pwc-archive/papers-with-abstracts huggingface dataset:
Greedy Graph Searching for Vascular Tracking in Angiographic Image Sequences
Authors: Huihui Fang, Jian Yang, Jianjun Zhu, Danni Ai, Yong Huang, Yurong Jiang, Hong Song, Yongtian Wang
Date: 2018-05-25 00:00:00
Tasks: Diagnostic, Dynamic Time Warping, Image Registration

Vascular tracking of angiographic image sequences is one of the most
clinically important tasks in the diagnostic assessment and interventional
guidance of cardiac disease. However, this task can be challenging to
accomplish because of unsatisfactory angiography image quality and complex
vascular structures. Thus, this study proposed a new greedy graph search-based
method for vascular tracking. Each vascular branch is separated from the
vasculature and is tracked independently. Then, all branches are combined using
topology optimization, thereby resulting in complete vasculature tracking. A
gray-based image registration method was applied to determine the tracking
range, and the deformation field between two consecutive frames was calculated.
The vascular branch was described using a vascular centerline extraction method
with multi-probability fusion-based topology optimization. We introduce an
undirected acyclic graph establishment technique. A greedy search method was
proposed to acquire all possible paths in the graph that might match the
tracked vascular branch. The final tracking result was selected by branch
matching using dynamic time warping with a DAISY descriptor. The solution to
the problem reflected both the spatial and textural information between
successive frames. Experimental results demonstrated that the proposed method
was effective and robust for vascular tracking, attaining a F1 score of 0.89 on
a single branch dataset and 0.88 on a vessel tree dataset. This approach
provided a universal solution to address the problem of filamentary structure
tracking.
Automated Verification of Neural Networks: Advances, Challenges and Perspectives
Authors: Francesco Leofante, Nina Narodytska, Luca Pulina, Armando Tacchella
Date: 2018-05-25 00:00:00
Tasks: BIG-bench Machine Learning

Neural networks are one of the most investigated and widely used techniques
in Machine Learning. In spite of their success, they still find limited
application in safety- and security-related contexts, wherein assurance about
networks' performances must be provided. In the recent past, automated
reasoning techniques have been proposed by several researchers to close the gap
between neural networks and applications requiring formal guarantees about
their behavior. In this work, we propose a primer of such techniques and a
comprehensive categorization of existing approaches for the automated
verification of neural networks. A discussion about current limitations and
directions for future investigation is provided to foster research on this
topic at the crossroads of Machine Learning and Automated Reasoning.
Polynomially Coded Regression: Optimal Straggler Mitigation via Data Encoding
Authors: Songze Li, Seyed Mohammadreza Mousavi Kalan, Qian Yu, Mahdi Soltanolkotabi, A. Salman Avestimehr
Date: 2018-05-24 00:00:00
Tasks: regression

We consider the problem of training a least-squares regression model on a
large dataset using gradient descent. The computation is carried out on a
distributed system consisting of a master node and multiple worker nodes. Such
distributed systems are significantly slowed down due to the presence of
slow-running machines (stragglers) as well as various communication
bottlenecks. We propose "polynomially coded regression" (PCR) that
substantially reduces the effect of stragglers and lessens the communication
burden in such systems. The key idea of PCR is to encode the partial data
stored at each worker, such that the computations at the workers can be viewed
as evaluating a polynomial at distinct points. This allows the master to
compute the final gradient by interpolating this polynomial. PCR significantly
reduces the recovery threshold, defined as the number of workers the master has
to wait for prior to computing the gradient. In particular, PCR requires a
recovery threshold that scales inversely proportionally with the amount of
computation/storage available at each worker. In comparison, state-of-the-art
straggler-mitigation schemes require a much higher recovery threshold that only
decreases linearly in the per worker computation/storage load. We prove that
PCR's recovery threshold is near minimal and within a factor two of the best
possible scheme. Our experiments over Amazon EC2 demonstrate that compared with
state-of-the-art schemes, PCR improves the run-time by 1.50x ~ 2.36x with
naturally occurring stragglers, and by as much as 2.58x ~ 4.29x with artificial
stragglers.
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction
Authors: Pengda Qin, Weiran Xu, William Yang Wang
Date: 2018-05-24 00:00:00
Proceeding: ACL 2018 7
Tasks: Relation, Relation Classification, Relation Extraction, Sentence

Distant supervision can effectively label data for relation extraction, but
suffers from the noise labeling problem. Recent works mainly perform soft
bag-level noise reduction strategies to find the relatively better samples in a
sentence bag, which is suboptimal compared with making a hard decision of false
positive samples in sentence level. In this paper, we introduce an adversarial
learning framework, which we named DSGAN, to learn a sentence-level
true-positive generator. Inspired by Generative Adversarial Networks, we regard
the positive samples generated by the generator as the negative samples to
train the discriminator. The optimal generator is obtained until the
discrimination ability of the discriminator has the greatest decline. We adopt
the generator to filter distant supervision training dataset and redistribute
the false positive instances into the negative set, in which way to provide a
cleaned dataset for relation classification. The experimental results show that
the proposed strategy significantly improves the performance of distant
supervision relation extraction comparing to state-of-the-art systems.
Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning
Authors: Pengda Qin, Weiran Xu, William Yang Wang
Date: 2018-05-24 00:00:00
Proceeding: ACL 2018 7
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Relation, Relation Extraction, Sentence

Distant supervision has become the standard method for relation extraction.
However, even though it is an efficient method, it does not come at no
cost---The resulted distantly-supervised training samples are often very noisy.
To combat the noise, most of the recent state-of-the-art approaches focus on
selecting one-best sentence or calculating soft attention weights over the set
of the sentences of one specific entity pair. However, these methods are
suboptimal, and the false positive problem is still a key stumbling bottleneck
for the performance. We argue that those incorrectly-labeled candidate
sentences must be treated with a hard decision, rather than being dealt with
soft attention weights. To do this, our paper describes a radical solution---We
explore a deep reinforcement learning strategy to generate the false-positive
indicator, where we automatically recognize false positives for each relation
type without any supervised information. Unlike the removal operation in the
previous studies, we redistribute them into the negative examples. The
experimental results show that the proposed strategy significantly improves the
performance of distant supervision comparing to state-of-the-art systems.
Superconducting Optoelectronic Neurons I: General Principles
Authors: Jeffrey M. Shainline, Sonia M. Buckley, Adam N. McCaughan, Jeff Chiles, Richard P. Mirin, Sae Woo Nam
Date: 2018-05-04 00:00:00

The design of neural hardware is informed by the prominence of differentiated
processing and information integration in cognitive systems. The central role
of communication leads to the principal assumption of the hardware platform:
signals between neurons should be optical to enable fanout and communication
with minimal delay. The requirement of energy efficiency leads to the
utilization of superconducting detectors to receive single-photon signals. We
discuss the potential of superconducting optoelectronic hardware to achieve the
spatial and temporal information integration advantageous for cognitive
processing, and we consider physical scaling limits based on light-speed
communication. We introduce the superconducting optoelectronic neurons and
networks that are the subject of the subsequent papers in this series.
Multi-Task Determinantal Point Processes for Recommendation
Authors: Romain Warlop, Jérémie Mary, Mike Gartrell
Date: 2018-05-24 00:00:00
Tasks: Diversity, General Classification, Multi-class Classification, Point Processes, Product Recommendation

Determinantal point processes (DPPs) have received significant attention in
the recent years as an elegant model for a variety of machine learning tasks,
due to their ability to elegantly model set diversity and item quality or
popularity. Recent work has shown that DPPs can be effective models for product
recommendation and basket completion tasks. We present an enhanced DPP model
that is specialized for the task of basket completion, the multi-task DPP. We
view the basket completion problem as a multi-class classification problem, and
leverage ideas from tensor factorization and multi-class classification to
design the multi-task DPP model. We evaluate our model on several real-world
datasets, and find that the multi-task DPP provides significantly better
predictive quality than a number of state-of-the-art models.
Fairness GAN
Authors: Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, Kush R. Varshney
Date: 2018-05-24 00:00:00
Tasks: Decision Making, Fairness

In this paper, we introduce the Fairness GAN, an approach for generating a
dataset that is plausibly similar to a given multimedia dataset, but is more
fair with respect to protected attributes in allocative decision making. We
propose a novel auxiliary classifier GAN that strives for demographic parity or
equality of opportunity and show empirical results on several datasets,
including the CelebFaces Attributes (CelebA) dataset, the Quick, Draw!\
dataset, and a dataset of soccer player images and the offenses they were
called for. The proposed formulation is well-suited to absorbing unlabeled
data; we leverage this to augment the soccer dataset with the much larger
CelebA dataset. The methodology tends to improve demographic parity and
equality of opportunity while generating plausible images.
Structure Learning from Time Series with False Discovery Control
Authors: Bernat Guillen Pegueroles, Bhanukiran Vinzamuri, Karthikeyan Shanmugam, Steve Hedden, Jonathan D. Moyer, Kush R. Varshney
Date: 2018-05-24 00:00:00
Tasks: Time Series, Time Series Analysis

We consider the Granger causal structure learning problem from time series
data. Granger causal algorithms predict a 'Granger causal effect' between two
variables by testing if prediction error of one decreases significantly in the
absence of the other variable among the predictor covariates. Almost all
existing Granger causal algorithms condition on a large number of variables
(all but two variables) to test for effects between a pair of variables. We
propose a new structure learning algorithm called MMPC-p inspired by the well
known MMHC algorithm for non-time series data. We show that under some
assumptions, the algorithm provides false discovery rate control. The algorithm
is sound and complete when given access to perfect directed information testing
oracles. We also outline a novel tester for the linear Gaussian case. We show
through our extensive experiments that the MMPC-p algorithm scales to larger
problems and has improved statistical power compared to existing state of the
art for large sparse graphs. We also apply our algorithm on a global
development dataset and validate our findings with subject matter experts.
Diffusion Maps for Textual Network Embedding
Authors: Xinyuan Zhang, Yitong Li, Dinghan Shen, Lawrence Carin
Date: 2018-05-24 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: General Classification, Link Prediction, Network Embedding

Textual network embedding leverages rich text information associated with the
network to learn low-dimensional vectorial representations of vertices. Rather
than using typical natural language processing (NLP) approaches, recent
research exploits the relationship of texts on the same edge to graphically
embed text. However, these models neglect to measure the complete level of
connectivity between any two texts in the graph. We present diffusion maps for
textual network embedding (DMTE), integrating global structural information of
the graph to capture the semantic relatedness between texts, with a
diffusion-convolution operation applied on the text inputs. In addition, a new
objective function is designed to efficiently preserve the high-order proximity
using the graph diffusion. Experimental results show that the proposed approach
outperforms state-of-the-art methods on the vertex-classification and
link-prediction tasks.
Boolean Decision Rules via Column Generation
Authors: Sanjeeb Dash, Oktay Günlük, Dennis Wei
Date: 2018-05-24 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: General Classification

This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification. An integer program is formulated to optimally trade classification accuracy for rule simplicity. Column generation (CG) is used to efficiently search over an exponential number of candidate clauses (conjunctions or disjunctions) without the need for heuristic rule mining. This approach also bounds the gap between the selected rule set and the best possible rule set on the training data. To handle large datasets, we propose an approximate CG algorithm using randomization. Compared to three recently proposed alternatives, the CG algorithm dominates the accuracy-simplicity trade-off in 7 out of 15 datasets. When maximized for accuracy, CG is competitive with rule learners designed for this purpose, sometimes finding significantly simpler solutions that are no less accurate.
Performing Co-Membership Attacks Against Deep Generative Models
Authors: Kin Sum Liu, Chaowei Xiao, Bo Li, Jie Gao
Date: 2018-05-24 00:00:00

In this paper we propose a new membership attack method called co-membership attacks against deep generative models including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Specifically, membership attack aims to check whether a given instance x was used in the training data or not. A co-membership attack checks whether the given bundle of n instances were in the training, with the prior knowledge that the bundle was either entirely used in the training or none at all. Successful membership attacks can compromise the privacy of training data when the generative model is published. Our main idea is to cast membership inference of target data x as the optimization of another neural network (called the attacker network) to search for the latent encoding to reproduce x. The final reconstruction error is used directly to conclude whether x was in the training data or not. We conduct extensive experiments on a variety of datasets and generative models showing that: our attacker network outperforms prior membership attacks; co-membership attacks can be substantially more powerful than single attacks; and VAEs are more susceptible to membership attacks compared to GANs.
3D Sketching using Multi-View Deep Volumetric Prediction
Authors: Johanna Delanoy, Mathieu Aubry, Phillip Isola, Alexei A. Efros, Adrien Bousseau
Date: 2017-07-26 00:00:00
Tasks: 3D Reconstruction, Prediction

Sketch-based modeling strives to bring the ease and immediacy of drawing to
the 3D world. However, while drawings are easy for humans to create, they are
very challenging for computers to interpret due to their sparsity and
ambiguity. We propose a data-driven approach that tackles this challenge by
learning to reconstruct 3D shapes from one or more drawings. At the core of our
approach is a deep convolutional neural network (CNN) that predicts occupancy
of a voxel grid from a line drawing. This CNN provides us with an initial 3D
reconstruction as soon as the user completes a single drawing of the desired
shape. We complement this single-view network with an updater CNN that refines
an existing prediction given a new drawing of the shape created from a novel
viewpoint. A key advantage of our approach is that we can apply the updater
iteratively to fuse information from an arbitrary number of viewpoints, without
requiring explicit stroke correspondences between the drawings. We train both
CNNs by rendering synthetic contour drawings from hand-modeled shape
collections as well as from procedurally-generated abstract shapes. Finally, we
integrate our CNNs in a minimal modeling interface that allows users to
seamlessly draw an object, rotate it to see its 3D reconstruction, and refine
it by re-drawing from another vantage point using the 3D reconstruction as
guidance. The main strengths of our approach are its robustness to freehand
bitmap drawings, its ability to adapt to different object categories, and the
continuum it offers between single-view and multi-view sketch-based modeling.
Generalized Scene Reconstruction
Authors: John K. Leffingwell, Donald J. Meagher, Khan W. Mahmud, Scott Ackerson
Date: 2018-03-22 00:00:00

A new passive approach called Generalized Scene Reconstruction (GSR) enables
"generalized scenes" to be effectively reconstructed. Generalized scenes are
defined to be "boundless" spaces that include non-Lambertian, partially
transmissive, textureless and finely-structured matter. A new data structure
called a plenoptic octree is introduced to enable efficient (database-like)
light and matter field reconstruction in devices such as mobile phones,
augmented reality (AR) glasses and drones. To satisfy threshold requirements
for GSR accuracy, scenes are represented as systems of partially polarized
light, radiometrically interacting with matter. To demonstrate GSR, a prototype
imaging polarimeter is used to reconstruct (in generalized light fields) highly
reflective, hail-damaged automobile body panels. Follow-on GSR experiments are
described.
Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images
Authors: Kuang Gong, Jaewon Yang, Kyungsang Kim, Georges El Fakhri, Youngho Seo, Quanzheng Li
Date: 2017-12-17 00:00:00
Tasks: Image Reconstruction

Positron Emission Tomography (PET) is a functional imaging modality widely
used in neuroscience studies. To obtain meaningful quantitative results from
PET images, attenuation correction is necessary during image reconstruction.
For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance
(MR) images do not reflect attenuation coefficients directly. To address this
issue, we present deep neural network methods to derive the continuous
attenuation coefficients for brain PET imaging from MR images. With only Dixon
MR images as the network input, the existing U-net structure was adopted and
analysis using forty patient data sets shows it is superior than other Dixon
based methods. When both Dixon and zero echo time (ZTE) images are available,
we have proposed a modified U-net structure, named GroupU-net, to efficiently
make use of both Dixon and ZTE information through group convolution modules
when the network goes deeper. Quantitative analysis based on fourteen real
patient data sets demonstrates that both network approaches can perform better
than the standard methods, and the proposed network structure can further
reduce the PET quantification error compared to the U-net structure.
On the Computational Complexity of Model Checking for Dynamic Epistemic Logic with S5 Models
Authors: Ronald de Haan, Iris van de Pol
Date: 2018-05-24 00:00:00

Dynamic epistemic logic (DEL) is a logical framework for representing and reasoning about knowledge change for multiple agents. An important computational task in this framework is the model checking problem, which has been shown to be PSPACE-hard even for S5 models and two agents---in the presence of other features, such as multi-pointed models. We answer open questions in the literature about the complexity of this problem in more restricted settings. We provide a detailed complexity analysis of the model checking problem for DEL, where we consider various combinations of restrictions, such as the number of agents, whether the models are single-pointed or multi-pointed, and whether postconditions are allowed in the updates. In particular, we show that the problem is already PSPACE-hard in (1) the case of one agent, multi-pointed S5 models, and no postconditions, and (2) the case of two agents, only single-pointed S5 models, and no postconditions. In addition, we study the setting where only semi-private announcements are allowed as updates. We show that for this case the problem is already PSPACE-hard when restricted to two agents and three propositional variables. The results that we obtain in this paper help outline the exact boundaries of the restricted settings for which the model checking problem for DEL is computationally tractable.
Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM
Authors: German Abrevaya, Irina Rish, Aleksandr Y. Aravkin, Guillermo Cecchi, James Kozloski, Pablo Polosecki, Peng Zheng, Silvina Ponce Dawson, Juliana Rhee, David Cox
Date: 2018-05-24 00:00:00
Tasks: Data Augmentation, parameter estimation, Time Series Analysis

Many real-world data sets, especially in biology, are produced by complex nonlinear dynamical systems. In this paper, we focus on brain calcium imaging (CaI) of different organisms (zebrafish and rat), aiming to build a model of joint activation dynamics in large neuronal populations, including the whole brain of zebrafish. We propose a new approach for capturing dynamics of temporal SVD components that uses the coupled (multivariate) van der Pol (VDP) oscillator, a nonlinear ordinary differential equation (ODE) model describing neural activity, with a new parameter estimation technique that combines variable projection optimization and stochastic search. We show that the approach successfully handles nonlinearities and hidden state variables in the coupled VDP. The approach is accurate, achieving 0.82 to 0.94 correlation between the actual and model-generated components, and interpretable, as VDP's coupling matrix reveals anatomically meaningful positive (excitatory) and negative (inhibitory) interactions across different brain subsystems corresponding to spatial SVD components. Moreover, VDP is comparable to (or sometimes better than) recurrent neural networks (LSTM) for (short-term) prediction of future brain activity; VDP needs less parameters to train, which was a plus on our small training data. Finally, the overall best predictive method, greatly outperforming both VDP and LSTM in short- and long-term predictive settings on both datasets, was the new hybrid VDP-LSTM approach that used VDP to simulate large domain-specific dataset for LSTM pretraining; note that simple LSTM data-augmentation via noisy versions of training data was much less effective.