pwc-archive/papers-with-abstracts huggingface dataset:
Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN
Authors: Dror Freirich, Ron Meir, Aviv Tamar
Date: 2018-08-06 00:00:00
Tasks: Generative Adversarial Network, Reinforcement Learning

The recently proposed distributional approach to reinforcement learning
(DiRL) is centered on learning the distribution of the reward-to-go, often
referred to as the value distribution. In this work, we show that the
distributional Bellman equation, which drives DiRL methods, is equivalent to a
generative adversarial network (GAN) model. In this formulation, DiRL can be
seen as learning a deep generative model of the value distribution, driven by
the discrepancy between the distribution of the current value, and the
distribution of the sum of current reward and next value. We use this insight
to propose a GAN-based approach to DiRL, which leverages the strengths of GANs
in learning distributions of high-dimensional data. In particular, we show that
our GAN approach can be used for DiRL with multivariate rewards, an important
setting which cannot be tackled with prior methods. The multivariate setting
also allows us to unify learning the distribution of values and state
transitions, and we exploit this idea to devise a novel exploration method that
is driven by the discrepancy in estimating both values and states.
A Review on Image- and Network-based Brain Data Analysis Techniques for Alzheimer's Disease Diagnosis Reveals a Gap in Developing Predictive Methods for Prognosis
Authors: Mayssa Soussia, Islem Rekik
Date: 2018-08-06 00:00:00
Tasks: Prognosis

Unveiling pathological brain changes associated with Alzheimer's disease (AD)
is a challenging task especially that people do not show symptoms of dementia
until it is late. Over the past years, neuroimaging techniques paved the way
for computer-based diagnosis and prognosis to facilitate the automation of
medical decision support and help clinicians identify cognitively intact
subjects that are at high-risk of developing AD. As a progressive
neurodegenerative disorder, researchers investigated how AD affects the brain
using different approaches: 1) image-based methods where mainly neuroimaging
modalities are used to provide early AD biomarkers, and 2) network-based
methods which focus on functional and structural brain connectivities to give
insights into how AD alters brain wiring. In this study, we reviewed
neuroimaging-based technical methods developed for AD and mild-cognitive
impairment (MCI) classification and prediction tasks, selected by screening all
MICCAI proceedings published between 2010 and 2016. We included papers that fit
into image-based or network-based categories. The majority of papers focused on
classifying MCI vs. AD brain states, which has enabled the discovery of
discriminative or altered brain regions and connections. However, very few
works aimed to predict MCI progression based on early neuroimaging-based
observations. Despite the high importance of reliably identifying which early
MCI patient will convert to AD, remain stable or reverse to normal over
months/years, predictive models are still lagging behind.
Deep Shape Analysis on Abdominal Organs for Diabetes Prediction
Authors: Benjamin Gutierrez-Becker, Sergios Gatidis, Daniel Gutmann, Annette Peters, Christopher Schlett Fabian Bamberg, Christian Wachinger
Date: 2018-08-06 00:00:00
Tasks: Diabetes Prediction, Morphological Analysis, Prediction

Morphological analysis of organs based on images is a key task in medical
imaging computing. Several approaches have been proposed for the quantitative
assessment of morphological changes, and they have been widely used for the
analysis of the effects of aging, disease and other factors in organ
morphology. In this work, we propose a deep neural network for predicting
diabetes on abdominal shapes. The network directly operates on raw point clouds
without requiring mesh processing or shape alignment. Instead of relying on
hand-crafted shape descriptors, an optimal representation is learned in the
end-to-end training stage of the network. For comparison, we extend the
state-of-the-art shape descriptor BrainPrint to the AbdomenPrint. Our results
demonstrate that the network learns shape representations that better separates
healthy and diabetic individuals than traditional representations.
V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentation
Authors: Nicoló Savioli, Giovanni Montana, Pablo Lamata
Date: 2018-08-06 00:00:00
Tasks: Anatomy

Atrial Fibrillation (AF) is a common electro-physiological cardiac disorder
that causes changes in the anatomy of the atria. A better characterization of
these changes is desirable for the definition of clinical biomarkers,
furthermore, thus there is a need for its fully automatic segmentation from
clinical images. In this work, we present an architecture based on
3D-convolution kernels, a Volumetric Fully Convolution Neural Network (V-FCNN),
able to segment the entire volume in a one-shot, and consequently integrate the
implicit spatial redundancy present in high-resolution images. A loss function
based on the mixture of both Mean Square Error (MSE) and Dice Loss (DL) is
used, in an attempt to combine the ability to capture the bulk shape as well as
the reduction of local errors products by over-segmentation. Results
demonstrate a reasonable performance in the middle region of the atria along
with the impact of the challenges of capturing the variability of the pulmonary
veins or the identification of the valve plane that separates the atria to the
ventricle. A final dice of $92.5\%$ in $54$ patients ($4752$ atria test slices
in total) is shown.
Error Correction Maximization for Deep Image Hashing
Authors: Xiang Xu, Xiaofang Wang, Kris M. Kitani
Date: 2018-08-06 00:00:00

We propose to use the concept of the Hamming bound to derive the optimal
criteria for learning hash codes with a deep network. In particular, when the
number of binary hash codes (typically the number of image categories) and code
length are known, it is possible to derive an upper bound on the minimum
Hamming distance between the hash codes. This upper bound can then be used to
define the loss function for learning hash codes. By encouraging the margin
(minimum Hamming distance) between the hash codes of different image categories
to match the upper bound, we are able to learn theoretically optimal hash
codes. Our experiments show that our method significantly outperforms competing
deep learning-based approaches and obtains top performance on benchmark
datasets.
Audio Tagging With Connectionist Temporal Classification Model Using Sequential Labelled Data
Authors: Yuanbo Hou, Qiuqiang Kong, Shengchen Li
Date: 2018-08-06 00:00:00
Tasks: Audio Tagging, General Classification

Audio tagging aims to predict one or several labels in an audio clip. Many
previous works use weakly labelled data (WLD) for audio tagging, where only
presence or absence of sound events is known, but the order of sound events is
unknown. To use the order information of sound events, we propose sequential
labelled data (SLD), where both the presence or absence and the order
information of sound events are known. To utilize SLD in audio tagging, we
propose a Convolutional Recurrent Neural Network followed by a Connectionist
Temporal Classification (CRNN-CTC) objective function to map from an audio clip
spectrogram to SLD. Experiments show that CRNN-CTC obtains an Area Under Curve
(AUC) score of 0.986 in audio tagging, outperforming the baseline CRNN of 0.908
and 0.815 with Max Pooling and Average Pooling, respectively. In addition, we
show CRNN-CTC has the ability to predict the order of sound events in an audio
clip.
Residual Memory Networks: Feed-forward approach to learn long temporal dependencies
Authors: Murali Karthick Baskar, Martin Karafiat, Lukas Burget, Karel Vesely, Frantisek Grezl, Jan Honza Cernocky
Date: 2018-08-06 00:00:00

Training deep recurrent neural network (RNN) architectures is complicated due
to the increased network complexity. This disrupts the learning of higher order
abstracts using deep RNN. In case of feed-forward networks training deep
structures is simple and faster while learning long-term temporal information
is not possible. In this paper we propose a residual memory neural network
(RMN) architecture to model short-time dependencies using deep feed-forward
layers having residual and time delayed connections. The residual connection
paves way to construct deeper networks by enabling unhindered flow of gradients
and the time delay units capture temporal information with shared weights. The
number of layers in RMN signifies both the hierarchical processing depth and
temporal depth. The computational complexity in training RMN is significantly
less when compared to deep recurrent networks. RMN is further extended as
bi-directional RMN (BRMN) to capture both past and future information.
Experimental analysis is done on AMI corpus to substantiate the capability of
RMN in learning long-term information and hierarchical information. Recognition
performance of RMN trained with 300 hours of Switchboard corpus is compared
with various state-of-the-art LVCSR systems. The results indicate that RMN and
BRMN gains 6 % and 3.8 % relative improvement over LSTM and BLSTM networks.
DeepTAM: Deep Tracking and Mapping
Authors: Huizhong Zhou, Benjamin Ummenhofer, Thomas Brox
Date: 2018-08-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Depth Estimation, Depth Prediction

We present a system for keyframe-based dense camera tracking and depth map
estimation that is entirely learned. For tracking, we estimate small pose
increments between the current camera image and a synthetic viewpoint. This
significantly simplifies the learning problem and alleviates the dataset bias
for camera motions. Further, we show that generating a large number of pose
hypotheses leads to more accurate predictions. For mapping, we accumulate
information in a cost volume centered at the current depth estimate. The
mapping network then combines the cost volume and the keyframe image to update
the depth prediction, thereby effectively making use of depth measurements and
image-based priors. Our approach yields state-of-the-art results with few
images and is robust with respect to noisy camera poses. We demonstrate that
the performance of our 6 DOF tracking competes with RGB-D tracking algorithms.
We compare favorably against strong classic and deep learning powered dense
depth algorithms.
An Efficient Deep Reinforcement Learning Model for Urban Traffic Control
Authors: Yilun Lin, Xingyuan Dai, Li Li, Fei-Yue Wang
Date: 2018-08-06 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Urban Traffic Control (UTC) plays an essential role in Intelligent
Transportation System (ITS) but remains difficult. Since model-based UTC
methods may not accurately describe the complex nature of traffic dynamics in
all situations, model-free data-driven UTC methods, especially reinforcement
learning (RL) based UTC methods, received increasing interests in the last
decade. However, existing DL approaches did not propose an efficient algorithm
to solve the complicated multiple intersections control problems whose
state-action spaces are vast. To solve this problem, we propose a Deep
Reinforcement Learning (DRL) algorithm that combines several tricks to master
an appropriate control strategy within an acceptable time. This new algorithm
relaxes the fixed traffic demand pattern assumption and reduces human invention
in parameter tuning. Simulation experiments have shown that our method
outperforms traditional rule-based approaches and has the potential to handle
more complex traffic problems in the real world.
Reasoning with Justifiable Exceptions in Contextual Hierarchies (Appendix)
Authors: Loris Bozzato, Luciano Serafini, Thomas Eiter
Date: 2018-08-06 00:00:00
Tasks: Translation

This paper is an appendix to the paper "Reasoning with Justifiable Exceptions
in Contextual Hierarchies" by Bozzato, Serafini and Eiter, 2018. It provides
further details on the language, the complexity results and the datalog
translation introduced in the main paper.
Metal Artifact Reduction in Cone-Beam X-Ray CT via Ray Profile Correction
Authors: Sungsoo Ha, Klaus Mueller
Date: 2018-08-06 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction, Diagnostic, Metal Artifact Reduction

In computed tomography (CT), metal implants increase the inconsistencies
between the measured data and the linear attenuation assumption made by
analytic CT reconstruction algorithms. The inconsistencies give rise to dark
and bright bands and streaks in the reconstructed image, collectively called
metal artifacts. These artifacts make it difficult for radiologists to render
correct diagnostic decisions. We describe a data-driven metal artifact
reduction (MAR) algorithm for image-guided spine surgery that applies to
scenarios in which a prior CT scan of the patient is available. We tested the
proposed method with two clinical datasets that were both obtained during spine
surgery. Using the proposed method, we were not only able to remove the dark
and bright streaks caused by the implanted screws but we also recovered the
anatomical structures hidden by these artifacts. This results in an improved
capability of surgeons to confirm the correctness of the implanted pedicle
screw placements.
An Efficient Approach to Learning Chinese Judgment Document Similarity Based on Knowledge Summarization
Authors: Yinglong Ma, Peng Zhang, Jiangang Ma
Date: 2018-08-06 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

A previous similar case in common law systems can be used as a reference with
respect to the current case such that identical situations can be treated
similarly in every case. However, current approaches for judgment document
similarity computation failed to capture the core semantics of judgment
documents and therefore suffer from lower accuracy and higher computation
complexity. In this paper, a knowledge block summarization based machine
learning approach is proposed to compute the semantic similarity of Chinese
judgment documents. By utilizing domain ontologies for judgment documents, the
core semantics of Chinese judgment documents is summarized based on knowledge
blocks. Then the WMD algorithm is used to calculate the similarity between
knowledge blocks. At last, the related experiments were made to illustrate that
our approach is very effective and efficient in achieving higher accuracy and
faster computation speed in comparison with the traditional approaches.
Beyond $1/2$-Approximation for Submodular Maximization on Massive Data Streams
Authors: Ashkan Norouzi-Fard, Jakub Tarnawski, Slobodan Mitrović, Amir Zandieh, Aida Mousavifar, Ola Svensson
Date: 2018-08-06 00:00:00
Tasks: Clustering, Recommendation Systems

Many tasks in machine learning and data mining, such as data diversification,
non-parametric learning, kernel machines, clustering etc., require extracting a
small but representative summary from a massive dataset. Often, such problems
can be posed as maximizing a submodular set function subject to a cardinality
constraint. We consider this question in the streaming setting, where elements
arrive over time at a fast pace and thus we need to design an efficient,
low-memory algorithm. One such method, proposed by Badanidiyuru et al. (2014),
always finds a $0.5$-approximate solution. Can this approximation factor be
improved? We answer this question affirmatively by designing a new algorithm
SALSA for streaming submodular maximization. It is the first low-memory,
single-pass algorithm that improves the factor $0.5$, under the natural
assumption that elements arrive in a random order. We also show that this
assumption is necessary, i.e., that there is no such algorithm with better than
$0.5$-approximation when elements arrive in arbitrary order. Our experiments
demonstrate that SALSA significantly outperforms the state of the art in
applications related to exemplar-based clustering, social graph analysis, and
recommender systems.
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation
Authors: Eddy Ilg, Tonmoy Saikia, Margret Keuper, Thomas Brox
Date: 2018-08-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Motion Segmentation, Optical Flow Estimation, Scene Flow Estimation, Segmentation

Occlusions play an important role in disparity and optical flow estimation,
since matching costs are not available in occluded areas and occlusions
indicate depth or motion boundaries. Moreover, occlusions are relevant for
motion segmentation and scene flow estimation. In this paper, we present an
efficient learning-based approach to estimate occlusion areas jointly with
disparities or optical flow. The estimated occlusions and motion boundaries
clearly improve over the state-of-the-art. Moreover, we present networks with
state-of-the-art performance on the popular KITTI benchmark and good generic
performance. Making use of the estimated occlusions, we also show improved
results on motion segmentation and scene flow estimation.
Improving Temporal Interpolation of Head and Body Pose using Gaussian Process Regression in a Matrix Completion Setting
Authors: Stephanie Tan, Hayley Hung
Date: 2018-08-06 00:00:00
Tasks: Matrix Completion, Missing Labels, Pose Estimation

This paper presents a model for head and body pose estimation (HBPE) when
labelled samples are highly sparse. The current state-of-the-art multimodal
approach to HBPE utilizes the matrix completion method in a transductive
setting to predict pose labels for unobserved samples. Based on this approach,
the proposed method tackles HBPE when manually annotated ground truth labels
are temporally sparse. We posit that the current state of the art approach
oversimplifies the temporal sparsity assumption by using Laplacian smoothing.
Our final solution uses: i) Gaussian process regression in place of Laplacian
smoothing, ii) head and body coupling, and iii) nuclear norm minimization in
the matrix completion setting. The model is applied to the challenging SALSA
dataset for benchmark against the state-of-the-art method. Our presented
formulation outperforms the state-of-the-art significantly in this particular
setting, e.g. at 5% ground truth labels as training data, head pose accuracy
and body pose accuracy is approximately 62% and 70%, respectively. As well as
fitting a more flexible model to missing labels in time, we posit that our
approach also loosens the head and body coupling constraint, allowing for a
more expressive model of the head and body pose typically seen during
conversational interaction in groups. This provides a new baseline to improve
upon for future integration of multimodal sensor data for the purpose of HBPE.
Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform
Authors: Lingni Ma, Jörg Stückler, Tao Wu, Daniel Cremers
Date: 2018-08-06 00:00:00
Tasks: Decoder, Semantic Segmentation

Dense pixelwise prediction such as semantic segmentation is an up-to-date
challenge for deep convolutional neural networks (CNNs). Many state-of-the-art
approaches either tackle the loss of high-resolution information due to pooling
in the encoder stage, or use dilated convolutions or high-resolution lanes to
maintain detailed feature maps and predictions. Motivated by the structural
analogy between multi-resolution wavelet analysis and the pooling/unpooling
layers of CNNs, we introduce discrete wavelet transform (DWT) into the CNN
encoder-decoder architecture and propose WCNN. The high-frequency wavelet
coefficients are computed at encoder, which are later used at the decoder to
unpooled jointly with coarse-resolution feature maps through the inverse DWT.
The DWT/iDWT is further used to develop two wavelet pyramids to capture the
global context, where the multi-resolution DWT is applied to successively
reduce the spatial resolution and increase the receptive field. Experiment with
the Cityscape dataset, the proposed WCNNs are computationally efficient and
yield improvements the accuracy for high-resolution dense pixelwise prediction.
Visual Question Generation for Class Acquisition of Unknown Objects
Authors: Kohei Uehara, Antonio Tejero-de-Pablos, Yoshitaka Ushiku, Tatsuya Harada
Date: 2018-08-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Question Generation, Question-Generation

Traditional image recognition methods only consider objects belonging to
already learned classes. However, since training a recognition model with every
object class in the world is unfeasible, a way of getting information on
unknown objects (i.e., objects whose class has not been learned) is necessary.
A way for an image recognition system to learn new classes could be asking a
human about objects that are unknown. In this paper, we propose a method for
generating questions about unknown objects in an image, as means to get
information about classes that have not been learned. Our method consists of a
module for proposing objects, a module for identifying unknown objects, and a
module for generating questions about unknown objects. The experimental results
via human evaluation show that our method can successfully get information
about unknown objects in an image dataset. Our code and dataset are available
at https://github.com/mil-tokyo/vqg-unknown.
Regret Bounds for Reinforcement Learning via Markov Chain Concentration
Authors: Ronald Ortner
Date: 2018-08-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We give a simple optimistic algorithm for which it is easy to derive regret
bounds of $\tilde{O}(\sqrt{t_{\rm mix} SAT})$ after $T$ steps in uniformly
ergodic Markov decision processes with $S$ states, $A$ actions, and mixing time
parameter $t_{\rm mix}$. These bounds are the first regret bounds in the
general, non-episodic setting with an optimal dependence on all given
parameters. They could only be improved by using an alternative mixing time
parameter.
Defense Against Adversarial Attacks with Saak Transform
Authors: Sibo Song, Yueru Chen, Ngai-Man Cheung, C. -C. Jay Kuo
Date: 2018-08-06 00:00:00
Tasks: Adversarial Defense

Deep neural networks (DNNs) are known to be vulnerable to adversarial
perturbations, which imposes a serious threat to DNN-based decision systems. In
this paper, we propose to apply the lossy Saak transform to adversarially
perturbed images as a preprocessing tool to defend against adversarial attacks.
Saak transform is a recently-proposed state-of-the-art for computing the
spatial-spectral representations of input images. Empirically, we observe that
outputs of the Saak transform are very discriminative in differentiating
adversarial examples from clean ones. Therefore, we propose a Saak transform
based preprocessing method with three steps: 1) transforming an input image to
a joint spatial-spectral representation via the forward Saak transform, 2)
apply filtering to its high-frequency components, and, 3) reconstructing the
image via the inverse Saak transform. The processed image is found to be robust
against adversarial perturbations. We conduct extensive experiments to
investigate various settings of the Saak transform and filtering functions.
Without harming the decision performance on clean images, our method
outperforms state-of-the-art adversarial defense methods by a substantial
margin on both the CIFAR-10 and ImageNet datasets. Importantly, our results
suggest that adversarial perturbations can be effectively and efficiently
defended using state-of-the-art frequency analysis.