pwc-archive/papers-with-abstracts huggingface dataset:
Rank-one matrix estimation: analysis of algorithmic and information theoretic limits by the spatial coupling method
Authors: Jean Barbier, Mohamad Dia, Nicolas Macris, Florent Krzakala, Lenka Zdeborová
Date: 2018-12-06 00:00:00
Tasks: Community Detection, Compressive Sensing

Factorizing low-rank matrices is a problem with many applications in machine
learning and statistics, ranging from sparse PCA to community detection and
sub-matrix localization. For probabilistic models in the Bayes optimal setting,
general expressions for the mutual information have been proposed using
powerful heuristic statistical physics computations via the replica and cavity
methods, and proven in few specific cases by a variety of methods. Here, we use
the spatial coupling methodology developed in the framework of error correcting
codes, to rigorously derive the mutual information for the symmetric rank-one
case. We characterize the detectability phase transitions in a large set of
estimation problems, where we show that there exists a gap between what
currently known polynomial algorithms (in particular spectral methods and
approximate message-passing) can do and what is expected information
theoretically. Moreover, we show that the computational gap vanishes for the
proposed spatially coupled model, a promising feature with many possible
applications. Our proof technique has an interest on its own and exploits three
essential ingredients: the interpolation method first introduced in statistical
physics, the analysis of approximate message-passing algorithms first
introduced in compressive sensing, and the theory of threshold saturation for
spatially coupled systems first developed in coding theory. Our approach is
very generic and can be applied to many other open problems in statistical
estimation where heuristic statistical physics predictions are available.
Evaluating Architectural Choices for Deep Learning Approaches for Question Answering over Knowledge Bases
Authors: Sherzod Hakimov, Soufian Jebbara, Philipp Cimiano
Date: 2018-12-06 00:00:00
Tasks: Question Answering

The task of answering natural language questions over knowledge bases has
received wide attention in recent years. Various deep learning architectures
have been proposed for this task. However, architectural design choices are
typically not systematically compared nor evaluated under the same conditions.
In this paper, we contribute to a better understanding of the impact of
architectural design choices by evaluating four different architectures under
the same conditions. We address the task of answering simple questions,
consisting in predicting the subject and predicate of a triple given a
question. In order to provide a fair comparison of different architectures, we
evaluate them under the same strategy for inferring the subject, and compare
different architectures for inferring the predicate. The architecture for
inferring the subject is based on a standard LSTM model trained to recognize
the span of the subject in the question and on a linking component that links
the subject span to an entity in the knowledge base. The architectures for
predicate inference are based on i) a standard softmax classifier ranging over
all predicates as output, iii) a model that predicts a low-dimensional encoding
of the property given entity representation and question, iii) a model that
learns to score a pair of subject and predicate given the question as well as
iv) a model based on the well-known FastText model. The comparison of
architectures shows that FastText provides better results than other
architectures.
On the stability analysis of deep neural network representations of an optimal state-feedback
Authors: Dario Izzo, Dharmesh Tailor, Thomas Vasileiou
Date: 2018-12-06 00:00:00
Tasks: Imitation Learning

Recent work have shown how the optimal state-feedback, obtained as the
solution to the Hamilton-Jacobi-Bellman equations, can be approximated for
several nonlinear, deterministic systems by deep neural networks. When
imitation (supervised) learning is used to train the neural network on optimal
state-action pairs, for instance as derived by applying Pontryagin's theory of
optimal processes, the resulting model is referred here as the guidance and
control network. In this work, we analyze the stability of nonlinear and
deterministic systems controlled by such networks. We then propose a method
utilising differential algebraic techniques and high-order Taylor maps to gain
information on the stability of the neurocontrolled state trajectories. We
exemplify the proposed methods in the case of the two-dimensional dynamics of a
quadcopter controlled to reach the origin and we study how different
architectures of the guidance and control network affect the stability of the
target equilibrium point and the stability margins to time delay. Moreover, we
show how to study the robustness to initial conditions of a nominal trajectory,
using a Taylor representation of the neurocontrolled neighbouring trajectories.
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack
Authors: Jingyang Zhang, Hsin-Pai Cheng, Chunpeng Wu, Hai Li, Yiran Chen
Date: 2018-12-06 00:00:00
Tasks: Adversarial Attack

In recent years, deep neural networks demonstrated state-of-the-art
performance in a large variety of tasks and therefore have been adopted in many
applications. On the other hand, the latest studies revealed that neural
networks are vulnerable to adversarial examples obtained by carefully adding
small perturbation to legitimate samples. Based upon the observation, many
attack methods were proposed. Among them, the optimization-based CW attack is
the most powerful as the produced adversarial samples present much less
distortion compared to other methods. The better attacking effect, however,
comes at the cost of running more iterations and thus longer computation time
to reach desirable results. In this work, we propose to leverage the
information of gradients as a guidance during the search of adversaries. More
specifically, directly incorporating the gradients into the perturbation can be
regarded as a constraint added to the optimization process. We intuitively and
empirically prove the rationality of our method in reducing the search space.
Our experiments show that compared to the original CW attack, the proposed
method requires fewer iterations towards adversarial samples, obtaining a
higher success rate and resulting in smaller $\ell_2$ distortion.
A novel health risk model based on intraday physical activity time series collected by smartphones
Authors: Evgeny Getmantsev, Boris Zhurov, Timothy V. Pyrkov, Peter O. Fedichev
Date: 2018-12-06 00:00:00
Tasks: Domain Adaptation, Time Series, Time Series Analysis

We compiled a demo application and collected a motion database of more than
10,000 smartphone users to produce a health risk model trained on physical
activity streams. We turned to adversarial domain adaptation and employed the
UK Biobank dataset of motion data, augmented by a rich set of clinical
information as the source domain to train the model using a deep residual
convolutional neuron network (ResNet). The model risk score is a biomarker of
ageing, since it was predictive of lifespan and healthspan (as defined by the
onset of specified diseases), and was elevated in groups associated with
life-shortening lifestyles, such as smoking. We ascertained the target domain
performance in a smaller cohort of the mobile application that included users
who were willing to share answers to a short questionnaire related to their
disease and smoking status. We thus conclude that the proposed pipeline
combining deep convolutional and Domain Adversarial neuron networks (DANN) is a
powerful tool for disease risk and lifestyle-associated hazard assessment from
mobile motion sensors that are transferable across devices and populations.
Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets and a Contour Loss
Authors: Shuman Jia, Antoine Despinasse, ZiHao Wang, Hervé Delingette, Xavier Pennec, Pierre Jaïs, Hubert Cochet, Maxime Sermesant
Date: 2018-12-06 00:00:00
Tasks: Anatomy, Image Segmentation, Segmentation, Semantic Segmentation, Specificity

Radiological imaging offers effective measurement of anatomy, which is useful
in disease diagnosis and assessment. Previous study has shown that the left
atrial wall remodeling can provide information to predict treatment outcome in
atrial fibrillation. Nevertheless, the segmentation of the left atrial
structures from medical images is still very time-consuming. Current advances
in neural network may help creating automatic segmentation models that reduce
the workload for clinicians. In this preliminary study, we propose automated,
two-stage, three-dimensional U-Nets with convolutional neural network, for the
challenging task of left atrial segmentation. Unlike previous two-dimensional
image segmentation methods, we use 3D U-Nets to obtain the heart cavity
directly in 3D. The dual 3D U-Net structure consists of, a first U-Net to
coarsely segment and locate the left atrium, and a second U-Net to accurately
segment the left atrium under higher resolution. In addition, we introduce a
Contour loss based on additional distance information to adjust the final
segmentation. We randomly split the data into training datasets (80 subjects)
and validation datasets (20 subjects) to train multiple models, with different
augmentation setting. Experiments show that the average Dice coefficients for
validation datasets are around 0.91 - 0.92, the sensitivity around 0.90-0.94
and the specificity 0.99. Compared with traditional Dice loss, models trained
with Contour loss in general offer smaller Hausdorff distance with similar Dice
coefficient, and have less connected components in predictions. Finally, we
integrate several trained models in an ensemble prediction to segment testing
datasets.
ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection
Authors: Davide Cozzolino, Justus Thies, Andreas Rössler, Christian Riess, Matthias Nießner, Luisa Verdoliva
Date: 2018-12-06 00:00:00
Tasks: Domain Adaptation

Distinguishing manipulated from real images is becoming increasingly difficult as new sophisticated image forgery approaches come out by the day. Naive classification approaches based on Convolutional Neural Networks (CNNs) show excellent performance in detecting image manipulations when they are trained on a specific forgery method. However, on examples from unseen manipulation approaches, their performance drops significantly. To address this limitation in transferability, we introduce Forensic-Transfer (FT). We devise a learning-based forensic detector which adapts well to new domains, i.e., novel manipulation methods and can handle scenarios where only a handful of fake examples are available during training. To this end, we learn a forensic embedding based on a novel autoencoder-based architecture that can be used to distinguish between real and fake imagery. The learned embedding acts as a form of anomaly detector; namely, an image manipulated from an unseen method will be detected as fake provided it maps sufficiently far away from the cluster of real images. Comparing to prior works, FT shows significant improvements in transferability, which we demonstrate in a series of experiments on cutting-edge benchmarks. For instance, on unseen examples, we achieve up to 85% in terms of accuracy, and with only a handful of seen examples, our performance already reaches around 95%.
Observing the Population Dynamics in GE by means of the Intrinsic Dimension
Authors: Eric Medvet, Alberto Bartoli, Alessio Ansuini, Fabiano Tarlao
Date: 2018-12-06 00:00:00
Tasks: BIG-bench Machine Learning, Diversity, Evolutionary Algorithms

We explore the use of Intrinsic Dimension (ID) for gaining insights in how
populations evolve in Evolutionary Algorithms. ID measures the minimum number
of dimensions needed to accurately describe a dataset and its estimators are
being used more and more in Machine Learning to cope with large datasets. We
postulate that ID can provide information about population which is
complimentary w.r.t.\ what (a simple measure of) diversity tells. We
experimented with the application of ID to populations evolved with a recent
variant of Grammatical Evolution. The preliminary results suggest that
diversity and ID constitute two different points of view on the population
dynamics.
Zero-Shot Anticipation for Instructional Activities
Authors: Fadime Sener, Angela Yao
Date: 2018-12-06 00:00:00
Proceeding: ICCV 2019 10
Tasks: Zero-Shot Learning

How can we teach a robot to predict what will happen next for an activity it has never seen before? We address this problem of zero-shot anticipation by presenting a hierarchical model that generalizes instructional knowledge from large-scale text-corpora and transfers the knowledge to the visual domain. Given a portion of an instructional video, our model predicts coherent and plausible actions multiple steps into the future, all in rich natural language. To demonstrate the anticipation capabilities of our model, we introduce the Tasty Videos dataset, a collection of 2511 recipes for zero-shot learning, recognition and anticipation.
A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization
Authors: Peng Yang, Ke Tang, Xin Yao
Date: 2018-12-06 00:00:00
Tasks: Evolutionary Algorithms

Large-scale optimization problems that involve thousands of decision
variables have extensively arisen from various industrial areas. As a powerful
optimization tool for many real-world applications, evolutionary algorithms
(EAs) fail to solve the emerging large-scale problems both effectively and
efficiently. In this paper, we propose a novel Divide-and-Conquer (DC) based EA
that can not only produce high-quality solution by solving sub-problems
separately, but also highly utilizes the power of parallel computing by solving
the sub-problems simultaneously. Existing DC-based EAs that were deemed to
enjoy the same advantages of the proposed algorithm, are shown to be
practically incompatible with the parallel computing scheme, unless some
trade-offs are made by compromising the solution quality.
Active Learning Methods based on Statistical Leverage Scores
Authors: Cem Orhan, Oznur Tastan
Date: 2018-12-06 00:00:00
Tasks: Active Learning

In many real-world machine learning applications, unlabeled data are abundant
whereas class labels are expensive and scarce. An active learner aims to obtain
a model of high accuracy with as few labeled instances as possible by
effectively selecting useful examples for labeling. We propose a new selection
criterion that is based on statistical leverage scores and present two novel
active learning methods based on this criterion: ALEVS for querying single
example at each iteration and DBALEVS for querying a batch of examples. To
assess the representativeness of the examples in the pool, ALEVS and DBALEVS
use the statistical leverage scores of the kernel matrices computed on the
examples of each class. Additionally, DBALEVS selects a diverse a set of
examples that are highly representative but are dissimilar to already labeled
examples through maximizing a submodular set function defined with the
statistical leverage scores and the kernel matrix computed on the pool of the
examples. The submodularity property of the set scoring function let us
identify batches with a constant factor approximate to the optimal batch in an
efficient manner. Our experiments on diverse datasets show that querying based
on leverage scores is a powerful strategy for active learning.
Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
Authors: David Robben, Anna M. M. Boers, Henk A. Marquering, Lucianne L. C. M. Langezaal, Yvo B. W. E. M. Roos, Robert J. van Oostenbrugge, Wim H. van Zwam, Diederik W. J. Dippel, Charles B. L. M. Majoie, Aad van der Lugt, Robin Lemmens, Paul Suetens
Date: 2018-12-06 00:00:00

CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-driven and deconvolution-free approach, where a deep neural network learns to predict the final infarct volume directly from the native CTP images and metadata such as the time parameters and treatment. This would allow clinicians to simulate various treatments and gain insight into predicted tissue status over time. We demonstrate on a multicenter dataset that our approach is able to predict the final infarct and effectively uses the metadata. An ablation study shows that using the native CTP measurements instead of the deconvolved measurements improves the prediction.
Learning to Infer the Depth Map of a Hand from its Color Image
Authors: Vassilis C. Nicodemou, Iason Oikonomidis, Georgios Tzimiropoulos, Antonis Argyros
Date: 2018-12-06 00:00:00
Tasks: 3D Reconstruction, Depth Estimation, Hand Segmentation

We propose the first approach to the problem of inferring the depth map of a
human hand based on a single RGB image. We achieve this with a Convolutional
Neural Network (CNN) that employs a stacked hourglass model as its main
building block. Intermediate supervision is used in several outputs of the
proposed architecture in a staged approach. To aid the process of training and
inference, hand segmentation masks are also estimated in such an intermediate
supervision step, and used to guide the subsequent depth estimation process. In
order to train and evaluate the proposed method we compile and make publicly
available HandRGBD, a new dataset of 20,601 views of hands, each consisting of
an RGB image and an aligned depth map. Based on HandRGBD, we explore variants
of the proposed approach in an ablative study and determine the best performing
one. The results of an extensive experimental evaluation demonstrate that hand
depth estimation from a single RGB frame can be achieved with an accuracy of
22mm, which is comparable to the accuracy achieved by contemporary low-cost
depth cameras. Such a 3D reconstruction of hands based on RGB information is
valuable as a final result on its own right, but also as an input to several
other hand analysis and perception algorithms that require depth input.
Essentially, in such a context, the proposed approach bridges the gap between
RGB and RGBD, by making all existing RGBD-based methods applicable to RGB
input.
Binary Document Image Super Resolution for Improved Readability and OCR Performance
Authors: Ram Krishna Pandey, K Vignesh, A. G. Ramakrishnan, Chandrahasa B
Date: 2018-12-06 00:00:00
Tasks: Image Super-Resolution, Information Retrieval, Optical Character Recognition, Optical Character Recognition (OCR), Retrieval, Super-Resolution

There is a need for information retrieval from large collections of
low-resolution (LR) binary document images, which can be found in digital
libraries across the world, where the high-resolution (HR) counterpart is not
available. This gives rise to the problem of binary document image
super-resolution (BDISR). The objective of this paper is to address the
interesting and challenging problem of super resolution of binary Tamil
document images for improved readability and better optical character
recognition (OCR). We propose multiple deep neural network architectures to
address this problem and analyze their performance. The proposed models are all
single image super-resolution techniques, which learn a generalized spatial
correspondence between the LR and HR binary document images. We employ
convolutional layers for feature extraction followed by transposed convolution
and sub-pixel convolution layers for upscaling the features. Since the outputs
of the neural networks are gray scale, we utilize the advantage of power law
transformation as a post-processing technique to improve the character level
pixel connectivity. The performance of our models is evaluated by comparing the
OCR accuracies and the mean opinion scores given by human evaluators on LR
images and the corresponding model-generated HR images.
The Role of Normware in Trustworthy and Explainable AI
Authors: Giovanni Sileno, Alexander Boer, Tom van Engers
Date: 2018-12-06 00:00:00
Tasks: Decision Making, Position

For being potentially destructive, in practice incomprehensible and for the
most unintelligible, contemporary technology is setting high challenges on our
society. New conception methods are urgently required. Reorganizing ideas and
discussions presented in AI and related fields, this position paper aims to
highlight the importance of normware--that is, computational artifacts
specifying norms--with respect to these issues, and argues for its
irreducibility with respect to software by making explicit its neglected
ecological dimension in the decision-making cycle.
Deep Embedding using Bayesian Risk Minimization with Application to Sketch Recognition
Authors: Anand Mishra, Ajeet Kumar Singh
Date: 2018-12-06 00:00:00
Tasks: Metric Learning, Sketch Recognition

In this paper, we address the problem of hand-drawn sketch recognition.
Inspired by the Bayesian decision theory, we present a deep metric learning
loss with the objective to minimize the Bayesian risk of misclassification. We
estimate this risk for every mini-batch during training, and learn robust deep
embeddings by backpropagating it to a deep neural network in an end-to-end
trainable paradigm. Our learnt embeddings are discriminative and robust despite
of intra-class variations and inter-class similarities naturally present in
hand-drawn sketch images. Outperforming the state of the art on sketch
recognition, our method achieves 82.2% and 88.7% on TU-Berlin-250 and
TU-Berlin-160 benchmarks respectively.
Fast and Accurate Person Re-Identification with RMNet
Authors: Evgeny Izutov
Date: 2018-12-06 00:00:00
Tasks: Person Re-Identification

In this paper we introduce a new neural network architecture designed to use
in embedded vision applications. It merges the best working practices of
network architectures like MobileNets and ResNets to our named RMNet
architecture. We also focus on key moments of building mobile architectures to
carry out in the limited computation budget. Additionally, to demonstrate the
effectiveness of our architecture we evaluate the RMNet backbone on Person
Re-identification task. The proposed approach is in top 3 of state of the art
solutions on Market-1501 challenge, however our method significantly
outperforms them by the inference speed.
Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting
Authors: Craig Atkinson, Brendan McCane, Lech Szymanski, Anthony Robins
Date: 2018-12-06 00:00:00
Tasks: Atari Games, Continual Learning, Deep Reinforcement Learning, image-classification, Image Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Neural networks can achieve excellent results in a wide variety of applications. However, when they attempt to sequentially learn, they tend to learn the new task while catastrophically forgetting previous ones. We propose a model that overcomes catastrophic forgetting in sequential reinforcement learning by combining ideas from continual learning in both the image classification domain and the reinforcement learning domain. This model features a dual memory system which separates continual learning from reinforcement learning and a pseudo-rehearsal system that "recalls" items representative of previous tasks via a deep generative network. Our model sequentially learns Atari 2600 games without demonstrating catastrophic forgetting and continues to perform above human level on all three games. This result is achieved without: demanding additional storage requirements as the number of tasks increases, storing raw data or revisiting past tasks. In comparison, previous state-of-the-art solutions are substantially more vulnerable to forgetting on these complex deep reinforcement learning tasks.