pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network
Authors: Qi. Wang, Jiahui Qiu, Yangming Zhou, Tong Ruan, Daqi Gao, Ju Gao
Date: 2018-07-18 00:00:00
Tasks: General Classification

Coronary artery disease (CAD) is one of the leading causes of cardiovascular
disease deaths. CAD condition progresses rapidly, if not diagnosed and treated
at an early stage may eventually lead to an irreversible state of the heart
muscle death. Invasive coronary arteriography is the gold standard technique
for CAD diagnosis. Coronary arteriography texts describe which part has
stenosis and how much stenosis is in details. It is crucial to conduct the
severity classification of CAD. In this paper, we employ a recurrent capsule
network (RCN) to extract semantic relations between clinical named entities in
Chinese coronary arteriography texts, through which we can automatically find
out the maximal stenosis for each lumen to inference how severe CAD is
according to the improved method of Gensini. Experimental results on the corpus
collected from Shanghai Shuguang Hospital show that our proposed method
achieves an accuracy of 97.0\% in the severity classification of CAD.
Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions
Authors: Adnan Siraj Rakin, Jin-Feng Yi, Boqing Gong, Deliang Fan
Date: 2018-07-18 00:00:00
Tasks: Quantization

Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks. To this end, many defense approaches that attempt to improve the robustness of DNNs have been proposed. In a separate and yet related area, recent works have explored to quantize neural network weights and activation functions into low bit-width to compress model size and reduce computational complexity. In this work, we find that these two different tracks, namely the pursuit of network compactness and robustness, can be merged into one and give rise to networks of both advantages. To the best of our knowledge, this is the first work that uses quantization of activation functions to defend against adversarial examples. We also propose to train robust neural networks by using adaptive quantization techniques for the activation functions. Our proposed Dynamic Quantized Activation (DQA) is verified through a wide range of experiments with the MNIST and CIFAR-10 datasets under different white-box attack methods, including FGSM, PGD, and C & W attacks. Furthermore, Zeroth Order Optimization and substitute model-based black-box attacks are also considered in this work. The experimental results clearly show that the robustness of DNNs could be greatly improved using the proposed DQA.
On the Interaction Effects Between Prediction and Clustering
Authors: Matt Barnes, Artur Dubrawski
Date: 2018-07-18 00:00:00
Tasks: Clustering, Prediction

Machine learning systems increasingly depend on pipelines of multiple
algorithms to provide high quality and well structured predictions. This paper
argues interaction effects between clustering and prediction (e.g.
classification, regression) algorithms can cause subtle adverse behaviors
during cross-validation that may not be initially apparent. In particular, we
focus on the problem of estimating the out-of-cluster (OOC) prediction loss
given an approximate clustering with probabilistic error rate $p_0$.
Traditional cross-validation techniques exhibit significant empirical bias in
this setting, and the few attempts to estimate and correct for these effects
are intractable on larger datasets. Further, no previous work has been able to
characterize the conditions under which these empirical effects occur, and if
they do, what properties they have. We precisely answer these questions by
providing theoretical properties which hold in various settings, and prove that
expected out-of-cluster loss behavior rapidly decays with even minor clustering
errors. Fortunately, we are able to leverage these same properties to construct
hypothesis tests and scalable estimators necessary for correcting the problem.
Empirical results on benchmark datasets validate our theoretical results and
demonstrate how scaling techniques provide solutions to new classes of
problems.
Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation
Authors: Xiong Lyu, Mickael Binois, Michael Ludkovski
Date: 2018-07-18 00:00:00

We consider the problem of learning the level set for which a noisy black-box function exceeds a given threshold. To efficiently reconstruct the level set, we investigate Gaussian process (GP) metamodels. Our focus is on strongly stochastic samplers, in particular with heavy-tailed simulation noise and low signal-to-noise ratio. To guard against noise misspecification, we assess the performance of three variants: (i) GPs with Student-$t$ observations; (ii) Student-$t$ processes (TPs); and (iii) classification GPs modeling the sign of the response. In conjunction with these metamodels, we analyze several acquisition functions for guiding the sequential experimental designs, extending existing stepwise uncertainty reduction criteria to the stochastic contour-finding context. This also motivates our development of (approximate) updating formulas to efficiently compute such acquisition functions. Our schemes are benchmarked by using a variety of synthetic experiments in 1--6 dimensions. We also consider an application of level set estimation for determining the optimal exercise policy of Bermudan options in finance.
Receiver Operating Characteristic Curves and Confidence Bands for Support Vector Machines
Authors: Daniel J. Luckett, Eric B. Laber, Samer S. El-Kamary, Cheng Fan, Ravi Jhaveri, Charles M. Perou, Fatma M. Shebl, Michael R. Kosorok
Date: 2018-07-17 00:00:00
Tasks: Binary Classification, Decision Making, Diagnostic, Specificity

Many problems that appear in biomedical decision making, such as diagnosing
disease and predicting response to treatment, can be expressed as binary
classification problems. The costs of false positives and false negatives vary
across application domains and receiver operating characteristic (ROC) curves
provide a visual representation of this trade-off. Nonparametric estimators for
the ROC curve, such as a weighted support vector machine (SVM), are desirable
because they are robust to model misspecification. While weighted SVMs have
great potential for estimating ROC curves, their theoretical properties were
heretofore underdeveloped. We propose a method for constructing confidence
bands for the SVM ROC curve and provide the theoretical justification for the
SVM ROC curve by showing that the risk function of the estimated decision rule
is uniformly consistent across the weight parameter. We demonstrate the
proposed confidence band method and the superior sensitivity and specificity of
the weighted SVM compared to commonly used methods in diagnostic medicine using
simulation studies. We present two illustrative examples: diagnosis of
hepatitis C and a predictive model for treatment response in breast cancer.
A Modulation Module for Multi-task Learning with Applications in Image Retrieval
Authors: Xiangyun Zhao, Haoxiang Li, Xiaohui Shen, Xiaodan Liang, Ying Wu
Date: 2018-07-17 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Retrieval, Multi-Task Learning, Retrieval

Multi-task learning has been widely adopted in many computer vision tasks to
improve overall computation efficiency or boost the performance of individual
tasks, under the assumption that those tasks are correlated and complementary
to each other. However, the relationships between the tasks are complicated in
practice, especially when the number of involved tasks scales up. When two
tasks are of weak relevance, they may compete or even distract each other
during joint training of shared parameters, and as a consequence undermine the
learning of all the tasks. This will raise destructive interference which
decreases learning efficiency of shared parameters and lead to low quality loss
local optimum w.r.t. shared parameters. To address the this problem, we propose
a general modulation module, which can be inserted into any convolutional
neural network architecture, to encourage the coupling and feature sharing of
relevant tasks while disentangling the learning of irrelevant tasks with minor
parameters addition. Equipped with this module, gradient directions from
different tasks can be enforced to be consistent for those shared parameters,
which benefits multi-task joint training. The module is end-to-end learnable
without ad-hoc design for specific tasks, and can naturally handle many tasks
at the same time. We apply our approach on two retrieval tasks, face retrieval
on the CelebA dataset [1] and product retrieval on the UT-Zappos50K dataset [2,
3], and demonstrate its advantage over other multi-task learning methods in
both accuracy and storage efficiency.
Adaptive Neural Trees
Authors: Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya Nori
Date: 2018-07-17 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification, Representation Learning

Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures. We unite the two via adaptive neural trees (ANTs) that incorporates representation learning into edges, routing functions and leaf nodes of a decision tree, along with a backpropagation-based training algorithm that adaptively grows the architecture from primitive modules (e.g., convolutional layers). We demonstrate that, whilst achieving competitive performance on classification and regression datasets, ANTs benefit from (i) lightweight inference via conditional computation, (ii) hierarchical separation of features useful to the task e.g. learning meaningful class associations, such as separating natural vs. man-made objects, and (iii) a mechanism to adapt the architecture to the size and complexity of the training dataset.
Integrating Algorithmic Planning and Deep Learning for Partially Observable Navigation
Authors: Peter Karkus, David Hsu, Wee Sun Lee
Date: 2018-07-17 00:00:00
Tasks: Deep Learning, Navigate, Robot Navigation, State Estimation

We propose to take a novel approach to robot system design where each
building block of a larger system is represented as a differentiable program,
i.e. a deep neural network. This representation allows for integrating
algorithmic planning and deep learning in a principled manner, and thus combine
the benefits of model-free and model-based methods. We apply the proposed
approach to a challenging partially observable robot navigation task. The robot
must navigate to a goal in a previously unseen 3-D environment without knowing
its initial location, and instead relying on a 2-D floor map and visual
observations from an onboard camera. We introduce the Navigation Networks
(NavNets) that encode state estimation, planning and acting in a single,
end-to-end trainable recurrent neural network. In preliminary simulation
experiments we successfully trained navigation networks to solve the
challenging partially observable navigation task.
Tensor Methods for Additive Index Models under Discordance and Heterogeneity
Authors: Krishnakumar Balasubramanian, Jianqing Fan, Zhuoran Yang
Date: 2018-07-17 00:00:00

Motivated by the sampling problems and heterogeneity issues common in high-
dimensional big datasets, we consider a class of discordant additive index
models. We propose method of moments based procedures for estimating the
indices of such discordant additive index models in both low and
high-dimensional settings. Our estimators are based on factorizing certain
moment tensors and are also applicable in the overcomplete setting, where the
number of indices is more than the dimensionality of the datasets. Furthermore,
we provide rates of convergence of our estimator in both high and
low-dimensional setting. Establishing such results requires deriving tensor
operator norm concentration inequalities that might be of independent interest.
Finally, we provide simulation results supporting our theory. Our contributions
extend the applicability of tensor methods for novel models in addition to
making progress on understanding theoretical properties of such tensor methods.
Efficient Deep Learning on Multi-Source Private Data
Authors: Nick Hynes, Raymond Cheng, Dawn Song
Date: 2018-07-17 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Privacy Preserving

Machine learning models benefit from large and diverse datasets. Using such
datasets, however, often requires trusting a centralized data aggregator. For
sensitive applications like healthcare and finance this is undesirable as it
could compromise patient privacy or divulge trade secrets. Recent advances in
secure and privacy-preserving computation, including trusted hardware enclaves
and differential privacy, offer a way for mutually distrusting parties to
efficiently train a machine learning model without revealing the training data.
In this work, we introduce Myelin, a deep learning framework which combines
these privacy-preservation primitives, and use it to establish a baseline level
of performance for fully private machine learning.
Modular Semantics and Characteristics for Bipolar Weighted Argumentation Graphs
Authors: Till Mossakowski, Fabian Neuhaus
Date: 2018-07-17 00:00:00
Tasks: All

This paper addresses the semantics of weighted argumentation graphs that are
bipolar, i.e. contain both attacks and supports for arguments. It builds on
previous work by Amgoud, Ben-Naim et. al. We study the various characteristics
of acceptability semantics that have been introduced in these works, and
introduce the notion of a modular acceptability semantics. A semantics is
modular if it cleanly separates aggregation of attacking and supporting
arguments (for a given argument $a$) from the computation of their influence on
$a$'s initial weight.
  We show that the various semantics for bipolar argumentation graphs from the
literature may be analysed as a composition of an aggregation function with an
influence function. Based on this modular framework, we prove general
convergence and divergence theorems. We demonstrate that all well-behaved
modular acceptability semantics converge for all acyclic graphs and that no
sum-based semantics can converge for all graphs. In particular, we show
divergence of Euler-based semantics (Amgoud et al.) for certain cyclic graphs.
Further, we provide the first semantics for bipolar weighted graphs that
converges for all graphs.
Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags
Authors: Onur Güngör, Suzan Üsküdarlı, Tunga Güngör
Date: 2018-07-17 00:00:00
Tasks: Morphological Disambiguation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Previous studies have shown that linguistic features of a word such as
possession, genitive or other grammatical cases can be employed in word
representations of a named entity recognition (NER) tagger to improve the
performance for morphologically rich languages. However, these taggers require
external morphological disambiguation (MD) tools to function which are hard to
obtain or non-existent for many languages. In this work, we propose a model
which alleviates the need for such disambiguators by jointly learning NER and
MD taggers in languages for which one can provide a list of candidate
morphological analyses. We show that this can be done independent of the
morphological annotation schemes, which differ among languages. Our experiments
employing three different model architectures that join these two tasks show
that joint learning improves NER performance. Furthermore, the morphological
disambiguator's performance is shown to be competitive.
Query-Conditioned Three-Player Adversarial Network for Video Summarization
Authors: Yu-jia Zhang, Michael Kampffmeyer, Xiaodan Liang, Min Tan, Eric P. Xing
Date: 2018-07-17 00:00:00
Tasks: Generative Adversarial Network, Video Summarization, Video Understanding

Video summarization plays an important role in video understanding by
selecting key frames/shots. Traditionally, it aims to find the most
representative and diverse contents in a video as short summaries. Recently, a
more generalized task, query-conditioned video summarization, has been
introduced, which takes user queries into consideration to learn more
user-oriented summaries. In this paper, we propose a query-conditioned
three-player generative adversarial network to tackle this challenge. The
generator learns the joint representation of the user query and the video
content, and the discriminator takes three pairs of query-conditioned summaries
as the input to discriminate the real summary from a generated and a random
one. A three-player loss is introduced for joint training of the generator and
the discriminator, which forces the generator to learn better summary results,
and avoids the generation of random trivial summaries. Experiments on a
recently proposed query-conditioned video summarization benchmark dataset show
the efficiency and efficacy of our proposed method.
Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video
Authors: Samvit Jain, Xin Wang, Joseph Gonzalez
Date: 2018-07-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: Optical Flow Estimation, Segmentation, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

We present Accel, a novel semantic video segmentation system that achieves high accuracy at low inference cost by combining the predictions of two network branches: (1) a reference branch that extracts high-detail features on a reference keyframe, and warps these features forward using frame-to-frame optical flow estimates, and (2) an update branch that computes features of adjustable quality on the current frame, performing a temporal update at each video frame. The modularity of the update branch, where feature subnetworks of varying layer depth can be inserted (e.g. ResNet-18 to ResNet-101), enables operation over a new, state-of-the-art accuracy-throughput trade-off spectrum. Over this curve, Accel models achieve both higher accuracy and faster inference times than the closest comparable single-frame segmentation networks. In general, Accel significantly outperforms previous work on efficient semantic video segmentation, correcting warping-related error that compounds on datasets with complex dynamics. Accel is end-to-end trainable and highly modular: the reference network, the optical flow network, and the update network can each be selected independently, depending on application requirements, and then jointly fine-tuned. The result is a robust, general system for fast, high-accuracy semantic segmentation on video.
Payoff Control in the Iterated Prisoner's Dilemma
Authors: Dong Hao, Kai Li, Tao Zhou
Date: 2018-07-17 00:00:00

Repeated game has long been the touchstone model for agents' long-run
relationships. Previous results suggest that it is particularly difficult for a
repeated game player to exert an autocratic control on the payoffs since they
are jointly determined by all participants. This work discovers that the scale
of a player's capability to unilaterally influence the payoffs may have been
much underestimated. Under the conventional iterated prisoner's dilemma, we
develop a general framework for controlling the feasible region where the
players' payoff pairs lie. A control strategy player is able to confine the
payoff pairs in her objective region, as long as this region has feasible
linear boundaries. With this framework, many well-known existing strategies can
be categorized and various new strategies with nice properties can be further
identified. We show that the control strategies perform well either in a
tournament or against a human-like opponent.
Airline Passenger Name Record Generation using Generative Adversarial Networks
Authors: Alejandro Mottini, Alix Lheritier, Rodrigo Acuna-Agost
Date: 2018-07-17 00:00:00
Tasks: Memorization

Passenger Name Records (PNRs) are at the heart of the travel industry.
Created when an itinerary is booked, they contain travel and passenger
information. It is usual for airlines and other actors in the industry to
inter-exchange and access each other's PNR, creating the challenge of using
them without infringing data ownership laws. To address this difficulty, we
propose a method to generate realistic synthetic PNRs using Generative
Adversarial Networks (GANs). Unlike other GAN applications, PNRs consist of
categorical and numerical features with missing/NaN values, which makes the use
of GANs challenging. We propose a solution based on Cram\'{e}r GANs,
categorical feature embedding and a Cross-Net architecture. The method was
tested on a real PNR dataset, and evaluated in terms of distribution matching,
memorization, and performance of predictive models for two real business
problems: client segmentation and passenger nationality prediction. Results
show that the generated data matches well with the real PNRs without memorizing
them, and that it can be used to train models for real business applications.
Mixed-Stationary Gaussian Process for Flexible Non-Stationary Modeling of Spatial Outcomes
Authors: Leo L. Duan, Xia Wang, Rhonda D. Szczesniak
Date: 2018-07-17 00:00:00
Tasks: Gaussian Processes

Gaussian processes (GPs) are commonplace in spatial statistics. Although many
non-stationary models have been developed, there is arguably a lack of
flexibility compared to equipping each location with its own parameters.
However, the latter suffers from intractable computation and can lead to
overfitting. Taking the instantaneous stationarity idea, we construct a
non-stationary GP with the stationarity parameter individually set at each
location. Then we utilize the non-parametric mixture model to reduce the
effective number of unique parameters. Different from a simple mixture of
independent GPs, the mixture in stationarity allows the components to be
spatial correlated, leading to improved prediction efficiency. Theoretical
properties are examined and a linearly scalable algorithm is provided. The
application is shown through several simulated scenarios as well as the massive
spatiotemporally correlated temperature data.