pwc-archive/papers-with-abstracts huggingface dataset:
Importance mixing: Improving sample reuse in evolutionary policy search methods
Authors: Aloïs Pourchot, Nicolas Perrin, Olivier Sigaud
Date: 2018-08-17 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep neuroevolution, that is evolutionary policy search methods based on deep
neural networks, have recently emerged as a competitor to deep reinforcement
learning algorithms due to their better parallelization capabilities. However,
these methods still suffer from a far worse sample efficiency. In this paper we
investigate whether a mechanism known as "importance mixing" can significantly
improve their sample efficiency. We provide a didactic presentation of
importance mixing and we explain how it can be extended to reuse more samples.
Then, from an empirical comparison based on a simple benchmark, we show that,
though it actually provides better sample efficiency, it is still far from the
sample efficiency of deep reinforcement learning, though it is more stable.
Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving
Authors: Nemanja Djuric, Vladan Radosavljevic, Henggang Cui, Thi Nguyen, Fang-Chieh Chou, Tsung-Han Lin, Nitin Singh, Jeff Schneider
Date: 2018-08-17 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, motion prediction

We address one of the crucial aspects necessary for safe and efficient operations of autonomous vehicles, namely predicting future state of traffic actors in the autonomous vehicle's surroundings. We introduce a deep learning-based approach that takes into account a current world state and produces raster images of each actor's vicinity. The rasters are then used as inputs to deep convolutional models to infer future movement of actors while also accounting for and capturing inherent uncertainty of the prediction task. Extensive experiments on real-world data strongly suggest benefits of the proposed approach. Moreover, following completion of the offline tests the system was successfully tested onboard self-driving vehicles.
Towards Robotic Eye Surgery: Marker-free, Online Hand-eye Calibration using Optical Coherence Tomography Images
Authors: Mingchuan Zhou, Mahdi Hamad, Jakob Weiss, Abouzar Eslami, Kai Huang, Mathias Maier, Chris P. Lohmann, Nassir Navab, Alois Knoll, M. Ali Nasseri
Date: 2018-08-17 00:00:00

Ophthalmic microsurgery is known to be a challenging operation, which
requires very precise and dexterous manipulation. Image guided robot-assisted
surgery (RAS) is a promising solution that brings significant improvements in
outcomes and reduces the physical limitations of human surgeons. However, this
technology must be further developed before it can be routinely used in
clinics. One of the problems is the lack of proper calibration between the
robotic manipulator and appropriate imaging device. In this work, we developed
a flexible framework for hand-eye calibration of an ophthalmic robot with a
microscope-integrated Optical Coherence Tomography (MIOCT) without any markers.
The proposed method consists of three main steps: a) we estimate the OCT
calibration parameters; b) with micro-scale displacements controlled by the
robot, we detect and segment the needle tip in 3D-OCT volume; c) we find the
transformation between the coordinate system of the OCT camera and the
coordinate system of the robot. We verified the capability of our framework in
ex-vivo pig eye experiments and compared the results with a reference method
(marker-based). In all experiments, our method showed a small difference from
the marker based method, with a mean calibration error of 9.2 $\mu$m and 7.0
$\mu$m, respectively. Additionally, the noise test shows the robustness of the
proposed method.
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
Authors: Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini
Date: 2018-08-17 00:00:00
Tasks: Diversity, Document Classification, Multilingual text classification, Multiview Learning, Text Classification

In this paper we propose a boosting based multiview learning algorithm,
referred to as PB-MVBoost, which iteratively learns i) weights over
view-specific voters capturing view-specific information; and ii) weights over
views by optimizing a PAC-Bayes multiview C-Bound that takes into account the
accuracy of view-specific classifiers and the diversity between the views. We
derive a generalization bound for this strategy following the PAC-Bayes theory
which is a suitable tool to deal with models expressed as weighted combination
over a set of voters. Different experiments on three publicly available
datasets show the efficiency of the proposed approach with respect to
state-of-art models.
Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss
Authors: Sangil Jung, Changyong Son, Seohyung Lee, Jinwoo Son, Youngjun Kwak, Jae-Joon Han, Sung Ju Hwang, Changkyu Choi
Date: 2018-08-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: Quantization

Reducing bit-widths of activations and weights of deep networks makes it
efficient to compute and store them in memory, which is crucial in their
deployments to resource-limited devices, such as mobile phones. However,
decreasing bit-widths with quantization generally yields drastically degraded
accuracy. To tackle this problem, we propose to learn to quantize activations
and weights via a trainable quantizer that transforms and discretizes them.
Specifically, we parameterize the quantization intervals and obtain their
optimal values by directly minimizing the task loss of the network. This
quantization-interval-learning (QIL) allows the quantized networks to maintain
the accuracy of the full-precision (32-bit) networks with bit-width as low as
4-bit and minimize the accuracy degeneration with further bit-width reduction
(i.e., 3 and 2-bit). Moreover, our quantizer can be trained on a heterogeneous
dataset, and thus can be used to quantize pretrained networks without access to
their training data. We demonstrate the effectiveness of our trainable
quantizer on ImageNet dataset with various network architectures such as
ResNet-18, -34 and AlexNet, on which it outperforms existing methods to achieve
the state-of-the-art accuracy.
Unsupervised adversarial domain adaptation for acoustic scene classification
Authors: Shayan Gharib, Konstantinos Drossos, Emre Çakır, Dmitriy Serdyuk, Tuomas Virtanen
Date: 2018-08-17 00:00:00
Tasks: Acoustic Scene Classification, Classification, Domain Adaptation, General Classification, Scene Classification

A general problem in acoustic scene classification task is the mismatched
conditions between training and testing data, which significantly reduces the
performance of the developed methods on classification accuracy. As a
countermeasure, we present the first method of unsupervised adversarial domain
adaptation for acoustic scene classification. We employ a model pre-trained on
data from one set of conditions and by using data from other set of conditions,
we adapt the model in order that its output cannot be used for classifying the
set of conditions that input data belong to. We use a freely available dataset
from the DCASE 2018 challenge Task 1, subtask B, that contains data from
mismatched recording devices. We consider the scenario where the annotations
are available for the data recorded from one device, but not for the rest. Our
results show that with our model agnostic method we can achieve $\sim 10\%$
increase at the accuracy on an unseen and unlabeled dataset, while keeping
almost the same performance on the labeled dataset.
Reinforcement Learning for Autonomous Defence in Software-Defined Networking
Authors: Yi Han, Benjamin I. P. Rubinstein, Tamas Abraham, Tansu Alpcan, Olivier De Vel, Sarah Erfani, David Hubczenko, Christopher Leckie, Paul Montague
Date: 2018-08-17 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Despite the successful application of machine learning (ML) in a wide range
of domains, adaptability---the very property that makes machine learning
desirable---can be exploited by adversaries to contaminate training and evade
classification. In this paper, we investigate the feasibility of applying a
specific class of machine learning algorithms, namely, reinforcement learning
(RL) algorithms, for autonomous cyber defence in software-defined networking
(SDN). In particular, we focus on how an RL agent reacts towards different
forms of causative attacks that poison its training process, including
indiscriminate and targeted, white-box and black-box attacks. In addition, we
also study the impact of the attack timing, and explore potential
countermeasures such as adversarial training.
Data Poisoning Attacks in Contextual Bandits
Authors: Yuzhe Ma, Kwang-Sung Jun, Lihong Li, Xiaojin Zhu
Date: 2018-08-17 00:00:00
Tasks: Data Poisoning, Multi-Armed Bandits, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study offline data poisoning attacks in contextual bandits, a class of
reinforcement learning problems with important applications in online
recommendation and adaptive medical treatment, among others. We provide a
general attack framework based on convex optimization and show that by slightly
manipulating rewards in the data, an attacker can force the bandit algorithm to
pull a target arm for a target contextual vector. The target arm and target
contextual vector are both chosen by the attacker. That is, the attacker can
hijack the behavior of a contextual bandit. We also investigate the feasibility
and the side effects of such attacks, and identify future directions for
defense. Experiments on both synthetic and real-world data demonstrate the
efficiency of the attack algorithm.
Read + Verify: Machine Reading Comprehension with Unanswerable Questions
Authors: Minghao Hu, Furu Wei, Yuxing Peng, Zhen Huang, Nan Yang, Dongsheng Li
Date: 2018-08-17 00:00:00
Tasks: Machine Reading Comprehension, Question Answering, Reading Comprehension

Machine reading comprehension with unanswerable questions aims to abstain
from answering when no answer can be inferred. In addition to extract answers,
previous works usually predict an additional "no-answer" probability to detect
unanswerable cases. However, they fail to validate the answerability of the
question by verifying the legitimacy of the predicted answer. To address this
problem, we propose a novel read-then-verify system, which not only utilizes a
neural reader to extract candidate answers and produce no-answer probabilities,
but also leverages an answer verifier to decide whether the predicted answer is
entailed by the input snippets. Moreover, we introduce two auxiliary losses to
help the reader better handle answer extraction as well as no-answer detection,
and investigate three different architectures for the answer verifier. Our
experiments on the SQuAD 2.0 dataset show that our system achieves a score of
74.2 F1 on the test set, achieving state-of-the-art results at the time of
submission (Aug. 28th, 2018).
Dynamic Routing on Deep Neural Network for Thoracic Disease Classification and Sensitive Area Localization
Authors: Yan Shen, Mingchen Gao
Date: 2018-08-17 00:00:00
Tasks: General Classification, image-classification, Image Classification, object-detection, Object Detection, Thoracic Disease Classification

We present and evaluate a new deep neural network architecture for automatic
thoracic disease detection on chest X-rays. Deep neural networks have shown
great success in a plethora of visual recognition tasks such as image
classification and object detection by stacking multiple layers of
convolutional neural networks (CNN) in a feed-forward manner. However, the
performance gain by going deeper has reached bottlenecks as a result of the
trade-off between model complexity and discrimination power. We address this
problem by utilizing the recently developed routing-by agreement mechanism in
our architecture. A novel characteristic of our network structure is that it
extends routing to two types of layer connections (1) connection between
feature maps in dense layers, (2) connection between primary capsules and
prediction capsules in final classification layer. We show that our networks
achieve comparable results with much fewer layers in the measurement of AUC
score. We further show the combined benefits of model interpretability by
generating Gradient-weighted Class Activation Mapping (Grad-CAM) for
localization. We demonstrate our results on the NIH chestX-ray14 dataset that
consists of 112,120 images on 30,805 unique patients including 14 kinds of lung
diseases.
Convolutional Neural Networks based Intra Prediction for HEVC
Authors: Wenxue Cui, Tao Zhang, Shengping Zhang, Feng Jiang, WangMeng Zuo, Debin Zhao
Date: 2018-08-17 00:00:00
Tasks: Prediction

Traditional intra prediction methods for HEVC rely on using the nearest
reference lines for predicting a block, which ignore much richer context
between the current block and its neighboring blocks and therefore cause
inaccurate prediction especially when weak spatial correlation exists between
the current block and the reference lines. To overcome this problem, in this
paper, an intra prediction convolutional neural network (IPCNN) is proposed for
intra prediction, which exploits the rich context of the current block and
therefore is capable of improving the accuracy of predicting the current block.
Meanwhile, the predictions of the three nearest blocks can also be refined. To
the best of our knowledge, this is the first paper that directly applies CNNs
to intra prediction for HEVC. Experimental results validate the effectiveness
of applying CNNs to intra prediction and achieved significant performance
improvement compared to traditional intra prediction methods.
Medical Image Imputation from Image Collections
Authors: Adrian V. Dalca, Katherine L. Bouman, William T. Freeman, Natalia S. Rost, Mert R. Sabuncu, Polina Golland
Date: 2018-08-17 00:00:00
Tasks: Anatomy, Image Imputation, Imputation, Super-Resolution

We present an algorithm for creating high resolution anatomically plausible
images consistent with acquired clinical brain MRI scans with large inter-slice
spacing. Although large data sets of clinical images contain a wealth of
information, time constraints during acquisition result in sparse scans that
fail to capture much of the anatomy. These characteristics often render
computational analysis impractical as many image analysis algorithms tend to
fail when applied to such images. Highly specialized algorithms that explicitly
handle sparse slice spacing do not generalize well across problem domains. In
contrast, we aim to enable application of existing algorithms that were
originally developed for high resolution research scans to significantly
undersampled scans. We introduce a generative model that captures fine-scale
anatomical structure across subjects in clinical image collections and derive
an algorithm for filling in the missing data in scans with large inter-slice
spacing. Our experimental results demonstrate that the resulting method
outperforms state-of-the-art upsampling super-resolution techniques, and
promises to facilitate subsequent analysis not previously possible with scans
of this quality. Our implementation is freely available at
https://github.com/adalca/papago .
Efficiently Learning Mixtures of Mallows Models
Authors: Allen Liu, Ankur Moitra
Date: 2018-08-17 00:00:00
Tasks: Recommendation Systems

Mixtures of Mallows models are a popular generative model for ranking data
coming from a heterogeneous population. They have a variety of applications
including social choice, recommendation systems and natural language
processing. Here we give the first polynomial time algorithm for provably
learning the parameters of a mixture of Mallows models with any constant number
of components. Prior to our work, only the two component case had been settled.
Our analysis revolves around a determinantal identity of Zagier which was
proven in the context of mathematical physics, which we use to show polynomial
identifiability and ultimately to construct test functions to peel off one
component at a time.
  To complement our upper bounds, we show information-theoretic lower bounds on
the sample complexity as well as lower bounds against restricted families of
algorithms that make only local queries. Together, these results demonstrate
various impediments to improving the dependence on the number of components.
They also motivate the study of learning mixtures of Mallows models from the
perspective of beyond worst-case analysis. In this direction, we show that when
the scaling parameters of the Mallows models have separation, there are much
faster learning algorithms.
Efficient Single-Shot Multibox Detector for Construction Site Monitoring
Authors: Viral Thakar, Himani Saini, Walid Ahmed, Mohammad M Soltani, Ahmed Aly, Jia Yuan Yu
Date: 2018-08-17 00:00:00
Tasks: Clustering

Asset monitoring in construction sites is an intricate, manually intensive
task, that can highly benefit from automated solutions engineered using deep
neural networks. We use Single-Shot Multibox Detector --- SSD, for its fine
balance between speed and accuracy, to leverage ubiquitously available images
and videos from the surveillance cameras on the construction sites and automate
the monitoring tasks, hence enabling project managers to better track the
performance and optimize the utilization of each resource. We propose to
improve the performance of SSD by clustering the predicted boxes instead of a
greedy approach like non-maximum suppression. We do so using Affinity
Propagation Clustering --- APC to cluster the predicted boxes based on the
similarity index computed using the spatial features as well as location of
predicted boxes. In our attempts, we have been able to improve the mean average
precision of SSD by 3.77% on custom dataset consist of images from construction
sites and by 1.67% on PASCAL VOC Challenge.
Ensemble-based Adaptive Single-shot Multi-box Detector
Authors: Viral Thakar, Walid Ahmed, Mohammad M Soltani, Jia Yuan Yu
Date: 2018-08-17 00:00:00

We propose two improvements to the SSD---single shot multibox detector.
First, we propose an adaptive approach for default box selection in SSD. This
uses data to reduce the uncertainty in the selection of best aspect ratios for
the default boxes and improves performance of SSD for datasets containing small
and complex objects (e.g., equipments at construction sites). We do so by
finding the distribution of aspect ratios of the given training dataset, and
then choosing representative values. Secondly, we propose an ensemble
algorithm, using SSD as components, which improves the performance of SSD,
especially for small amount of training datasets. Compared to the conventional
SSD algorithm, adaptive box selection improves mean average precision by 3%,
while ensemble-based SSD improves it by 8%.
An N Time-Slice Dynamic Chain Event Graph
Authors: Rodrigo A. Collazo, Jim Q. Smith
Date: 2018-08-17 00:00:00

The Dynamic Chain Event Graph (DCEG) is able to depict many classes of
discrete random processes exhibiting asymmetries in their developments and
context-specific conditional probabilities structures. However, paradoxically,
this very generality has so far frustrated its wide application. So in this
paper we develop an object-oriented method to fully analyse a particularly
useful and feasibly implementable new subclass of these graphical models called
the N Time-Slice DCEG (NT-DCEG). After demonstrating a close relationship
between an NT-DCEG and a specific class of Markov processes, we discuss how
graphical modellers can exploit this connection to gain a deep understanding of
their processes. We also show how to read from the topology of this graph
context-specific independence statements that can then be checked by domain
experts. Our methods are illustrated throughout using examples of dynamic
multivariate processes describing inmate radicalisation in a prison.
Mitigation of Adversarial Attacks through Embedded Feature Selection
Authors: Ziyi Bao, Luis Muñoz-González, Emil C. Lupu
Date: 2018-08-16 00:00:00
Tasks: BIG-bench Machine Learning, feature selection

Machine learning has become one of the main components for task automation in
many application domains. Despite the advancements and impressive achievements
of machine learning, it has been shown that learning algorithms can be
compromised by attackers both at training and test time. Machine learning
systems are especially vulnerable to adversarial examples where small
perturbations added to the original data points can produce incorrect or
unexpected outputs in the learning algorithms at test time. Mitigation of these
attacks is hard as adversarial examples are difficult to detect. Existing
related work states that the security of machine learning systems against
adversarial examples can be weakened when feature selection is applied to
reduce the systems' complexity. In this paper, we empirically disprove this
idea, showing that the relative distortion that the attacker has to introduce
to succeed in the attack is greater when the target is using a reduced set of
features. We also show that the minimal adversarial examples differ
statistically more strongly from genuine examples with a lower number of
features. However, reducing the feature count can negatively impact the
system's performance. We illustrate the trade-off between security and accuracy
with specific examples. We propose a design methodology to evaluate the
security of machine learning classifiers with embedded feature selection
against adversarial examples crafted using different attack strategies.
Augmenting Statistical Machine Translation with Subword Translation of Out-of-Vocabulary Words
Authors: Nelson F. Liu, Jonathan May, Michael Pust, Kevin Knight
Date: 2018-08-16 00:00:00
Tasks: Machine Translation, Translation

Most statistical machine translation systems cannot translate words that are
unseen in the training data. However, humans can translate many classes of
out-of-vocabulary (OOV) words (e.g., novel morphological variants,
misspellings, and compounds) without context by using orthographic clues.
Following this observation, we describe and evaluate several general methods
for OOV translation that use only subword information. We pose the OOV
translation problem as a standalone task and intrinsically evaluate our
approaches on fourteen typologically diverse languages across varying resource
levels. Adding OOV translators to a statistical machine translation system
yields consistent BLEU gains (0.5 points on average, and up to 2.0) for all
fourteen languages, especially in low-resource scenarios.
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study
Authors: Aditya Siddhant, Zachary C. Lipton
Date: 2018-08-16 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Active Learning, Open-Ended Question Answering

Several recent papers investigate Active Learning (AL) for mitigating the
data dependence of deep learning for natural language processing. However, the
applicability of AL to real-world problems remains an open question. While in
supervised learning, practitioners can try many different methods, evaluating
each against a validation set before selecting a model, AL affords no such
luxury. Over the course of one AL run, an agent annotates its dataset
exhausting its labeling budget. Thus, given a new task, an active learner has
no opportunity to compare models and acquisition functions. This paper provides
a large scale empirical study of deep active learning, addressing multiple
tasks and, for each, multiple datasets, multiple models, and a full suite of
acquisition functions. We find that across all settings, Bayesian active
learning by disagreement, using uncertainty estimates provided either by
Dropout or Bayes-by Backprop significantly improves over i.i.d. baselines and
usually outperforms classic uncertainty sampling.