pwc-archive/papers-with-abstracts huggingface dataset:
eXclusive Autoencoder (XAE) for Nucleus Detection and Classification on Hematoxylin and Eosin (H&E) Stained Histopathological Images
Authors: Chao-Hui Huang, Daniel Racoceanu
Date: 2018-11-27 00:00:00
Tasks: Classification, General Classification, Miscellaneous

In this paper, we introduced a novel feature extraction approach, named
exclusive autoencoder (XAE), which is a supervised version of autoencoder (AE),
able to largely improve the performance of nucleus detection and classification
on hematoxylin and eosin (H&E) histopathological images. The proposed XAE can
be used in any AE-based algorithm, as long as the data labels are also provided
in the feature extraction phase. In the experiments, we evaluated the
performance of an approach which is the combination of an XAE and a fully
connected neural network (FCN) and compared with some AE-based methods. For a
nucleus detection problem (considered as a nucleus/non-nucleus classification
problem) on breast cancer H&E images, the F-score of the proposed XAE+FCN
approach achieved 96.64% while the state-of-the-art was at 84.49%. For nucleus
classification on colorectal cancer H&E images, with the annotations of four
categories of epithelial, inflammatory, fibroblast and miscellaneous nuclei.
The F-score of the proposed method reached 70.4%. We also proposed a lymphocyte
segmentation method. In the step of lymphocyte detection, we have compared with
cutting-edge technology and gained improved performance from 90% to 98.67%. We
also proposed an algorithm for lymphocyte segmentation based on nucleus
detection and classification. The obtained Dice coefficient achieved 88.31%
while the cutting-edge approach was at 74%.
A Compositional Textual Model for Recognition of Imperfect Word Images
Authors: Wei Tang, John Corring, Ying Wu, Gang Hua
Date: 2018-11-27 00:00:00
Tasks: Optical Character Recognition (OCR), Printed Text Recognition

Printed text recognition is an important problem for industrial OCR systems.
Printed text is constructed in a standard procedural fashion in most settings.
We develop a mathematical model for this process that can be applied to the
backward inference problem of text recognition from an image. Through ablation
experiments we show that this model is realistic and that a multi-task
objective setting can help to stabilize estimation of its free parameters,
enabling use of conventional deep learning methods. Furthermore, by directly
modeling the geometric perturbations of text synthesis we show that our model
can help recover missing characters from incomplete text regions, the bane of
multicomponent OCR systems, enabling recognition even when the detection
returns incomplete information.
Distributed traffic light control at uncoupled intersections with real-world topology by deep reinforcement learning
Authors: Mark Schutera, Niklas Goby, Stefan Smolarek, Markus Reischl
Date: 2018-11-27 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This work examines the implications of uncoupled intersections with local
real-world topology and sensor setup on traffic light control approaches.
Control approaches are evaluated with respect to: Traffic flow, fuel
consumption and noise emission at intersections.
  The real-world road network of Friedrichshafen is depicted, preprocessed and
the present traffic light controlled intersections are modeled with respect to
state space and action space.
  Different strategies, containing fixed-time, gap-based and time-based control
approaches as well as our deep reinforcement learning based control approach,
are implemented and assessed. Our novel DRL approach allows for modeling the
TLC action space, with respect to phase selection as well as selection of
transition timings. It was found that real-world topologies, and thus
irregularly arranged intersections have an influence on the performance of
traffic light control approaches. This is even to be observed within the same
intersection types (n-arm, m-phases). Moreover we could show, that these
influences can be efficiently dealt with by our deep reinforcement learning
based control approach.
CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss
Authors: Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi Nobashi, Daniel L. Rubin
Date: 2018-11-27 00:00:00
Tasks: CPU, Data Augmentation, GPU, Image Segmentation, Organ Segmentation, Segmentation, Semantic Segmentation, Unbalanced Segmentation

Fully-convolutional neural networks have achieved superior performance in a
variety of image segmentation tasks. However, their training requires laborious
manual annotation of large datasets, as well as acceleration by parallel
processors with high-bandwidth memory, such as GPUs. We show that simple models
can achieve competitive accuracy for organ segmentation on CT images when
trained with extensive data augmentation, which leverages existing graphics
hardware to quickly apply geometric and photometric transformations to 3D image
data. On 3 mm^3 CT volumes, our GPU implementation is 2.6-8X faster than a
widely-used CPU version, including communication overhead. We also show how to
automatically generate training labels using rudimentary morphological
operations, which are efficiently computed by 3D Fourier transforms. We
combined fully-automatic labels for the lungs and bone with semi-automatic ones
for the liver, kidneys and bladder, to create a dataset of 130 labeled CT
scans. To achieve the best results from data augmentation, our model uses the
intersection-over-union (IOU) loss function, a close relative of the Dice loss.
We discuss its mathematical properties and explain why it outperforms the usual
weighted cross-entropy loss for unbalanced segmentation tasks. We conclude that
there is no unique IOU loss function, as the naive one belongs to a broad
family of functions with the same essential properties. When combining data
augmentation with the IOU loss, our model achieves a Dice score of 78-92% for
each organ. The trained model, code and dataset will be made publicly
available, to further medical imaging research.
Grammars and reinforcement learning for molecule optimization
Authors: Egor Kraev
Date: 2018-11-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), valid

We seek to automate the design of molecules based on specific chemical
properties. Our primary contributions are a simpler method for generating
SMILES strings guaranteed to be chemically valid, using a combination of a new
context-free grammar for SMILES and additional masking logic; and casting the
molecular property optimization as a reinforcement learning problem,
specifically best-of-batch policy gradient applied to a Transformer model
architecture. This approach uses substantially fewer model steps per atom than
earlier approaches, thus enabling generation of larger molecules, and beats
previous state-of-the art baselines by a significant margin. Applying
reinforcement learning to a combination of a custom context-free grammar with
additional masking to enforce non-local constraints is applicable to any
optimization of a graph structure under a mixture of local and nonlocal
constraints.
Undermining User Privacy on Mobile Devices Using AI
Authors: Berk Gulmezoglu, Andreas Zankl, M. Caner Tol, Saad Islam, Thomas Eisenbarth, Berk Sunar
Date: 2018-11-27 00:00:00

Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users. This is because applications leave distinct footprints in the processor, which can be used by malware to infer user activities. In this work, we show that these inference attacks are considerably more practical when combined with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with Deep Learning methods including Convolutional Neural Networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zeropermission App in well under a minute. The App thereby detects running applications with an accuracy of 98% and reveals opened websites and streaming videos by monitoring the LLC for at most 6 seconds. This is possible, since Deep Learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics such as random line replacement policies. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to implement and execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users.
Understanding the impact of entropy on policy optimization
Authors: Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans
Date: 2018-11-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Entropy regularization is commonly used to improve policy optimization in reinforcement learning. It is believed to help with \emph{exploration} by encouraging the selection of more stochastic policies. In this work, we analyze this claim using new visualizations of the optimization landscape based on randomly perturbing the loss function. We first show that even with access to the exact gradient, policy optimization is difficult due to the geometry of the objective function. Then, we qualitatively show that in some environments, a policy with higher entropy can make the optimization landscape smoother, thereby connecting local optima and enabling the use of larger learning rates. This paper presents new tools for understanding the optimization landscape, shows that policy entropy serves as a regularizer, and highlights the challenge of designing general-purpose policy optimization algorithms.
DLHub: Model and Data Serving for Science
Authors: Ryan Chard, Zhuozhao Li, Kyle Chard, Logan Ward, Yadu Babuji, Anna Woodard, Steve Tuecke, Ben Blaiszik, Michael J. Franklin, Ian Foster
Date: 2018-11-27 00:00:00
Tasks: Distributed Computing, model

While the Machine Learning (ML) landscape is evolving rapidly, there has been
a relative lag in the development of the "learning systems" needed to enable
broad adoption. Furthermore, few such systems are designed to support the
specialized requirements of scientific ML. Here we present the Data and
Learning Hub for science (DLHub), a multi-tenant system that provides both
model repository and serving capabilities with a focus on science applications.
DLHub addresses two significant shortcomings in current systems. First, its
selfservice model repository allows users to share, publish, verify, reproduce,
and reuse models, and addresses concerns related to model reproducibility by
packaging and distributing models and all constituent components. Second, it
implements scalable and low-latency serving capabilities that can leverage
parallel and distributed computing resources to democratize access to published
models through a simple web interface. Unlike other model serving frameworks,
DLHub can store and serve any Python 3-compatible model or processing function,
plus multiple-function pipelines. We show that relative to other model serving
systems including TensorFlow Serving, SageMaker, and Clipper, DLHub provides
greater capabilities, comparable performance without memoization and batching,
and significantly better performance when the latter two techniques can be
employed. We also describe early uses of DLHub for scientific applications.
Self-Supervised GANs via Auxiliary Rotation Loss
Authors: Ting Chen, Xiaohua Zhai, Marvin Ritter, Mario Lucic, Neil Houlsby
Date: 2018-11-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Generation, Representation Learning

Conditional GANs are at the forefront of natural image synthesis. The main
drawback of such models is the necessity for labeled data. In this work we
exploit two popular unsupervised learning techniques, adversarial training and
self-supervision, and take a step towards bridging the gap between conditional
and unconditional GANs. In particular, we allow the networks to collaborate on
the task of representation learning, while being adversarial with respect to
the classic GAN game. The role of self-supervision is to encourage the
discriminator to learn meaningful feature representations which are not
forgotten during training. We test empirically both the quality of the learned
image representations, and the quality of the synthesized images. Under the
same conditions, the self-supervised GAN attains a similar performance to
state-of-the-art conditional counterparts. Finally, we show that this approach
to fully unsupervised learning can be scaled to attain an FID of 23.4 on
unconditional ImageNet generation.
Calibrating Uncertainties in Object Localization Task
Authors: Buu Phan, Rick Salay, Krzysztof Czarnecki, Vahdat Abdelzad, Taylor Denouden, Sachin Vernekar
Date: 2018-11-27 00:00:00
Tasks: Autonomous Driving, Decision Making, Object, object-detection, Object Detection, Object Localization, regression

In many safety-critical applications such as autonomous driving and surgical
robots, it is desirable to obtain prediction uncertainties from object
detection modules to help support safe decision-making. Specifically, such
modules need to estimate the probability of each predicted object in a given
region and the confidence interval for its bounding box. While recent Bayesian
deep learning methods provide a principled way to estimate this uncertainty,
the estimates for the bounding boxes obtained using these methods are
uncalibrated. In this paper, we address this problem for the single-object
localization task by adapting an existing technique for calibrating regression
models. We show, experimentally, that the resulting calibrated model obtains
more reliable uncertainty estimates.
Iterative Transformer Network for 3D Point Cloud
Authors: Wentao Yuan, David Held, Christoph Mertz, Martial Hebert
Date: 2018-11-27 00:00:00
Tasks: General Classification, Object, Segmentation

3D point cloud is an efficient and flexible representation of 3D structures. Recently, neural networks operating on point clouds have shown superior performance on 3D understanding tasks such as shape classification and part segmentation. However, performance on such tasks is evaluated on complete shapes aligned in a canonical frame, while real world 3D data are partial and unaligned. A key challenge in learning from partial, unaligned point cloud data is to learn features that are invariant or equivariant with respect to geometric transformations. To address this challenge, we propose the Iterative Transformer Network (IT-Net), a network module that canonicalizes the pose of a partial object with a series of 3D rigid transformations predicted in an iterative fashion. We demonstrate the efficacy of IT-Net as an anytime pose estimator from partial point clouds without using complete object models. Further, we show that IT-Net achieves superior performance over alternative 3D transformer networks on various tasks, such as partial shape classification and object part segmentation.
Partitioned Variational Inference: A unified framework encompassing federated and continual learning
Authors: Thang D. Bui, Cuong V. Nguyen, Siddharth Swaroop, Richard E. Turner
Date: 2018-11-27 00:00:00
Tasks: Continual Learning, Distributed Computing, Federated Learning, Variational Inference

Variational inference (VI) has become the method of choice for fitting many
modern probabilistic models. However, practitioners are faced with a fragmented
literature that offers a bewildering array of algorithmic options. First, the
variational family. Second, the granularity of the updates e.g. whether the
updates are local to each data point and employ message passing or global.
Third, the method of optimization (bespoke or blackbox, closed-form or
stochastic updates, etc.). This paper presents a new framework, termed
Partitioned Variational Inference (PVI), that explicitly acknowledges these
algorithmic dimensions of VI, unifies disparate literature, and provides
guidance on usage. Crucially, the proposed PVI framework allows us to identify
new ways of performing VI that are ideally suited to challenging learning
scenarios including federated learning (where distributed computing is
leveraged to process non-centralized data) and continual learning (where new
data and tasks arrive over time and must be accommodated quickly). We showcase
these new capabilities by developing communication-efficient federated training
of Bayesian neural networks and continual learning for Gaussian process models
with private pseudo-points. The new methods significantly outperform the
state-of-the-art, whilst being almost as straightforward to implement as
standard VI.
You Look Twice: GaterNet for Dynamic Filter Selection in CNNs
Authors: Zhourong Chen, Yang Li, Samy Bengio, Si Si
Date: 2018-11-27 00:00:00
Proceeding: CVPR 2019 6

The concept of conditional computation for deep nets has been proposed
previously to improve model performance by selectively using only parts of the
model conditioned on the sample it is processing. In this paper, we investigate
input-dependent dynamic filter selection in deep convolutional neural networks
(CNNs). The problem is interesting because the idea of forcing different parts
of the model to learn from different types of samples may help us acquire
better filters in CNNs, improve the model generalization performance and
potentially increase the interpretability of model behavior. We propose a novel
yet simple framework called GaterNet, which involves a backbone and a gater
network. The backbone network is a regular CNN that performs the major
computation needed for making a prediction, while a global gater network is
introduced to generate binary gates for selectively activating filters in the
backbone network based on each input. Extensive experiments on CIFAR and
ImageNet datasets show that our models consistently outperform the original
models with a large margin. On CIFAR-10, our model also improves upon
state-of-the-art results.
Semantically-aware population health risk analyses
Authors: Alexander New, Sabbir M. Rashid, John S. Erickson, Deborah L. McGuinness, Kristin P. Bennett
Date: 2018-11-27 00:00:00
Tasks: BIG-bench Machine Learning

One primary task of population health analysis is the identification of risk
factors that, for some subpopulation, have a significant association with some
health condition. Examples include finding lifestyle factors associated with
chronic diseases and finding genetic mutations associated with diseases in
precision health. We develop a combined semantic and machine learning system
that uses a health risk ontology and knowledge graph (KG) to dynamically
discover risk factors and their associated subpopulations. Semantics and the
novel supervised cadre model make our system explainable. Future population
health studies are easily performed and documented with provenance by
specifying additional input and output KG cartridges.
Scan2CAD: Learning CAD Model Alignment in RGB-D Scans
Authors: Armen Avetisyan, Manuel Dahnert, Angela Dai, Manolis Savva, Angel X. Chang, Matthias Nießner
Date: 2018-11-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Reconstruction

We present Scan2CAD, a novel data-driven method that learns to align clean 3D
CAD models from a shape database to the noisy and incomplete geometry of a
commodity RGB-D scan. For a 3D reconstruction of an indoor scene, our method
takes as input a set of CAD models, and predicts a 9DoF pose that aligns each
model to the underlying scan geometry. To tackle this problem, we create a new
scan-to-CAD alignment dataset based on 1506 ScanNet scans with 97607 annotated
keypoint pairs between 14225 CAD models from ShapeNet and their counterpart
objects in the scans. Our method selects a set of representative keypoints in a
3D scan for which we find correspondences to the CAD geometry. To this end, we
design a novel 3D CNN architecture that learns a joint embedding between real
and synthetic objects, and from this predicts a correspondence heatmap. Based
on these correspondence heatmaps, we formulate a variational energy
minimization that aligns a given set of CAD models to the reconstruction. We
evaluate our approach on our newly introduced Scan2CAD benchmark where we
outperform both handcrafted feature descriptor as well as state-of-the-art CNN
based methods by 21.39%.
Continuous Trade-off Optimization between Fast and Accurate Deep Face Detectors
Authors: Petru Soviany, Radu Tudor Ionescu
Date: 2018-11-27 00:00:00
Tasks: Face Detection

Although deep neural networks offer better face detection results than
shallow or handcrafted models, their complex architectures come with higher
computational requirements and slower inference speeds than shallow neural
networks. In this context, we study five straightforward approaches to achieve
an optimal trade-off between accuracy and speed in face detection. All the
approaches are based on separating the test images in two batches, an easy
batch that is fed to a faster face detector and a difficult batch that is fed
to a more accurate yet slower detector. We conduct experiments on the AFW and
the FDDB data sets, using MobileNet-SSD as the fast face detector and S3FD
(Single Shot Scale-invariant Face Detector) as the accurate face detector, both
models being pre-trained on the WIDER FACE data set. Our experiments show that
the proposed difficulty metrics compare favorably to a random split of the
images.
Document classification using a Bi-LSTM to unclog Brazil's supreme court
Authors: Fabricio Ataides Braz, Nilton Correia da Silva, Teofilo Emidio de Campos, Felipe Borges S. Chaves, Marcelo H. S. Ferreira, Pedro Henrique Inazawa, Victor H. D. Coelho, Bernardo Pablo Sukiennik, Ana Paula Goncalves Soares de Almeida, Flavio Barros Vidal, Davi Alves Bezerra, Davi B. Gusmao, Gabriel G. Ziegler, Ricardo V. C. Fernandes, Roberta Zumblick, Fabiano Hartmann Peixoto
Date: 2018-11-27 00:00:00
Tasks: Document Classification, General Classification

The Brazilian court system is currently the most clogged up judiciary system
in the world. Thousands of lawsuit cases reach the supreme court every day.
These cases need to be analyzed in order to be associated to relevant tags and
allocated to the right team. Most of the cases reach the court as raster
scanned documents with widely variable levels of quality. One of the first
steps for the analysis is to classify these documents. In this paper we present
a Bidirectional Long Short-Term Memory network (Bi-LSTM) to classify these
pieces of legal document.
Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection
Authors: Luke R Harries, Suyi Zhang, Geoffroy Dubourg-Felonneau, James H R Farmery, Jonathan Sinai, Belle Taylor, Nirmesh Patel, John W Cassidy, John Shawe-Taylor, Harry W Clifford
Date: 2018-11-26 00:00:00
Tasks: BIG-bench Machine Learning

DNA sequencing to identify genetic variants is becoming increasingly valuable
in clinical settings. Assessment of variants in such sequencing data is
commonly implemented through Bayesian heuristic algorithms. Machine learning
has shown great promise in improving on these variant calls, but the input for
these is still a standardized "pile-up" image, which is not always best suited.
In this paper, we present a novel method for generating images from DNA
sequencing data, which interlaces the human reference genome with personalized
sequencing output, to maximize usage of sequencing reads and improve machine
learning algorithm performance. We demonstrate the success of this in improving
standard germline variant calling. We also furthered this approach to include
somatic variant calling across tumor/normal data with Siamese networks. These
approaches can be used in machine learning applications on sequencing data with
the hope of improving clinical outcomes, and are freely available for
noncommercial use at www.ccg.ai.