pwc-archive/papers-with-abstracts huggingface dataset:
The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint
Authors: Stephen Tu, Benjamin Recht
Date: 2018-12-09 00:00:00
Tasks: continuous-control, Continuous Control, model, Reinforcement Learning, Reinforcement Learning (RL)

The effectiveness of model-based versus model-free methods is a long-standing
question in reinforcement learning (RL). Motivated by recent empirical success
of RL on continuous control tasks, we study the sample complexity of popular
model-based and model-free algorithms on the Linear Quadratic Regulator (LQR).
We show that for policy evaluation, a simple model-based plugin method requires
asymptotically less samples than the classical least-squares temporal
difference (LSTD) estimator to reach the same quality of solution; the sample
complexity gap between the two methods can be at least a factor of state
dimension. For policy evaluation, we study a simple family of problem instances
and show that nominal (certainty equivalence principle) control also requires
several factors of state and input dimension fewer samples than the policy
gradient method to reach the same level of control performance on these
instances. Furthermore, the gap persists even when employing commonly used
baselines. To the best of our knowledge, this is the first theoretical result
which demonstrates a separation in the sample complexity between model-based
and model-free methods on a continuous control task.
Deep Spectral Reflectance and Illuminant Estimation from Self-Interreflections
Authors: Rada Deeb, Joost Van de Weijer, Damien Muselet, Mathieu Hebert, Alain Tremeau
Date: 2018-12-09 00:00:00

In this work, we propose a CNN-based approach to estimate the spectral
reflectance of a surface and the spectral power distribution of the light from
a single RGB image of a V-shaped surface. Interreflections happening in a
concave surface lead to gradients of RGB values over its area. These gradients
carry a lot of information concerning the physical properties of the surface
and the illuminant. Our network is trained with only simulated data constructed
using a physics-based interreflection model. Coupling interreflection effects
with deep learning helps to retrieve the spectral reflectance under an unknown
light and to estimate the spectral power distribution of this light as well. In
addition, it is more robust to the presence of image noise than the classical
approaches. Our results show that the proposed approach outperforms the state
of the art learning-based approaches on simulated data. In addition, it gives
better results on real data compared to other interreflection-based approaches.
A Structured Model For Action Detection
Authors: Yubo Zhang, Pavel Tokmakov, Martial Hebert, Cordelia Schmid
Date: 2018-12-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Detection, model, Video Understanding

A dominant paradigm for learning-based approaches in computer vision is training generic models, such as ResNet for image recognition, or I3D for video understanding, on large datasets and allowing them to discover the optimal representation for the problem at hand. While this is an obviously attractive approach, it is not applicable in all scenarios. We claim that action detection is one such challenging problem - the models that need to be trained are large, and labeled data is expensive to obtain. To address this limitation, we propose to incorporate domain knowledge into the structure of the model, simplifying optimization. In particular, we augment a standard I3D network with a tracking module to aggregate long term motion patterns, and use a graph convolutional network to reason about interactions between actors and objects. Evaluated on the challenging AVA dataset, the proposed approach improves over the I3D baseline by 5.5% mAP and over the state-of-the-art by 4.8% mAP.
On balanced clustering with tree-like structures over clusters
Authors: Mark Sh. Levin
Date: 2018-12-09 00:00:00
Tasks: Clustering, Management

The article addresses balanced clustering problems with an additional
requirement as a tree-like structure over the obtained balanced clusters. This
kind of clustering problems can be useful in some applications (e.g., network
design, management and routing). Various types of the initial elements are
considered. Four basic greedy-like solving strategies (design framework) are
considered: balancing-spanning strategy, spanning-balancing strategy, direct
strategy, and design of layered structures with balancing. An extended
description of the spanning-balancing strategy is presented including four
solving schemes and an illustrative numerical example.
A Deep Multi-task Learning Approach to Skin Lesion Classification
Authors: Haofu Liao, Jiebo Luo
Date: 2018-12-09 00:00:00
Tasks: Classification, General Classification, Inductive Bias, Lesion Classification, Multi-Task Learning, Skin Lesion Classification, Skin Lesion Identification

Skin lesion identification is a key step toward dermatological diagnosis. When describing a skin lesion, it is very important to note its body site distribution as many skin diseases commonly affect particular parts of the body. To exploit the correlation between skin lesions and their body site distributions, in this study, we investigate the possibility of improving skin lesion classification using the additional context information provided by body location. Specifically, we build a deep multi-task learning (MTL) framework to jointly optimize skin lesion classification and body location classification (the latter is used as an inductive bias). Our MTL framework uses the state-of-the-art ImageNet pretrained model with specialized loss functions for the two related tasks. Our experiments show that the proposed MTL based method performs more robustly than its standalone (single-task) counterpart.
Skin Disease Classification versus Skin Lesion Characterization: Achieving Robust Diagnosis using Multi-label Deep Neural Networks
Authors: Haofu Liao, Yuncheng Li, Jiebo Luo
Date: 2018-12-09 00:00:00
Tasks: General Classification

In this study, we investigate what a practically useful approach is in order to achieve robust skin disease diagnosis. A direct approach is to target the ground truth diagnosis labels, while an alternative approach instead focuses on determining skin lesion characteristics that are more visually consistent and discernible. We argue that, for computer-aided skin disease diagnosis, it is both more realistic and more useful that lesion type tags should be considered as the target of an automated diagnosis system such that the system can first achieve a high accuracy in describing skin lesions, and in turn facilitate disease diagnosis using lesion characteristics in conjunction with other evidence. To further meet such an objective, we employ convolutional neural networks (CNNs) for both the disease-targeted and lesion-targeted classifications. We have collected a large-scale and diverse dataset of 75,665 skin disease images from six publicly available dermatology atlantes. Then we train and compare both disease-targeted and lesion-targeted classifiers, respectively. For disease-targeted classification, only 27.6% top-1 accuracy and 57.9% top-5 accuracy are achieved with a mean average precision (mAP) of 0.42. In contrast, for lesion-targeted classification, we can achieve a much higher mAP of 0.70.
Deep-Net: Deep Neural Network for Cyber Security Use Cases
Authors: Vinayakumar R, Barathi Ganesh HB, Prabaharan Poornachandran, Anand Kumar M, Soman Kp
Date: 2018-12-09 00:00:00
Tasks: Fraud Detection, General Classification, Malware Classification

Deep neural networks (DNNs) have witnessed as a powerful approach in this
year by solving long-standing Artificial intelligence (AI) supervised and
unsupervised tasks exists in natural language processing, speech processing,
computer vision and others. In this paper, we attempt to apply DNNs on three
different cyber security use cases: Android malware classification, incident
detection and fraud detection. The data set of each use case contains real
known benign and malicious activities samples. The efficient network
architecture for DNN is chosen by conducting various trails of experiments for
network parameters and network structures. The experiments of such chosen
efficient configurations of DNNs are run up to 1000 epochs with learning rate
set in the range [0.01-0.5]. Experiments of DNN performed well in comparison to
the classical machine learning algorithms in all cases of experiments of cyber
security use cases. This is due to the fact that DNNs implicitly extract and
build better features, identifies the characteristics of the data that lead to
better accuracy. The best accuracy obtained by DNN and XGBoost on Android
malware classification 0.940 and 0.741, incident detection 1.00 and 0.997 fraud
detection 0.972 and 0.916 respectively.
Working Principles of Binary Differential Evolution
Authors: Benjamin Doerr, Weijie Zheng
Date: 2018-12-09 00:00:00
Tasks: Evolutionary Algorithms

We conduct a first fundamental analysis of the working principles of binary
differential evolution (BDE), an optimization heuristic for binary decision
variables that was derived by Gong and Tuson (2007) from the very successful
classic differential evolution (DE) for continuous optimization. We show that
unlike most other optimization paradigms, it is stable in the sense that
neutral bit values are sampled with probability close to $1/2$ for a long time.
This is generally a desirable property, however, it makes it harder to find the
optima for decision variables with small influence on the objective function.
This can result in an optimization time exponential in the dimension when
optimizing simple symmetric functions like OneMax. On the positive side, BDE
quickly detects and optimizes the most important decision variables. For
example, dominant bits converge to the optimal value in time logarithmic in the
population size. This enables BDE to optimize the most important bits very
fast. Overall, our results indicate that BDE is an interesting optimization
paradigm having characteristics significantly different from classic
evolutionary algorithms or estimation-of-distribution algorithms (EDAs).
  On the technical side, we observe that the strong stochastic dependencies in
the random experiment describing a run of BDE prevent us from proving all
desired results with the mathematical rigor that was successfully used in the
analysis of other evolutionary algorithms. Inspired by mean-field approaches in
statistical physics we propose a more independent variant of BDE, show
experimentally its similarity to BDE, and prove some statements rigorously only
for the independent variant. Such a semi-rigorous approach might be interesting
for other problems in evolutionary computation where purely mathematical
methods failed so far.
Physics-informed deep generative models
Authors: Yibo Yang, Paris Perdikaris
Date: 2018-12-09 00:00:00
Tasks: Variational Inference

We consider the application of deep generative models in propagating
uncertainty through complex physical systems. Specifically, we put forth an
implicit variational inference formulation that constrains the generative model
output to satisfy given physical laws expressed by partial differential
equations. Such physics-informed constraints provide a regularization mechanism
for effectively training deep probabilistic models for modeling physical
systems in which the cost of data acquisition is high and training data-sets
are typically small. This provides a scalable framework for characterizing
uncertainty in the outputs of physical systems due to randomness in their
inputs or noise in their observations. We demonstrate the effectiveness of our
approach through a canonical example in transport dynamics.
Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning
Authors: Ziming Li, Julia Kiseleva, Maarten de Rijke
Date: 2018-12-09 00:00:00
Tasks: Dialogue Generation, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The performance of adversarial dialogue generation models relies on the
quality of the reward signal produced by the discriminator. The reward signal
from a poor discriminator can be very sparse and unstable, which may lead the
generator to fall into a local optimum or to produce nonsense replies. To
alleviate the first problem, we first extend a recently proposed adversarial
dialogue generation method to an adversarial imitation learning solution. Then,
in the framework of adversarial inverse reinforcement learning, we propose a
new reward model for dialogue generation that can provide a more accurate and
precise reward signal for generator training. We evaluate the performance of
the resulting model with automatic metrics and human evaluations in two
annotation settings. Our experimental results demonstrate that our model can
generate more high-quality responses and achieve higher overall performance
than the state-of-the-art.
Proceedings of the eleventh Workshop on Answer Set Programming and Other Computing Paradigms 2018
Authors: Jorge Fandinno, Johannes Fichte
Date: 2018-12-09 00:00:00

This is the Proceedings of the eleventh Workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP) 2018, which was held in Oxford, UK, July 18th, 2018.
More Knowledge is Better: Cross-Modality Volume Completion and 3D+2D Segmentation for Intracardiac Echocardiography Contouring
Authors: Haofu Liao, Yucheng Tang, Gareth Funka-Lea, Jiebo Luo, Shaohua Kevin Zhou
Date: 2018-12-09 00:00:00
Tasks: Anatomy, Diagnostic

Using catheter ablation to treat atrial fibrillation increasingly relies on intracardiac echocardiography (ICE) for an anatomical delineation of the left atrium and the pulmonary veins that enter the atrium. However, it is a challenge to build an automatic contouring algorithm because ICE is noisy and provides only a limited 2D view of the 3D anatomy. This work provides the first automatic solution to segment the left atrium and the pulmonary veins from ICE. In this solution, we demonstrate the benefit of building a cross-modality framework that can leverage a database of diagnostic images to supplement the less available interventional images. To this end, we develop a novel deep neural network approach that uses the (i) 3D geometrical information provided by a position sensor embedded in the ICE catheter and the (ii) 3D image appearance information from a set of computed tomography cardiac volumes. We evaluate the proposed approach over 11,000 ICE images collected from 150 clinical patients. Experimental results show that our model is significantly better than a direct 2D image-to-image deep neural network segmentation, especially for less-observed structures.
From Coarse to Fine: Robust Hierarchical Localization at Large Scale
Authors: Paul-Edouard Sarlin, Cesar Cadena, Roland Siegwart, Marcin Dymczyk
Date: 2018-12-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: Autonomous Driving, Retrieval, Visual Localization, Visual Place Recognition

Robust and accurate visual localization is a fundamental capability for
numerous applications, such as autonomous driving, mobile robotics, or
augmented reality. It remains, however, a challenging task, particularly for
large-scale environments and in presence of significant appearance changes.
State-of-the-art methods not only struggle with such scenarios, but are often
too resource intensive for certain real-time applications. In this paper we
propose HF-Net, a hierarchical localization approach based on a monolithic CNN
that simultaneously predicts local features and global descriptors for accurate
6-DoF localization. We exploit the coarse-to-fine localization paradigm: we
first perform a global retrieval to obtain location hypotheses and only later
match local features within those candidate places. This hierarchical approach
incurs significant runtime savings and makes our system suitable for real-time
operation. By leveraging learned descriptors, our method achieves remarkable
localization robustness across large variations of appearance and sets a new
state-of-the-art on two challenging benchmarks for large-scale localization.
Adversarial Sparse-View CBCT Artifact Reduction
Authors: Haofu Liao, Zhimin Huo, William J. Sehnert, Shaohua Kevin Zhou, Jiebo Luo
Date: 2018-12-09 00:00:00
Tasks: Cbct Artifact Reduction

We present an effective post-processing method to reduce the artifacts from
sparsely reconstructed cone-beam CT (CBCT) images. The proposed method is based
on the state-of-the-art, image-to-image generative models with a perceptual
loss as regulation. Unlike the traditional CT artifact-reduction approaches,
our method is trained in an adversarial fashion that yields more perceptually
realistic outputs while preserving the anatomical structures. To address the
streak artifacts that are inherently local and appear across various scales, we
further propose a novel discriminator architecture based on feature pyramid
networks and a differentially modulated focus map to induce the adversarial
training. Our experimental results show that the proposed method can greatly
correct the cone-beam artifacts from clinical CBCT images reconstructed using
1/3 projections, and outperforms strong baseline methods both quantitatively
and qualitatively.
Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information
Authors: Haofu Liao, Addisu Mesfin, Jiebo Luo
Date: 2018-12-09 00:00:00
Tasks: Joint Vertebrae Identification And Localization In Spinal Ct Images

Automatic vertebrae identification and localization from arbitrary CT images
is challenging. Vertebrae usually share similar morphological appearance.
Because of pathology and the arbitrary field-of-view of CT scans, one can
hardly rely on the existence of some anchor vertebrae or parametric methods to
model the appearance and shape. To solve the problem, we argue that one should
make use of the short-range contextual information, such as the presence of
some nearby organs (if any), to roughly estimate the target vertebrae; due to
the unique anatomic structure of the spine column, vertebrae have fixed
sequential order which provides the important long-range contextual information
to further calibrate the results.
  We propose a robust and efficient vertebrae identification and localization
system that can inherently learn to incorporate both the short-range and
long-range contextual information in a supervised manner. To this end, we
develop a multi-task 3D fully convolutional neural network (3D FCN) to
effectively extract the short-range contextual information around the target
vertebrae. For the long-range contextual information, we propose a multi-task
bidirectional recurrent neural network (Bi-RNN) to encode the spatial and
contextual information among the vertebrae of the visible spine column. We
demonstrate the effectiveness of the proposed approach on a challenging dataset
and the experimental results show that our approach outperforms the
state-of-the-art methods by a significant margin.
To Reverse the Gradient or Not: An Empirical Comparison of Adversarial and Multi-task Learning in Speech Recognition
Authors: Yossi Adi, Neil Zeghidour, Ronan Collobert, Nicolas Usunier, Vitaliy Liptchinsky, Gabriel Synnaeve
Date: 2018-12-09 00:00:00
Tasks: Multi-Task Learning, Speaker Recognition, speech-recognition, Speech Recognition

Transcribed datasets typically contain speaker identity for each instance in
the data. We investigate two ways to incorporate this information during
training: Multi-Task Learning and Adversarial Learning. In multi-task learning,
the goal is speaker prediction; we expect a performance improvement with this
joint training if the two tasks of speech recognition and speaker recognition
share a common set of underlying features. In contrast, adversarial learning is
a means to learn representations invariant to the speaker. We then expect
better performance if this learnt invariance helps generalizing to new
speakers. While the two approaches seem natural in the context of speech
recognition, they are incompatible because they correspond to opposite
gradients back-propagated to the model. In order to better understand the
effect of these approaches in terms of error rates, we compare both strategies
in controlled settings. Moreover, we explore the use of additional
untranscribed data in a semi-supervised, adversarial learning manner to improve
error rates. Our results show that deep models trained on big datasets already
develop invariant representations to speakers without any auxiliary loss. When
considering adversarial learning and multi-task learning, the impact on the
acoustic model seems minor. However, models trained in a semi-supervised manner
can improve error-rates.
Comixify: Transform video into a comics
Authors: Maciej Pęśko, Adam Svystun, Paweł Andruszkiewicz, Przemysław Rokita, Tomasz Trzciński
Date: 2018-12-09 00:00:00
Tasks: Style Transfer, Transform A Video Into A Comics

In this paper, we propose a solution to transform a video into a comics. We
approach this task using a neural style algorithm based on Generative
Adversarial Networks (GANs). Several recent works in the field of Neural Style
Transfer showed that producing an image in the style of another image is
feasible. In this paper, we build up on these works and extend the existing set
of style transfer use cases with a working application of video comixification.
To that end, we train an end-to-end solution that transforms input video into a
comics in two stages. In the first stage, we propose a state-of-the-art
keyframes extraction algorithm that selects a subset of frames from the video
to provide the most comprehensive video context and we filter those frames
using image aesthetic estimation engine. In the second stage, the style of
selected keyframes is transferred into a comics. To provide the most
aesthetically compelling results, we selected the most state-of-the art style
transfer solution and based on that implement our own ComixGAN framework. The
final contribution of our work is a Web-based working application of video
comixification available at http://comixify.ii.pw.edu.pl.
Theory of Curriculum Learning, with Convex Loss Functions
Authors: Daphna Weinshall, Dan Amir
Date: 2018-12-09 00:00:00
Tasks: Binary Classification

Curriculum Learning - the idea of teaching by gradually exposing the learner
to examples in a meaningful order, from easy to hard, has been investigated in
the context of machine learning long ago. Although methods based on this
concept have been empirically shown to improve performance of several learning
algorithms, no theoretical analysis has been provided even for simple cases. To
address this shortfall, we start by formulating an ideal definition of
difficulty score - the loss of the optimal hypothesis at a given datapoint. We
analyze the possible contribution of curriculum learning based on this score in
two convex problems - linear regression, and binary classification by hinge
loss minimization. We show that in both cases, the expected convergence rate
decreases monotonically with the ideal difficulty score, in accordance with
earlier empirical results. We also prove that when the ideal difficulty score
is fixed, the convergence rate is monotonically increasing with respect to the
loss of the current hypothesis at each point. We discuss how these results
bring to term two apparently contradicting heuristics: curriculum learning on
the one hand, and hard data mining on the other.