pwc-archive/papers-with-abstracts huggingface dataset:
Large-scale mammography CAD with Deformable Conv-Nets
Authors: Stephen Morrell, Zbigniew Wojna, Can Son Khoo, Sebastien Ourselin, Juan Eugenio Iglesias
Date: 2019-02-19 00:00:00

State-of-the-art deep learning methods for image processing are evolving into
increasingly complex meta-architectures with a growing number of modules. Among
them, region-based fully convolutional networks (R-FCN) and deformable
convolutional nets (DCN) can improve CAD for mammography: R-FCN optimizes for
speed and low consumption of memory, which is crucial for processing the high
resolutions of to 50 micrometers used by radiologists. Deformable convolution
and pooling can model a wide range of mammographic findings of different
morphology and scales, thanks to their versatility. In this study, we present a
neural net architecture based on R-FCN / DCN, that we have adapted from the
natural image domain to suit mammograms -- particularly their larger image size
-- without compromising resolution. We trained the network on a large, recently
released dataset (Optimam) including 6,500 cancerous mammograms. By combining
our modern architecture with such a rich dataset, we achieved an area under the
ROC curve of 0.879 for breast-wise detection in the DREAMS challenge (130,000
withheld images), which surpassed all other submissions in the competitive
phase.
DeepBall: Deep Neural-Network Ball Detector
Authors: Jacek Komorowski, Grzegorz Kurzejamski, Grzegorz Sarwas
Date: 2019-02-19 00:00:00
Tasks: General Classification, Object, Position, Sports Ball Detection and Tracking

The paper describes a deep network based object detector specialized for ball
detection in long shot videos. Due to its fully convolutional design, the
method operates on images of any size and produces \emph{ball confidence map}
encoding the position of detected ball. The network uses hypercolumn concept,
where feature maps from different hierarchy levels of the deep convolutional
network are combined and jointly fed to the convolutional classification layer.
This allows boosting the detection accuracy as larger visual context around the
object of interest is taken into account. The method achieves state-of-the-art
results when tested on publicly available ISSIA-CNR Soccer Dataset.
Augmentation for small object detection
Authors: Mate Kisantal, Zbigniew Wojna, Jakub Murawski, Jacek Naruniec, Kyunghyun Cho
Date: 2019-02-19 00:00:00
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Semantic Segmentation, Small Object Detection

In recent years, object detection has experienced impressive progress.
Despite these improvements, there is still a significant gap in the performance
between the detection of small and large objects. We analyze the current
state-of-the-art model, Mask-RCNN, on a challenging dataset, MS COCO. We show
that the overlap between small ground-truth objects and the predicted anchors
is much lower than the expected IoU threshold. We conjecture this is due to two
factors; (1) only a few images are containing small objects, and (2) small
objects do not appear enough even within each image containing them. We thus
propose to oversample those images with small objects and augment each of those
images by copy-pasting small objects many times. It allows us to trade off the
quality of the detector on large objects with that on small objects. We
evaluate different pasting augmentation strategies, and ultimately, we achieve
9.7\% relative improvement on the instance segmentation and 7.1\% on the object
detection of small objects, compared to the current state of the art method on
MS COCO.
Data Efficient Voice Cloning for Neural Singing Synthesis
Authors: Merlijn Blaauw, Jordi Bonada, Ryunosuke Daido
Date: 2019-02-19 00:00:00
Tasks: text-to-speech, Text to Speech, Voice Cloning

There are many use cases in singing synthesis where creating voices from
small amounts of data is desirable. In text-to-speech there have been several
promising results that apply voice cloning techniques to modern deep learning
based models. In this work, we adapt one such technique to the case of singing
synthesis. By leveraging data from many speakers to first create a multispeaker
model, small amounts of target data can then efficiently adapt the model to new
unseen voices. We evaluate the system using listening tests across a number of
different use cases, languages and kinds of data.
Accurate Automatic Segmentation of Amygdala Subnuclei and Modeling of Uncertainty via Bayesian Fully Convolutional Neural Network
Authors: Yilin Liu, Gengyan Zhao, Brendon M. Nacewicz, Nagesh Adluru, Gregory R. Kirk, Peter A Ferrazzano, Martin Styner, Andrew L. Alexander
Date: 2019-02-19 00:00:00
Tasks: Deep Learning

Recent advances in deep learning have improved the segmentation accuracy of
subcortical brain structures, which would be useful in neuroimaging studies of
many neurological disorders. However, most of the previous deep learning work
does not investigate the specific difficulties that exist in segmenting
extremely small but important brain regions such as the amygdala and its
subregions. To tackle this challenging task, a novel 3D Bayesian fully
convolutional neural network was developed to apply a dilated dualpathway
approach that retains fine details and utilizes both local and more global
contextual information to automatically segment the amygdala and its subregions
at high precision. The proposed method provides insights on network design and
sampling strategy that target segmentations of small 3D structures. In
particular, this study confirms that a large context, enabled by a large field
of view, is beneficial for segmenting small objects; furthermore, precise
contextual information enabled by dilated convolutions allows for better
boundary localization, which is critical for examining the morphology of the
structure. In addition, it is demonstrated that the uncertainty information
estimated from our network may be leveraged to identify atypicality in data.
Our method was compared with two state-of-the-art deep learning models and a
traditional multi-atlas approach, and exhibited excellent performance as
measured both by Dice overlap as well as average symmetric surface distance. To
the best of our knowledge, this work is the first deep learning-based approach
that targets the subregions of the amygdala.
Online Learning with Continuous Variations: Dynamic Regret and Reductions
Authors: Ching-An Cheng, Jonathan Lee, Ken Goldberg, Byron Boots
Date: 2019-02-19 00:00:00

Online learning is a powerful tool for analyzing iterative algorithms. However, the classic adversarial setup sometimes fails to capture certain regularity in online problems in practice. Motivated by this, we establish a new setup, called Continuous Online Learning (COL), where the gradient of online loss function changes continuously across rounds with respect to the learner's decisions. We show that COL covers and more appropriately describes many interesting applications, from general equilibrium problems (EPs) to optimization in episodic MDPs. In particular, we show monotone EPs admits a reduction to achieving sublinear static regret in COL. Using this new setup, we revisit the difficulty of sublinear dynamic regret. We prove a fundamental equivalence between achieving sublinear dynamic regret in COL and solving certain EPs. With this insight, we offer conditions for efficient algorithms that achieve sublinear dynamic regret, even when the losses are chosen adaptively without any a priori variation budget. Furthermore, we show for COL a reduction from dynamic regret to both static regret and convergence in the associated EP, allowing us to analyze the dynamic regret of many existing algorithms.
Graph Spectral Characterization of Brain Cortical Morphology
Authors: Sevil Maghsadhagh, Anders Eklund, Hamid Behjat
Date: 2019-02-19 00:00:00

The human brain cortical layer has a convoluted morphology that is unique to
each individual. Characterization of the cortical morphology is necessary in
longitudinal studies of structural brain change, as well as in discriminating
individuals in health and disease. A method for encoding the cortical
morphology in the form of a graph is presented. The design of graphs that
encode the global cerebral hemisphere cortices as well as localized cortical
regions is proposed. Spectral metrics derived from these graphs are then
studied and proposed as descriptors of cortical morphology. As proof-of-concept
of their applicability in characterizing cortical morphology, the metrics are
studied in the context of hemispheric asymmetry as well as gender dependent
discrimination of cortical morphology.
Semantic Neural Machine Translation using AMR
Authors: Linfeng Song, Daniel Gildea, Yue Zhang, Zhiguo Wang, Jinsong Su
Date: 2019-02-19 00:00:00
Proceeding: TACL 2019 3
Tasks: Abstract Meaning Representation, Machine Translation, NMT, Translation

It is intuitive that semantic representations can be useful for machine
translation, mainly because they can help in enforcing meaning preservation and
handling data sparsity (many sentences correspond to one meaning) of machine
translation models. On the other hand, little work has been done on leveraging
semantics for neural machine translation (NMT). In this work, we study the
usefulness of AMR (short for abstract meaning representation) on NMT.
Experiments on a standard English-to-German dataset show that incorporating AMR
as additional knowledge can significantly improve a strong attention-based
sequence-to-sequence neural translation model.
Subspace Methods That Are Resistant to a Limited Number of Features Corrupted by an Adversary
Authors: Chris Mesterharm, Rauf Izmailov, Scott Alexander, Simon Tsang
Date: 2019-02-19 00:00:00
Tasks: subspace methods

In this paper, we consider batch supervised learning where an adversary is allowed to corrupt instances with arbitrarily large noise. The adversary is allowed to corrupt any $l$ features in each instance and the adversary can change their values in any way. This noise is introduced on test instances and the algorithm receives no label feedback for these instances. We provide several subspace voting techniques that can be used to transform existing algorithms and prove data-dependent performance bounds in this setting. The key insight to our results is that we set our parameters so that a significant fraction of the voting hypotheses do not contain corrupt features and, for many real world problems, these uncorrupt hypotheses are sufficient to achieve high accuracy. We empirically validate our approach on several datasets including three new datasets that deal with side channel electromagnetic information.
Accuracy of the Epic Sepsis Prediction Model in a Regional Health System
Authors: Tellen Bennett, Seth Russell, James King, Lisa Schilling, Chan Voong, Nancy Rogers, Bonnie Adrian, Nicholas Bruce, Debashis Ghosh
Date: 2019-02-19 00:00:00
Tasks: regression

Interest in an electronic health record-based computational model that can
accurately predict a patient's risk of sepsis at a given point in time has
grown rapidly in the last several years. Like other EHR vendors, the Epic
Systems Corporation has developed a proprietary sepsis prediction model (ESPM).
Epic developed the model using data from three health systems and penalized
logistic regression. Demographic, comorbidity, vital sign, laboratory,
medication, and procedural variables contribute to the model. The objective of
this project was to compare the predictive performance of the ESPM with a
regional health system's current Early Warning Score-based sepsis detection
program.
Understanding and Controlling Memory in Recurrent Neural Networks
Authors: Doron Haviv, Alexander Rivkind, Omri Barak
Date: 2019-02-19 00:00:00
Tasks: Descriptive

To be effective in sequential data processing, Recurrent Neural Networks (RNNs) are required to keep track of past events by creating memories. While the relation between memories and the network's hidden state dynamics was established over the last decade, previous works in this direction were of a predominantly descriptive nature focusing mainly on locating the dynamical objects of interest. In particular, it remained unclear how dynamical observables affect the performance, how they form and whether they can be manipulated. Here, we utilize different training protocols, datasets and architectures to obtain a range of networks solving a delayed classification task with similar performance, alongside substantial differences in their ability to extrapolate for longer delays. We analyze the dynamics of the network's hidden state, and uncover the reasons for this difference. Each memory is found to be associated with a nearly steady state of the dynamics which we refer to as a 'slow point'. Slow point speeds predict extrapolation performance across all datasets, protocols and architectures tested. Furthermore, by tracking the formation of the slow points we are able to understand the origin of differences between training protocols. Finally, we propose a novel regularization technique that is based on the relation between hidden state speeds and memory longevity. Our technique manipulates these speeds, thereby leading to a dramatic improvement in memory robustness over time, and could pave the way for a new class of regularization methods.
BusyHands: A Hand-Tool Interaction Database for Assembly Tasks Semantic Segmentation
Authors: Roy Shilkrot, Zhi Chai, Minh Hoai
Date: 2019-02-19 00:00:00
Tasks: Segmentation, Semantic Segmentation

Visual segmentation has seen tremendous advancement recently with ready
solutions for a wide variety of scene types, including human hands and other
body parts. However, focus on segmentation of human hands while performing
complex tasks, such as manual assembly, is still severely lacking. Segmenting
hands from tools, work pieces, background and other body parts is extremely
difficult because of self-occlusions and intricate hand grips and poses. In
this paper we introduce BusyHands, a large open dataset of pixel-level
annotated images of hands performing 13 different tool-based assembly tasks,
from both real-world captures and virtual-world renderings. A total of 7906
samples are included in our first-in-kind dataset, with both RGB and depth
images as obtained from a Kinect V2 camera and Blender. We evaluate several
state-of-the-art semantic segmentation methods on our dataset as a proposed
performance benchmark.
DOM-Q-NET: Grounded RL on Structured Language
Authors: Sheng Jia, Jamie Kiros, Jimmy Ba
Date: 2019-02-19 00:00:00
Proceeding: ICLR 2019 5
Tasks: Deep Reinforcement Learning, Graph Neural Network, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Building agents to interact with the web would allow for significant
improvements in knowledge understanding and representation learning. However,
web navigation tasks are difficult for current deep reinforcement learning (RL)
models due to the large discrete action space and the varying number of actions
between the states. In this work, we introduce DOM-Q-NET, a novel architecture
for RL-based web navigation to address both of these problems. It parametrizes
Q functions with separate networks for different action categories: clicking a
DOM element and typing a string input. Our model utilizes a graph neural
network to represent the tree-structured HTML of a standard web page. We
demonstrate the capabilities of our model on the MiniWoB environment where we
can match or outperform existing work without the use of expert demonstrations.
Furthermore, we show 2x improvements in sample efficiency when training in the
multi-task setting, allowing our model to transfer learned behaviours across
tasks.
Fast Neural Network Verification via Shadow Prices
Authors: Vicenc Rubies-Royo, Roberto Calandra, Dusan M. Stipanovic, Claire Tomlin
Date: 2019-02-19 00:00:00
Tasks: Collision Avoidance

To use neural networks in safety-critical settings it is paramount to provide assurances on their runtime operation. Recent work on ReLU networks has sought to verify whether inputs belonging to a bounded box can ever yield some undesirable output. Input-splitting procedures, a particular type of verification mechanism, do so by recursively partitioning the input set into smaller sets. The efficiency of these methods is largely determined by the number of splits the box must undergo before the property can be verified. In this work, we propose a new technique based on shadow prices that fully exploits the information of the problem yielding a more efficient generation of splits than the state-of-the-art. Results on the Airborne Collision Avoidance System (ACAS) benchmark verification tasks show a considerable reduction in the partitions generated which substantially reduces computation times. These results open the door to improved verification methods for a wide variety of machine learning applications including vision and control.
Graph Neural Networks for Social Recommendation
Authors: Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin
Date: 2019-02-19 00:00:00
Tasks: Graph Neural Network, Recommendation Systems

In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key. However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs (e.g., the user-user social graph and the user-item graph). To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations. In particular, we provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec. Our code is available at \url{https://github.com/wenqifan03/GraphRec-WWW19}
Scalable Thompson Sampling via Optimal Transport
Authors: Ruiyi Zhang, Zheng Wen, Changyou Chen, Lawrence Carin
Date: 2019-02-19 00:00:00
Tasks: Decision Making, Sequential Decision Making, Thompson Sampling

Thompson sampling (TS) is a class of algorithms for sequential
decision-making, which requires maintaining a posterior distribution over a
model. However, calculating exact posterior distributions is intractable for
all but the simplest models. Consequently, efficient computation of an
approximate posterior distribution is a crucial problem for scalable TS with
complex models, such as neural networks. In this paper, we use distribution
optimization techniques to approximate the posterior distribution, solved via
Wasserstein gradient flows. Based on the framework, a principled
particle-optimization algorithm is developed for TS to approximate the
posterior efficiently. Our approach is scalable and does not make explicit
distribution assumptions on posterior approximations. Extensive experiments on
both synthetic data and real large-scale data demonstrate the superior
performance of the proposed methods.
Learning Optimal Linear Regularizers
Authors: Matthew Streeter
Date: 2019-02-19 00:00:00

We present algorithms for efficiently learning regularizers that improve
generalization. Our approach is based on the insight that regularizers can be
viewed as upper bounds on the generalization gap, and that reducing the slack
in the bound can improve performance on test data. For a broad class of
regularizers, the hyperparameters that give the best upper bound can be
computed using linear programming. Under certain Bayesian assumptions, solving
the LP lets us "jump" to the optimal hyperparameters given very limited data.
This suggests a natural algorithm for tuning regularization hyperparameters,
which we show to be effective on both real and synthetic data.
Feature Selection for Better Spectral Characterization or: How I Learned to Start Worrying and Love Ensembles
Authors: Sankalp Gilda
Date: 2019-02-19 00:00:00
Tasks: feature selection, Retrieval

An ever-looming threat to astronomical applications of machine learning is
the danger of over-fitting data, also known as the `curse of dimensionality.'
This occurs when there are fewer samples than the number of independent
variables. In this work, we focus on the problem of stellar parameterization
from low-mid resolution spectra, with blended absorption lines. We address this
problem using an iterative algorithm to sequentially prune redundant features
from synthetic PHOENIX spectra, and arrive at an optimal set of wavelengths
with the strongest correlation with each of the output variables -- T$_{\rm
eff}$, $\log g$, and [Fe/H]. We find that at any given resolution, most
features (i.e., absorption lines) are not only redundant, but actually act as
noise and decrease the accuracy of parameter retrieval.
Stochastic Conditional Gradient++
Authors: Hamed Hassani, Amin Karbasi, Aryan Mokhtari, Zebang Shen
Date: 2019-02-19 00:00:00
Tasks: Stochastic Optimization

In this paper, we consider the general non-oblivious stochastic optimization where the underlying stochasticity may change during the optimization procedure and depends on the point at which the function is evaluated. We develop Stochastic Frank-Wolfe++ ($\text{SFW}{++} $), an efficient variant of the conditional gradient method for minimizing a smooth non-convex function subject to a convex body constraint. We show that $\text{SFW}{++} $ converges to an $\epsilon$-first order stationary point by using $O(1/\epsilon^3)$ stochastic gradients. Once further structures are present, $\text{SFW}{++}$'s theoretical guarantees, in terms of the convergence rate and quality of its solution, improve. In particular, for minimizing a convex function, $\text{SFW}{++} $ achieves an $\epsilon$-approximate optimum while using $O(1/\epsilon^2)$ stochastic gradients. It is known that this rate is optimal in terms of stochastic gradient evaluations. Similarly, for maximizing a monotone continuous DR-submodular function, a slightly different form of $\text{SFW}{++} $, called Stochastic Continuous Greedy++ ($\text{SCG}{++} $), achieves a tight $[(1-1/e)\text{OPT} -\epsilon]$ solution while using $O(1/\epsilon^2)$ stochastic gradients. Through an information theoretic argument, we also prove that $\text{SCG}{++} $'s convergence rate is optimal. Finally, for maximizing a non-monotone continuous DR-submodular function, we can achieve a $[(1/e)\text{OPT} -\epsilon]$ solution by using $O(1/\epsilon^2)$ stochastic gradients. We should highlight that our results and our novel variance reduction technique trivially extend to the standard and easier oblivious stochastic optimization settings for (non-)covex and continuous submodular settings.