availability of a large amount of data and improvement in the hardware technology has accelerated the research in CNNs, and recently interesting deep CNN architectures have been reported. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the significant improvement in the representational capacity of the deep CNN is achieved through architectural innovations. Notably, the ideas of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing have gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and, consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges, and applications of CNN are also provided.
Multi-Scale Attention Network for Crowd Counting
Authors: Rahul Rama Varior, Bing Shuai, Joseph Tighe, Davide Modolo
Date: 2019-01-17 00:00:00
Tasks: Crowd Counting

In crowd counting datasets, people appear at different scales, depending on their distance from the camera. To address this issue, we propose a novel multi-branch scale-aware attention network that exploits the hierarchical structure of convolutional neural networks and generates, in a single forward pass, multi-scale density predictions from different layers of the architecture. To aggregate these maps into our final prediction, we present a new soft attention mechanism that learns a set of gating masks. Furthermore, we introduce a scale-aware loss function to regularize the training of different branches and guide them to specialize on a particular scale. As this new training requires annotations for the size of each head, we also propose a simple, yet effective technique to estimate them automatically. Finally, we present an ablation study on each of these components and compare our approach against the literature on 4 crowd counting datasets: UCF-QNRF, ShanghaiTech A & B and UCF_CC_50. Our approach achieves state-of-the-art on all them with a remarkable improvement on UCF-QNRF (+25% reduction in error).
Learning a Generative Model of Cancer Metastasis
Authors: Benjamin Kompa, Beau Coker
Date: 2019-01-17 00:00:00
Tasks: Disentanglement, General Classification, model

We introduce a Unified Disentanglement Network (UFDN) trained on The Cancer
Genome Atlas (TCGA). We demonstrate that the UFDN learns a biologically
relevant latent space of gene expression data by applying our network to two
classification tasks of cancer status and cancer type. Our UFDN specific
algorithms perform comparably to random forest methods. The UFDN allows for
continuous, partial interpolation between distinct cancer types. Furthermore,
we perform an analysis of differentially expressed genes between skin cutaneous
melanoma(SKCM) samples and the same samples interpolated into glioblastoma
(GBM). We demonstrate that our interpolations learn relevant metagenes that
recapitulate known glioblastoma mechanisms and suggest possible starting points
for investigations into the metastasis of SKCM into GBM.
GO Gradient for Expectation-Based Objectives
Authors: Yulai Cong, Miaoyun Zhao, Ke Bai, Lawrence Carin
Date: 2019-01-17 00:00:00
Proceeding: ICLR 2019 5

Within many machine learning algorithms, a fundamental problem concerns
efficient calculation of an unbiased gradient wrt parameters $\gammav$ for
expectation-based objectives $\Ebb_{q_{\gammav} (\yv)} [f(\yv)]$. Most existing
methods either (i) suffer from high variance, seeking help from (often)
complicated variance-reduction techniques; or (ii) they only apply to
reparameterizable continuous random variables and employ a reparameterization
trick. To address these limitations, we propose a General and One-sample (GO)
gradient that (i) applies to many distributions associated with
non-reparameterizable continuous or discrete random variables, and (ii) has the
same low-variance as the reparameterization trick. We find that the GO gradient
often works well in practice based on only one Monte Carlo sample (although one
can of course use more samples if desired). Alongside the GO gradient, we
develop a means of propagating the chain rule through distributions, yielding
statistical back-propagation, coupling neural networks to common random
variables.
FARSA: Fully Automated Roadway Safety Assessment
Authors: Weilian Song, Scott Workman, Armin Hadzic, Xu Zhang, Eric Green, Mei Chen, Reginald Souleyrette, Nathan Jacobs
Date: 2019-01-17 00:00:00

This paper addresses the task of road safety assessment. An emerging approach
for conducting such assessments in the United States is through the US Road
Assessment Program (usRAP), which rates roads from highest risk (1 star) to
lowest (5 stars). Obtaining these ratings requires manual, fine-grained
labeling of roadway features in street-level panoramas, a slow and costly
process. We propose to automate this process using a deep convolutional neural
network that directly estimates the star rating from a street-level panorama,
requiring milliseconds per image at test time. Our network also estimates many
other road-level attributes, including curvature, roadside hazards, and the
type of median. To support this, we incorporate task-specific attention layers
so the network can focus on the panorama regions that are most useful for a
particular task. We evaluated our approach on a large dataset of real-world
images from two US states. We found that incorporating additional tasks, and
using a semi-supervised training approach, significantly reduced overfitting
problems, allowed us to optimize more layers of the network, and resulted in
higher accuracy.
Instance-Level Microtubule Tracking
Authors: Samira Masoudi, Afsaneh Razi, Cameron H. G. Wright, Jay C. Gatlin, Ulas Bagci
Date: 2019-01-17 00:00:00
Tasks: Hyperparameter Optimization

We propose a new method of instance-level microtubule (MT) tracking in time-lapse image series using recurrent attention. Our novel deep learning algorithm segments individual MTs at each frame. Segmentation results from successive frames are used to assign correspondences among MTs. This ultimately generates a distinct path trajectory for each MT through the frames. Based on these trajectories, we estimate MT velocities. To validate our proposed technique, we conduct experiments using real and simulated data. We use statistics derived from real time-lapse series of MT gliding assays to simulate realistic MT time-lapse image series in our simulated data. This dataset is employed as pre-training and hyperparameter optimization for our network before training on the real data. Our experimental results show that the proposed supervised learning algorithm improves the precision for MT instance velocity estimation drastically to 71.3% from the baseline result (29.3%). We also demonstrate how the inclusion of temporal information into our deep network can reduce the false negative rates from 67.8% (baseline) down to 28.7% (proposed). Our findings in this work are expected to help biologists characterize the spatial arrangement of MTs, specifically the effects of MT-MT interactions.
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
Authors: Hongteng Xu, Dixin Luo, Hongyuan Zha, Lawrence Carin
Date: 2019-01-17 00:00:00
Tasks: Graph Matching

A novel Gromov-Wasserstein learning framework is proposed to jointly match (align) graphs and learn embedding vectors for the associated graph nodes. Using Gromov-Wasserstein discrepancy, we measure the dissimilarity between two graphs and find their correspondence, according to the learned optimal transport. The node embeddings associated with the two graphs are learned under the guidance of the optimal transport, the distance of which not only reflects the topological structure of each graph but also yields the correspondence across the graphs. These two learning steps are mutually-beneficial, and are unified here by minimizing the Gromov-Wasserstein discrepancy with structural regularizers. This framework leads to an optimization problem that is solved by a proximal point method. We apply the proposed method to matching problems in real-world networks, and demonstrate its superior performance compared to alternative approaches.
Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison
Authors: Thomas Villmann, John Ravichandran, Andrea Villmann, David Nebel, Marika Kaden
Date: 2019-01-17 00:00:00
Tasks: Classification, General Classification, Quantization, regression

An appropriate choice of the activation function (like ReLU, sigmoid or
swish) plays an important role in the performance of (deep) multilayer
perceptrons (MLP) for classification and regression learning. Prototype-based
classification learning methods like (generalized) learning vector quantization
(GLVQ) are powerful alternatives. These models also deal with activation
functions but here they are applied to the so-called classifier function
instead. In this paper we investigate successful candidates of activation
functions known for MLPs for application in GLVQ and their influence on the
performance.
PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation
Authors: Amod Jog, Andrew Hoopes, Douglas N. Greve, Koen van Leemput, Bruce Fischl
Date: 2019-01-17 00:00:00
Tasks: Brain Segmentation

With the advent of convolutional neural networks~(CNN), supervised learning
methods are increasingly being used for whole brain segmentation. However, a
large, manually annotated training dataset of labeled brain images required to
train such supervised methods is frequently difficult to obtain or create. In
addition, existing training datasets are generally acquired with a homogeneous
magnetic resonance imaging~(MRI) acquisition protocol. CNNs trained on such
datasets are unable to generalize on test data with different acquisition
protocols. Modern neuroimaging studies and clinical trials are necessarily
multi-center initiatives with a wide variety of acquisition protocols. Despite
stringent protocol harmonization practices, it is very difficult to standardize
the gamut of MRI imaging parameters across scanners, field strengths, receive
coils etc., that affect image contrast. In this paper we propose a CNN-based
segmentation algorithm that, in addition to being highly accurate and fast, is
also resilient to variation in the input acquisition. Our approach relies on
building approximate forward models of pulse sequences that produce a typical
test image. For a given pulse sequence, we use its forward model to generate
plausible, synthetic training examples that appear as if they were acquired in
a scanner with that pulse sequence. Sampling over a wide variety of pulse
sequences results in a wide variety of augmented training examples that help
build an image contrast invariant model. Our method trains a single CNN that
can segment input MRI images with acquisition parameters as disparate as
$T_1$-weighted and $T_2$-weighted contrasts with only $T_1$-weighted training
data. The segmentations generated are highly accurate with state-of-the-art
results~(overall Dice overlap$=0.94$), with a fast run time~($\approx$ 45
seconds), and consistent across a wide range of acquisition protocols.
Optimizing Deep Neural Networks with Multiple Search Neuroevolution
Authors: Ahmed Aly, David Weikersdorfer, Claire Delaunay
Date: 2019-01-17 00:00:00
Tasks: global-optimization

This paper presents an evolutionary metaheuristic called Multiple Search
Neuroevolution (MSN) to optimize deep neural networks. The algorithm attempts
to search multiple promising regions in the search space simultaneously,
maintaining sufficient distance between them. It is tested by training neural
networks for two tasks, and compared with other optimization algorithms. The
first task is to solve Global Optimization functions with challenging
topographies. We found to MSN to outperform classic optimization algorithms
such as Evolution Strategies, reducing the number of optimization steps
performed by at least 2X.
  The second task is to train a convolutional neural network (CNN) on the
popular MNIST dataset. Using 3.33% of the training set, MSN reaches a
validation accuracy of 90%. Stochastic Gradient Descent (SGD) was able to match
the same accuracy figure, while taking 7X less optimization steps. Despite
lagging, the fact that the MSN metaheurisitc trains a 4.7M-parameter CNN
suggests promise for future development. This is by far the largest network
ever evolved using a pool of only 50 samples.
Ranking Online Consumer Reviews
Authors: Sunil Saumya, Jyoti Prakash Singh, Abdullah Mohammed Baabdullah, Nripendra P. Rana, Yogesh k. Dwivedi
Date: 2019-01-17 00:00:00

The product reviews are posted online in the hundreds and even in the
thousands for some popular products. Handling such a large volume of
continuously generated online content is a challenging task for buyers,
sellers, and even researchers. The purpose of this study is to rank the
overwhelming number of reviews using their predicted helpfulness score. The
helpfulness score is predicted using features extracted from review text data,
product description data and customer question-answer data of a product using
random-forest classifier and gradient boosting regressor. The system is made to
classify the reviews into low or high quality by random-forest classifier. The
helpfulness score of the high-quality reviews is only predicted using gradient
boosting regressor. The helpfulness score of the low-quality reviews is not
calculated because they are never going to be in the top k reviews. They are
just added at the end of the review list to the review-listing website. The
proposed system provides fair review placement on review listing pages and
making all high-quality reviews visible to customers on the top. The
experimental results on data from two popular Indian e-commerce websites
validate our claim, as 3-4 new high-quality reviews are placed in the top ten
reviews along with 5-6 old reviews based on review helpfulness. Our findings
indicate that inclusion of features from product description data and customer
question-answer data improves the prediction accuracy of the helpfulness score.
A Semi-Supervised Machine Learning Approach to Detecting Recurrent Metastatic Breast Cancer Cases Using Linked Cancer Registry and Electronic Medical Record Data
Authors: Albee Y. Ling, Allison W. Kurian, Jennifer L. Caswell-Jin, George W. Sledge Jr., Nigam H. Shah, Suzanne R. Tamang
Date: 2019-01-17 00:00:00
Tasks: Epidemiology, Sensitivity, Specificity

Objectives: Most cancer data sources lack information on metastatic
recurrence. Electronic medical records (EMRs) and population-based cancer
registries contain complementary information on cancer treatment and outcomes,
yet are rarely used synergistically. To enable detection of metastatic breast
cancer (MBC), we applied a semi-supervised machine learning framework to linked
EMR-California Cancer Registry (CCR) data. Materials and Methods: We studied
11,459 female patients treated at Stanford Health Care who received an incident
breast cancer diagnosis from 2000-2014. The dataset consisted of structured
data and unstructured free-text clinical notes from EMR, linked to CCR, a
component of the Surveillance, Epidemiology and End Results (SEER) database. We
extracted information on metastatic disease from patient notes to infer a class
label and then trained a regularized logistic regression model for MBC
classification. We evaluated model performance on a gold standard set of set of
146 patients. Results: There are 495 patients with de novo stage IV MBC, 1,374
patients initially diagnosed with Stage 0-III disease had recurrent MBC, and
9,590 had no evidence of metastatis. The median follow-up time is 96.3 months
(mean 97.8, standard deviation 46.7). The best-performing model incorporated
both EMR and CCR features. The area under the receiver-operating characteristic
curve=0.925 [95% confidence interval: 0.880-0.969], sensitivity=0.861,
specificity=0.878 and overall accuracy=0.870. Discussion and Conclusion: A
framework for MBC case detection combining EMR and CCR data achieved good
sensitivity, specificity and discrimination without requiring expert-labeled
examples. This approach enables population-based research on how patients die
from cancer and may identify novel predictors of cancer recurrence.
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Authors: Atin Sood, Benjamin Elder, Benjamin Herta, Chao Xue, Costas Bekas, A. Cristiano I. Malossi, Debashish Saha, Florian Scheidegger, Ganesh Venkataraman, Gegi Thomas, Giovanni Mariani, Hendrik Strobelt, Horst Samulowitz, Martin Wistuba, Matteo Manica, Mihir Choudhury, Rong Yan, Roxana Istrate, Ruchir Puri, Tejaswini Pedapati
Date: 2019-01-17 00:00:00

Application of neural networks to a vast variety of practical applications is
transforming the way AI is applied in practice. Pre-trained neural network
models available through APIs or capability to custom train pre-built neural
network architectures with customer data has made the consumption of AI by
developers much simpler and resulted in broad adoption of these complex AI
models. While prebuilt network models exist for certain scenarios, to try and
meet the constraints that are unique to each application, AI teams need to
think about developing custom neural network architectures that can meet the
tradeoff between accuracy and memory footprint to achieve the tight constraints
of their unique use-cases. However, only a small proportion of data science
teams have the skills and experience needed to create a neural network from
scratch, and the demand far exceeds the supply. In this paper, we present
NeuNetS : An automated Neural Network Synthesis engine for custom neural
network design that is available as part of IBM's AI OpenScale's product.
NeuNetS is available for both Text and Image domains and can build neural
networks for specific tasks in a fraction of the time it takes today with human
effort, and with accuracy similar to that of human-designed AI models.