pwc-archive/papers-with-abstracts huggingface dataset:
Labeler-hot Detection of EEG Epileptic Transients
Authors: Lukasz Czekaj, Wojciech Ziembla, Pawel Jezierski, Pawel Swiniarski, Anna Kolodziejak, Pawel Ogniewski, Pawel Niedbalski, Anna Jezierska, Daniel Wesierski
Date: 2019-03-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Event Detection

Preventing early progression of epilepsy and so the severity of seizures requires an effective diagnosis. Epileptic transients indicate the ability to develop seizures but humans overlook such brief events in an electroencephalogram (EEG) what compromises patient treatment. Traditionally, training of the EEG event detection algorithms has relied on ground truth labels, obtained from the consensus of the majority of labelers. In this work, we go beyond labeler consensus on EEG data. Our event descriptor integrates EEG signal features with one-hot encoded labeler category that is a key to improved generalization performance. Notably, boosted decision trees take advantage of singly-labeled but more varied training sets. Our quantitative experiments show the proposed labeler-hot epileptic event detector consistently outperforms a consensus-trained detector and maintains confidence bounds of the detection. The results on our infant EEG recordings suggest datasets can gain higher event variety faster and thus better performance by shifting available human effort from consensus-oriented to separate labeling when labels include both, the event and the labeler category.
Scaling in Words on Twitter
Authors: Eszter Bokányi, Dániel Kondor, Gábor Vattay
Date: 2019-03-11 00:00:00

Scaling properties of language are a useful tool for understanding generative
processes in texts. We investigate the scaling relations in citywise Twitter
corpora coming from the Metropolitan and Micropolitan Statistical Areas of the
United States. We observe a slightly superlinear urban scaling with the city
population for the total volume of the tweets and words created in a city. We
then find that a certain core vocabulary follows the scaling relationship of
that of the bulk text, but most words are sensitive to city size, exhibiting a
super- or a sublinear urban scaling. For both regimes we can offer a plausible
explanation based on the meaning of the words. We also show that the parameters
for Zipf's law and Heaps law differ on Twitter from that of other texts, and
that the exponent of Zipf's law changes with city size.
Deep Recurrent Q-Learning vs Deep Q-Learning on a simple Partially Observable Markov Decision Process with Minecraft
Authors: Clément Romac, Vincent Béraud
Date: 2019-03-11 00:00:00
Tasks: Minecraft, Q-Learning

Deep Q-Learning has been successfully applied to a wide variety of tasks in
the past several years. However, the architecture of the vanilla Deep Q-Network
is not suited to deal with partially observable environments such as 3D video
games. For this, recurrent layers have been added to the Deep Q-Network in
order to allow it to handle past dependencies. We here use Minecraft for its
customization advantages and design two very simple missions that can be frames
as Partially Observable Markov Decision Process. We compare on these missions
the Deep Q-Network and the Deep Recurrent Q-Network in order to see if the
latter, which is trickier and longer to train, is always the best architecture
when the agent has to deal with partial observability.
Reachability and Coverage Planning for Connected Agents: Extended Version
Authors: Tristan Charrier, Arthur Queffelec, Ocan Sankur, François Schwarzentruber
Date: 2019-03-11 00:00:00

Motivated by the increasing appeal of robots in information-gathering
missions, we study multi-agent path planning problems in which the agents must
remain interconnected. We model an area by a topological graph specifying the
movement and the connectivity constraints of the agents. We study the
theoretical complexity of the reachability and the coverage problems of a fleet
of connected agents on various classes of topological graphs. We establish the
complexity of these problems on known classes, and introduce a new class called
sight-moveable graphs which admit efficient algorithms.
Gradient Descent based Optimization Algorithms for Deep Learning Models Training
Authors: Jiawei Zhang
Date: 2019-03-11 00:00:00
Tasks: Deep Learning

In this paper, we aim at providing an introduction to the gradient descent
based optimization algorithms for learning deep neural network models. Deep
learning models involving multiple nonlinear projection layers are very
challenging to train. Nowadays, most of the deep learning model training still
relies on the back propagation algorithm actually. In back propagation, the
model variables will be updated iteratively until convergence with gradient
descent based optimization algorithms. Besides the conventional vanilla
gradient descent algorithm, many gradient descent variants have also been
proposed in recent years to improve the learning performance, including
Momentum, Adagrad, Adam, Gadam, etc., which will all be introduced in this
paper respectively.
A Unified Formulation for Visual Odometry
Authors: Georges Younes, Daniel Asmar, John Zelek
Date: 2019-03-11 00:00:00
Tasks: Depth Estimation, Visual Odometry

Monocular Odometry systems can be broadly categorized as being either Direct,
Indirect, or a hybrid of both. While Indirect systems process an alternative
image representation to compute geometric residuals, Direct methods process the
image pixels directly to generate photometric residuals. Both paradigms have
distinct but often complementary properties. This paper presents a Unified
Formulation for Visual Odometry, referred to as UFVO, with the following key
contributions: (1) a tight coupling of photometric (Direct) and geometric
(Indirect) measurements using a joint multi-objective optimization, (2) the use
of a utility function as a decision maker that incorporates prior knowledge on
both paradigms, (3) descriptor sharing, where a feature can have more than one
type of descriptor and its different descriptors are used for tracking and
mapping, (4) the depth estimation of both corner features and pixel features
within the same map using an inverse depth parametrization, and (5) a corner
and pixel selection strategy that extracts both types of information, while
promoting a uniform distribution over the image domain. Experiments show that
our proposed system can handle large inter-frame motions, inherits the
sub-pixel accuracy of direct methods, can run efficiently in real-time, can
generate an Indirect map representation at a marginal computational cost when
compared to traditional Indirect systems, all while outperforming state of the
art in Direct, Indirect and hybrid systems.
Manifold Mixup improves text recognition with CTC loss
Authors: Bastien Moysset, Ronaldo Messina
Date: 2019-03-11 00:00:00
Tasks: Data Augmentation, General Classification, Handwritten Text Recognition, parameter estimation

Modern handwritten text recognition techniques employ deep recurrent neural
networks. The use of these techniques is especially efficient when a large
amount of annotated data is available for parameter estimation. Data
augmentation can be used to enhance the performance of the systems when data is
scarce. Manifold Mixup is a modern method of data augmentation that meld two
images or the feature maps corresponding to these images and the targets are
fused accordingly. We propose to apply the Manifold Mixup to text recognition
while adapting it to work with a Connectionist Temporal Classification cost. We
show that Manifold Mixup improves text recognition results on various languages
and datasets.
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
Authors: Anees Kazi, Shayan shekarforoush, S. Arvind krishna, Hendrik Burwinkel, Gerome Vivar, Karsten Kortuem, Seyed-Ahmad Ahmadi, Shadi Albarqouni, Nassir Navab
Date: 2019-03-11 00:00:00
Tasks: Deep Learning, Disease Prediction, Matrix Completion, Prediction

Geometric deep learning provides a principled and versatile manner for the
integration of imaging and non-imaging modalities in the medical domain. Graph
Convolutional Networks (GCNs) in particular have been explored on a wide
variety of problems such as disease prediction, segmentation, and matrix
completion by leveraging large, multimodal datasets. In this paper, we
introduce a new spectral domain architecture for deep learning on graphs for
disease prediction. The novelty lies in defining geometric 'inception modules'
which are capable of capturing intra- and inter-graph structural heterogeneity
during convolutions. We design filters with different kernel sizes to build our
architecture. We show our disease prediction results on two publicly available
datasets. Further, we provide insights on the behaviour of regular GCNs and our
proposed model under varying input scenarios on simulated data.
Instance- and Category-level 6D Object Pose Estimation
Authors: Caner Sahin, Guillermo Garcia-Hernando, Juil Sock, Tae-Kyun Kim
Date: 2019-03-11 00:00:00
Tasks: 6D Pose Estimation using RGB, Object, Pose Estimation, Scene Understanding

6D object pose estimation is an important task that determines the 3D
position and 3D rotation of an object in camera-centred coordinates. By
utilizing such a task, one can propose promising solutions for various problems
related to scene understanding, augmented reality, control and navigation of
robotics. Recent developments on visual depth sensors and low-cost availability
of depth data significantly facilitate object pose estimation. Using depth
information from RGB-D sensors, substantial progress has been made in the last
decade by the methods addressing the challenges such as viewpoint variability,
occlusion and clutter, and similar looking distractors. Particularly, with the
recent advent of convolutional neural networks, RGB-only based solutions have
been presented. However, improved results have only been reported for
recovering the pose of known instances, i.e., for the instance-level object
pose estimation tasks. More recently, state-of-the-art approaches target to
solve object pose estimation problem at the level of categories, recovering the
6D pose of unknown instances. To this end, they address the challenges of the
category-level tasks such as distribution shift among source and target
domains, high intra-class variations, and shape discrepancies between objects.
From interpretability to inference: an estimation framework for universal approximators
Authors: Andreas Joseph
Date: 2019-03-11 00:00:00

We present a novel framework for estimation and inference with the broad class of universal approximators. Estimation is based on the decomposition of model predictions into Shapley values. Inference relies on analyzing the bias and variance properties of individual Shapley components. We show that Shapley value estimation is asymptotically unbiased, and we introduce Shapley regressions as a tool to uncover the true data generating process from noisy data alone. The well-known case of the linear regression is the special case in our framework if the model is linear in parameters. We present theoretical, numerical, and empirical results for the estimation of heterogeneous treatment effects as our guiding example.
Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury
Authors: Samuel Remedios, Snehashis Roy, Justin Blaber, Camilo Bermudez, Vishwesh Nath, Mayur B. Patel, John A. Butman, Bennett A. Landman, Dzung L. Pham
Date: 2019-03-11 00:00:00
Tasks: Computed Tomography (CT)

Machine learning models are becoming commonplace in the domain of medical
imaging, and with these methods comes an ever-increasing need for more data.
However, to preserve patient anonymity it is frequently impractical or
prohibited to transfer protected health information (PHI) between institutions.
Additionally, due to the nature of some studies, there may not be a large
public dataset available on which to train models. To address this conundrum,
we analyze the efficacy of transferring the model itself in lieu of data
between different sites. By doing so we accomplish two goals: 1) the model
gains access to training on a larger dataset that it could not normally obtain
and 2) the model better generalizes, having trained on data from separate
locations. In this paper, we implement multi-site learning with disparate
datasets from the National Institutes of Health (NIH) and Vanderbilt University
Medical Center (VUMC) without compromising PHI. Three neural networks are
trained to convergence on a computed tomography (CT) brain hematoma
segmentation task: one only with NIH data,one only with VUMC data, and one
multi-site model alternating between NIH and VUMC data. Resultant lesion masks
with the multi-site model attain an average Dice similarity coefficient of 0.64
and the automatically segmented hematoma volumes correlate to those done
manually with a Pearson correlation coefficient of 0.87,corresponding to an 8%
and 5% improvement, respectively, over the single-site model counterparts.
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics
Authors: Denis Steckelmacher, Hélène Plisnier, Diederik M. Roijers, Ann Nowé
Date: 2019-03-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

Value-based reinforcement-learning algorithms provide state-of-the-art results in model-free discrete-action settings, and tend to outperform actor-critic algorithms. We argue that actor-critic algorithms are limited by their need for an on-policy critic. We propose Bootstrapped Dual Policy Iteration (BDPI), a novel model-free reinforcement-learning algorithm for continuous states and discrete actions, with an actor and several off-policy critics. Off-policy critics are compatible with experience replay, ensuring high sample-efficiency, without the need for off-policy corrections. The actor, by slowly imitating the average greedy policy of the critics, leads to high-quality and state-specific exploration, which we compare to Thompson sampling. Because the actor and critics are fully decoupled, BDPI is remarkably stable, and unusually robust to its hyper-parameters. BDPI is significantly more sample-efficient than Bootstrapped DQN, PPO, and ACKTR, on discrete, continuous and pixel-based tasks. Source code: https://github.com/vub-ai-lab/bdpi.
Accelerating Minibatch Stochastic Gradient Descent using Typicality Sampling
Authors: Xinyu Peng, Li Li, Fei-Yue Wang
Date: 2019-03-11 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL), speech-recognition, Speech Recognition, Stochastic Optimization

Machine learning, especially deep neural networks, has been rapidly developed
in fields including computer vision, speech recognition and reinforcement
learning. Although Mini-batch SGD is one of the most popular stochastic
optimization methods in training deep networks, it shows a slow convergence
rate due to the large noise in gradient approximation. In this paper, we
attempt to remedy this problem by building more efficient batch selection
method based on typicality sampling, which reduces the error of gradient
estimation in conventional Minibatch SGD. We analyze the convergence rate of
the resulting typical batch SGD algorithm and compare convergence properties
between Minibatch SGD and the algorithm. Experimental results demonstrate that
our batch selection scheme works well and more complex Minibatch SGD variants
can benefit from the proposed batch selection strategy.
A cross-center smoothness prior for variational Bayesian brain tissue segmentation
Authors: Wouter M. Kouw, Silas N. Ørting, Jens Petersen, Kim S. Pedersen, Marleen de Bruijne
Date: 2019-03-11 00:00:00

Suppose one is faced with the challenge of tissue segmentation in MR images,
without annotators at their center to provide labeled training data. One option
is to go to another medical center for a trained classifier. Sadly, tissue
classifiers do not generalize well across centers due to voxel intensity shifts
caused by center-specific acquisition protocols. However, certain aspects of
segmentations, such as spatial smoothness, remain relatively consistent and can
be learned separately. Here we present a smoothness prior that is fit to
segmentations produced at another medical center. This informative prior is
presented to an unsupervised Bayesian model. The model clusters the voxel
intensities, such that it produces segmentations that are similarly smooth to
those of the other medical center. In addition, the unsupervised Bayesian model
is extended to a semi-supervised variant, which needs no visual interpretation
of clusters into tissues.
Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning
Authors: Weipeng Huang, Xingyi Cheng, Kunlong Chen, Taifeng Wang, Wei Chu
Date: 2019-03-11 00:00:00
Tasks: Chinese Word Segmentation, Compiler Optimization, Computational Efficiency, Quantization, Segmentation

The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities. Multi-criteria Chinese word segmentation aims to capture various annotation criteria among datasets and leverage their common underlying knowledge. In this paper, we propose a domain adaptive segmenter to exploit diverse criteria of various datasets. Our model is based on Bidirectional Encoder Representations from Transformers (BERT), which is responsible for introducing open-domain knowledge. Private and shared projection layers are proposed to capture domain-specific knowledge and common knowledge, respectively. We also optimize computational efficiency via distillation, quantization, and compiler optimization. Experiments show that our segmenter outperforms the previous state of the art (SOTA) models on 10 CWS datasets with superior efficiency.
Automated Circuit Approximation Method Driven by Data Distribution
Authors: Zdenek Vasicek, Vojtech Mrazek, Lukas Sekanina
Date: 2019-03-11 00:00:00
Tasks: General Classification

We propose an application-tailored data-driven fully automated method for
functional approximation of combinational circuits. We demonstrate how an
application-level error metric such as the classification accuracy can be
translated to a component-level error metric needed for an efficient and fast
search in the space of approximate low-level components that are used in the
application. This is possible by employing a weighted mean error distance
(WMED) metric for steering the circuit approximation process which is conducted
by means of genetic programming. WMED introduces a set of weights (calculated
from the data distribution measured on a selected signal in a given
application) determining the importance of each input vector for the
approximation process. The method is evaluated using synthetic benchmarks and
application-specific approximate MAC (multiply-and-accumulate) units that are
designed to provide the best trade-offs between the classification accuracy and
power consumption of two image classifiers based on neural networks.
Graph Data on the Web: extend the pivot, don't reinvent the wheel
Authors: Fabien Gandon, Franck Michel, Olivier Corby, Michel Buffa, Andrea Tettamanzi, Catherine Faron Zucker, Elena Cabrio, Serena Villata
Date: 2019-03-11 00:00:00
Tasks: Management, Position

This article is a collective position paper from the Wimmics research team,
expressing our vision of how Web graph data technologies should evolve in the
future in order to ensure a high-level of interoperability between the many
types of applications that produce and consume graph data. Wimmics stands for
Web-Instrumented Man-Machine Interactions, Communities, and Semantics. We are a
joint research team between INRIA Sophia Antipolis-M{\'e}diterran{\'e}e and I3S
(CNRS and Universit{\'e} C{\^o}te d'Azur). Our challenge is to bridge formal
semantics and social semantics on the web. Our research areas are
graph-oriented knowledge representation, reasoning and operationalization to
model and support actors, actions and interactions in web-based epistemic
communities. The application of our research is supporting and fostering
interactions in online communities and management of their resources. In this
position paper, we emphasize the need to extend the semantic Web standard stack
to address and fulfill new graph data needs, as well as the importance of
remaining compatible with existing recommendations, in particular the RDF
stack, to avoid the painful duplication of models, languages, frameworks, etc.
The following sections group motivations for different directions of work and
collect reasons for the creation of a working group on RDF 2.0 and other
recommendations of the RDF family.
Investigation on Combining 3D Convolution of Image Data and Optical Flow to Generate Temporal Action Proposals
Authors: Patrick Schlosser, David Münch, Michael Arens
Date: 2019-03-11 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization, Temporal Action Proposal Generation

In this paper, several variants of two-stream architectures for temporal
action proposal generation in long, untrimmed videos are presented. Inspired by
the recent advances in the field of human action recognition utilizing 3D
convolutions in combination with two-stream networks and based on the
Single-Stream Temporal Action Proposals (SST) architecture, four different
two-stream architectures utilizing sequences of images on one stream and
sequences of images of optical flow on the other stream are subsequently
investigated. The four architectures fuse the two separate streams at different
depths in the model; for each of them, a broad range of parameters is
investigated systematically as well as an optimal parametrization is
empirically determined. The experiments on the THUMOS'14 dataset show that all
four two-stream architectures are able to outperform the original single-stream
SST and achieve state of the art results. Additional experiments revealed that
the improvements are not restricted to a single method of calculating optical
flow by exchanging the formerly used method of Brox with FlowNet2 and still
achieving improvements.
Fisher-Bures Adversary Graph Convolutional Networks
Authors: Ke Sun, Piotr Koniusz, Zhen Wang
Date: 2019-03-11 00:00:00
Tasks: Graph Neural Network, Node Classification

In a graph convolutional network, we assume that the graph $G$ is generated wrt some observation noise. During learning, we make small random perturbations $\Delta{}G$ of the graph and try to improve generalization. Based on quantum information geometry, $\Delta{}G$ can be characterized by the eigendecomposition of the graph Laplacian matrix. We try to minimize the loss wrt the perturbed $G+\Delta{G}$ while making $\Delta{G}$ to be effective in terms of the Fisher information of the neural network. Our proposed model can consistently improve graph convolutional networks on semi-supervised node classification tasks with reasonable computational overhead. We present three different geometries on the manifold of graphs: the intrinsic geometry measures the information theoretic dynamics of a graph; the extrinsic geometry characterizes how such dynamics can affect externally a graph neural network; the embedding geometry is for measuring node embeddings. These new analytical tools are useful in developing a good understanding of graph neural networks and fostering new techniques.