pwc-archive/papers-with-abstracts huggingface dataset:
End-to-End Single Image Fog Removal using Enhanced Cycle Consistent Adversarial Networks
Authors: Wei Liu, Xianxu Hou, Jiang Duan, Guoping Qiu
Date: 2019-02-04 00:00:00

Single image defogging is a classical and challenging problem in computer
vision. Existing methods towards this problem mainly include handcrafted priors
based methods that rely on the use of the atmospheric degradation model and
learning based approaches that require paired fog-fogfree training example
images. In practice, however, prior-based methods are prone to failure due to
their own limitations and paired training data are extremely difficult to
acquire. Inspired by the principle of CycleGAN network, we have developed an
end-to-end learning system that uses unpaired fog and fogfree training images,
adversarial discriminators and cycle consistency losses to automatically
construct a fog removal system. Similar to CycleGAN, our system has two
transformation paths; one maps fog images to a fogfree image domain and the
other maps fogfree images to a fog image domain. Instead of one stage mapping,
our system uses a two stage mapping strategy in each transformation path to
enhance the effectiveness of fog removal. Furthermore, we make explicit use of
prior knowledge in the networks by embedding the atmospheric degradation
principle and a sky prior for mapping fogfree images to the fog images domain.
In addition, we also contribute the first real world nature fog-fogfree image
dataset for defogging research. Our multiple real fog images dataset (MRFID)
contains images of 200 natural outdoor scenes. For each scene, there are one
clear image and corresponding four foggy images of different fog densities
manually selected from a sequence of images taken by a fixed camera over the
course of one year. Qualitative and quantitative comparison against several
state-of-the-art methods on both synthetic and real world images demonstrate
that our approach is effective and performs favorably for recovering a clear
image from a foggy image.
Stochastic Zeroth-order Discretizations of Langevin Diffusions for Bayesian Inference
Authors: Abhishek Roy, Lingqing Shen, Krishnakumar Balasubramanian, Saeed Ghadimi
Date: 2019-02-04 00:00:00
Tasks: Bayesian Inference, Stochastic Optimization, Variable Selection

Discretizations of Langevin diffusions provide a powerful method for sampling and Bayesian inference. However, such discretizations require evaluation of the gradient of the potential function. In several real-world scenarios, obtaining gradient evaluations might either be computationally expensive, or simply impossible. In this work, we propose and analyze stochastic zeroth-order sampling algorithms for discretizing overdamped and underdamped Langevin diffusions. Our approach is based on estimating the gradients, based on Gaussian Stein's identities, widely used in the stochastic optimization literature. We provide a comprehensive sample complexity analysis -- number noisy function evaluations to be made to obtain an $\epsilon$-approximate sample in Wasserstein distance -- of stochastic zeroth-order discretizations of both overdamped and underdamped Langevin diffusions, under various noise models. We also propose a variable selection technique based on zeroth-order gradient estimates and establish its theoretical guarantees. Our theoretical contributions extend the practical applicability of sampling algorithms to the noisy black-box and high-dimensional settings.
Insertion-based Decoding with automatically Inferred Generation Order
Authors: Jiatao Gu, Qi Liu, Kyunghyun Cho
Date: 2019-02-04 00:00:00
Proceeding: TACL 2019 3
Tasks: Code Generation, Machine Translation, Translation

Conventional neural autoregressive decoding commonly assumes a fixed left-to-right generation order, which may be sub-optimal. In this work, we propose a novel decoding algorithm -- InDIGO -- which supports flexible sequence generation in arbitrary orders through insertion operations. We extend Transformer, a state-of-the-art sequence generation model, to efficiently implement the proposed approach, enabling it to be trained with either a pre-defined generation order or adaptive orders obtained from beam-search. Experiments on four real-world tasks, including word order recovery, machine translation, image caption and code generation, demonstrate that our algorithm can generate sequences following arbitrary orders, while achieving competitive or even better performance compared to the conventional left-to-right generation. The generated sequences show that InDIGO adopts adaptive generation orders based on input information.
Evaluation of Multidisciplinary Effects of Artificial Intelligence with Optimization Perspective
Authors: M. H. Calp
Date: 2019-02-04 00:00:00

Artificial Intelligence has an important place in the scientific community as
a result of its successful outputs in terms of different fields. In time, the
field of Artificial Intelligence has been divided into many sub-fields because
of increasing number of different solution approaches, methods, and techniques.
Machine Learning has the most remarkable role with its functions to learn from
samples from the environment. On the other hand, intelligent optimization done
by inspiring from nature and swarms had its own unique scientific literature,
with effective solutions provided for optimization problems from different
fields. Because intelligent optimization can be applied in different fields
effectively, this study aims to provide a general discussion on
multidisciplinary effects of Artificial Intelligence by considering its
optimization oriented solutions. The study briefly focuses on background of the
intelligent optimization briefly and then gives application examples of
intelligent optimization from a multidisciplinary perspective.
Solving The Exam Scheduling Problems in Central Exams With Genetic Algorithms
Authors: Murat Dener, M. Hanefi Calp
Date: 2019-02-04 00:00:00
Tasks: Scheduling

It is the efficient use of resources expected from an exam scheduling
application. There are various criteria for efficient use of resources and for
all tests to be carried out at minimum cost in the shortest possible time. It
is aimed that educational institutions with such criteria successfully carry
out central examination organizations. In the study, a two-stage genetic
algorithm was developed. In the first stage, the assignment of courses to
sessions was carried out. In the second stage, the students who participated in
the test session were assigned to examination rooms. Purposes of the study are
increasing the number of joint students participating in sessions, using the
minimum number of buildings in the same session, and reducing the number of
supervisors using the minimum number of classrooms possible. In this study, a
general purpose exam scheduling solution for educational institutions was
presented. The developed system can be used in different central examinations
to create originality. Given the results of the sample application, it is seen
that the proposed genetic algorithm gives successful results.1
An Argument-Marker Model for Syntax-Agnostic Proto-Role Labeling
Authors: Juri Opitz, Anette Frank
Date: 2019-02-04 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Semantic Role Labeling

Semantic proto-role labeling (SPRL) is an alternative to semantic role
labeling (SRL) that moves beyond a categorical definition of roles, following
Dowty's feature-based view of proto-roles. This theory determines agenthood vs.
patienthood based on a participant's instantiation of more or less typical
agent vs. patient properties, such as, for example, volition in an event. To
perform SPRL, we develop an ensemble of hierarchical models with self-attention
and concurrently learned predicate-argument-markers. Our method is competitive
with the state-of-the art, overall outperforming previous work in two
formulations of the task (multi-label and multi-variate Likert scale
prediction). In contrast to previous work, our results do not depend on gold
argument heads derived from supplementary gold tree banks.
Recommender Systems Notation: Proposed Common Notation for Teaching and Research
Authors: Michael D. Ekstrand, Joseph A. Konstan
Date: 2019-02-04 00:00:00
Tasks: Information Retrieval, Recommendation Systems, Retrieval

As the field of recommender systems has developed, authors have used a myriad
of notations for describing the mathematical workings of recommendation
algorithms. These notations ap-pear in research papers, books, lecture notes,
blog posts, and software documentation. The dis-ciplinary diversity of the
field has not contributed to consistency in notation; scholars whose home base
is in information retrieval have different habits and expectations than those
in ma-chine learning or human-computer interaction.
  In the course of years of teaching and research on recommender systems, we
have seen the val-ue in adopting a consistent notation across our work. This
has been particularly highlighted in our development of the Recommender Systems
MOOC on Coursera (Konstan et al. 2015), as we need to explain a wide variety of
algorithms and our learners are not well-served by changing notation between
algorithms.
  In this paper, we describe the notation we have adopted in our work, along
with its justification and some discussion of considered alternatives. We
present this in hope that it will be useful to others writing and teaching
about recommender systems. This notation has served us well for some time now,
in research, online education, and traditional classroom instruction. We feel
it is ready for broad use.
Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning
Authors: Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni, Chlodwig Kirchhoff, Peter Biberthaler, Nassir Navab, Sonja Kirchhoff, Diana Mateus
Date: 2019-02-04 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Image Retrieval, Retrieval

We demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray images according to the AO classification. The proposed framework aims to improve patient treatment planning and provide support for the training of trauma surgeon residents. A database of 1347 clinical radiographic studies was collected. Radiologists and trauma surgeons annotated all fractures with bounding boxes, and provided a classification according to the AO standard. The proposed CAD tool for the classification of radiographs into types "A", "B" and "not-fractured", reaches a F1-score of 87% and AUC of 0.95, when classifying fractures versus not-fractured cases it improves up to 94% and 0.98. Prior localization of the fracture results in an improvement with respect to full image classification. 100% of the predicted centers of the region of interest are contained in the manually provided bounding boxes. The system retrieves on average 9 relevant images (from the same class) out of 10 cases. Our CAD scheme localizes, detects and further classifies proximal femur fractures achieving results comparable to expert-level and state-of-the-art performance. Our auxiliary localization model was highly accurate predicting the region of interest in the radiograph. We further investigated several strategies of verification for its adoption into the daily clinical routine. A sensitivity analysis of the size of the ROI and image retrieval as a clinical use case were presented.
'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images
Authors: Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl, Nassir Navab, Christian Wachinger
Date: 2019-02-04 00:00:00
Tasks: Few-Shot Learning, Image Segmentation, Organ Segmentation, Segmentation, Semantic Segmentation

Deep neural networks enable highly accurate image segmentation, but require large amounts of manually annotated data for supervised training. Few-shot learning aims to address this shortcoming by learning a new class from a few annotated support examples. We introduce, a novel few-shot framework, for the segmentation of volumetric medical images with only a few annotated slices. Compared to other related works in computer vision, the major challenges are the absence of pre-trained networks and the volumetric nature of medical scans. We address these challenges by proposing a new architecture for few-shot segmentation that incorporates 'squeeze & excite' blocks. Our two-armed architecture consists of a conditioner arm, which processes the annotated support input and generates a task-specific representation. This representation is passed on to the segmenter arm that uses this information to segment the new query image. To facilitate efficient interaction between the conditioner and the segmenter arm, we propose to use 'channel squeeze & spatial excitation' blocks - a light-weight computational module - that enables heavy interaction between both the arms with negligible increase in model complexity. This contribution allows us to perform image segmentation without relying on a pre-trained model, which generally is unavailable for medical scans. Furthermore, we propose an efficient strategy for volumetric segmentation by optimally pairing a few slices of the support volume to all the slices of the query volume. We perform experiments for organ segmentation on whole-body contrast-enhanced CT scans from the Visceral Dataset. Our proposed model outperforms multiple baselines and existing approaches with respect to the segmentation accuracy by a significant margin. The source code is available at https://github.com/abhi4ssj/few-shot-segmentation.
An Effective Approach to Unsupervised Machine Translation
Authors: Mikel Artetxe, Gorka Labaka, Eneko Agirre
Date: 2019-02-04 00:00:00
Proceeding: ACL 2019 7
Tasks: Machine Translation, NMT, Translation, Unsupervised Machine Translation

While machine translation has traditionally relied on large amounts of parallel corpora, a recent research line has managed to train both Neural Machine Translation (NMT) and Statistical Machine Translation (SMT) systems using monolingual corpora only. In this paper, we identify and address several deficiencies of existing unsupervised SMT approaches by exploiting subword information, developing a theoretically well founded unsupervised tuning method, and incorporating a joint refinement procedure. Moreover, we use our improved SMT system to initialize a dual NMT model, which is further fine-tuned through on-the-fly back-translation. Together, we obtain large improvements over the previous state-of-the-art in unsupervised machine translation. For instance, we get 22.5 BLEU points in English-to-German WMT 2014, 5.5 points more than the previous best unsupervised system, and 0.5 points more than the (supervised) shared task winner back in 2014.
Declarative Data Analytics: a Survey
Authors: Nantia Makrynioti, Vasilis Vassalos
Date: 2019-02-04 00:00:00
Tasks: BIG-bench Machine Learning, Survey

The area of declarative data analytics explores the application of the
declarative paradigm on data science and machine learning. It proposes
declarative languages for expressing data analysis tasks and develops systems
which optimize programs written in those languages. The execution engine can be
either centralized or distributed, as the declarative paradigm advocates
independence from particular physical implementations. The survey explores a
wide range of declarative data analysis frameworks by examining both the
programming model and the optimization techniques used, in order to provide
conclusions on the current state of the art in the area and identify open
challenges.
Real-time Prediction of Automotive Collision Risk from Monocular Video
Authors: Derek J. Phillips, Juan Carlos Aragon, Anjali Roychowdhury, Regina Madigan, Sunil Chintakindi, Mykel J. Kochenderfer
Date: 2019-02-04 00:00:00
Tasks: Collision Avoidance, Object, object-detection, Object Detection, Object Tracking, State Estimation

Many automotive applications, such as Advanced Driver Assistance Systems
(ADAS) for collision avoidance and warnings, require estimating the future
automotive risk of a driving scene. We present a low-cost system that predicts
the collision risk over an intermediate time horizon from a monocular video
source, such as a dashboard-mounted camera. The modular system includes
components for object detection, object tracking, and state estimation. We
introduce solutions to the object tracking and distance estimation problems.
Advanced approaches to the other tasks are used to produce real-time
predictions of the automotive risk for the next 10 s at over 5 Hz. The system
is designed such that alternative components can be substituted with minimal
effort. It is demonstrated on common physical hardware, specifically an
off-the-shelf gaming laptop and a webcam. We extend the framework to support
absolute speed estimation and more advanced risk estimation techniques.
Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows
Authors: Zhongliang Yang, Hao Yang, Yuting Hu, Yongfeng Huang, Yu-Jin Zhang
Date: 2019-02-04 00:00:00
Tasks: Steganalysis

Previous VoIP steganalysis methods face great challenges in detecting speech
signals at low embedding rates, and they are also generally difficult to
perform real-time detection, making them hard to truly maintain cyberspace
security. To solve these two challenges, in this paper, combined with the
sliding window detection algorithm and Convolution Neural Network we propose a
real-time VoIP steganalysis method which based on multi-channel convolution
sliding windows. In order to analyze the correlations between frames and
different neighborhood frames in a VoIP signal, we define multi channel sliding
detection windows. Within each sliding window, we design two feature extraction
channels which contain multiple convolution layers with multiple convolution
kernels each layer to extract correlation features of the input signal. Then
based on these extracted features, we use a forward fully connected network for
feature fusion. Finally, by analyzing the statistical distribution of these
features, the discriminator will determine whether the input speech signal
contains covert information or not.We designed several experiments to test the
proposed model's detection ability under various conditions, including
different embedding rates, different speech length, etc. Experimental results
showed that the proposed model outperforms all the previous methods, especially
in the case of low embedding rate, which showed state-of-the-art performance.
In addition, we also tested the detection efficiency of the proposed model, and
the results showed that it can achieve almost real-time detection of VoIP
speech signals.
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
Authors: Martin Sundermeyer, Zoltan-Csaba Marton, Maximilian Durner, Manuel Brucker, Rudolph Triebel
Date: 2019-02-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, Denoising, Object, object-detection, Object Detection, Pose Estimation

We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization. This so-called Augmented Autoencoder has several advantages over existing methods: It does not require real, pose-annotated training data, generalizes to various test sensors and inherently handles object and view symmetries. Instead of learning an explicit mapping from input images to object poses, it provides an implicit representation of object orientations defined by samples in a latent space. Our pipeline achieves state-of-the-art performance on the T-LESS dataset both in the RGB and RGB-D domain. We also evaluate on the LineMOD dataset where we can compete with other synthetically trained approaches. We further increase performance by correcting 3D orientation estimates to account for perspective errors when the object deviates from the image center and show extended results.
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
Authors: Paavo Parmas, Carl Edward Rasmussen, Jan Peters, Kenji Doya
Date: 2019-02-04 00:00:00
Proceeding: ICML 2018 7
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Previously, the exploding gradient problem has been explained to be central
in deep learning and model-based reinforcement learning, because it causes
numerical issues and instability in optimization. Our experiments in
model-based reinforcement learning imply that the problem is not just a
numerical issue, but it may be caused by a fundamental chaos-like nature of
long chains of nonlinear computations. Not only do the magnitudes of the
gradients become large, the direction of the gradients becomes essentially
random. We show that reparameterization gradients suffer from the problem,
while likelihood ratio gradients are robust. Using our insights, we develop a
model-based policy search framework, Probabilistic Inference for Particle-Based
Policy Search (PIPPS), which is easily extensible, and allows for almost
arbitrary models and policies, while simultaneously matching the performance of
previous data-efficient learning algorithms. Finally, we invent the total
propagation algorithm, which efficiently computes a union over all pathwise
derivative depths during a single backwards pass, automatically giving greater
weight to estimators with lower variance, sometimes improving over
reparameterization gradients by $10^6$ times.
A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players
Authors: Etienne Boursier, Emilie Kaufmann, Abbas Mehrabian, Vianney Perchet
Date: 2019-02-04 00:00:00
Tasks: Open-Ended Question Answering

We study a multiplayer stochastic multi-armed bandit problem in which players cannot communicate, and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward. We consider the challenging heterogeneous setting, in which different arms may have different means for different players, and propose a new and efficient algorithm that combines the idea of leveraging forced collisions for implicit communication and that of performing matching eliminations. We present a finite-time analysis of our algorithm, giving the first sublinear minimax regret bound for this problem, and prove that if the optimal assignment of players to arms is unique, our algorithm attains the optimal $O(\ln(T))$ regret, solving an open question raised at NeurIPS 2018.
New Risk Bounds for 2D Total Variation Denoising
Authors: Sabyasachi Chatterjee, Subhajit Goswami
Date: 2019-02-04 00:00:00
Tasks: Denoising, Image Denoising

2D Total Variation Denoising (TVD) is a widely used technique for image denoising. It is also an important nonparametric regression method for estimating functions with heterogenous smoothness. Recent results have shown the TVD estimator to be nearly minimax rate optimal for the class of functions with bounded variation. In this paper, we complement these worst case guarantees by investigating the adaptivity of the TVD estimator to functions which are piecewise constant on axis aligned rectangles. We rigorously show that, when the truth is piecewise constant, the ideally tuned TVD estimator performs better than in the worst case. We also study the issue of choosing the tuning parameter. In particular, we propose a fully data driven version of the TVD estimator which enjoys similar worst case risk guarantees as the ideally tuned TVD estimator.