pwc-archive/papers-with-abstracts huggingface dataset:
A Survey on Graph Kernels
Authors: Nils M. Kriege, Fredrik D. Johansson, Christopher Morris
Date: 2019-03-28 00:00:00
Tasks: Classification, General Classification, Graph Classification, Survey

Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice. In an extensive experimental evaluation, we study the classification accuracy of a large suite of graph kernels on established benchmarks as well as new datasets. We compare the performance of popular kernels with several baseline methods and study the effect of applying a Gaussian RBF kernel to the metric induced by a graph kernel. In doing so, we find that simple baselines become competitive after this transformation on some datasets. Moreover, we study the extent to which existing graph kernels agree in their predictions (and prediction errors) and obtain a data-driven categorization of kernels as result. Finally, based on our experimental results, we derive a practitioner's guide to kernel-based graph classification.
Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation
Authors: Xueying Chen, Rong Zhang, Pingkun Yan
Date: 2019-03-28 00:00:00
Tasks: Decoder, Image Segmentation, Lesion Segmentation, Medical Image Analysis, Segmentation, Semantic Segmentation, Tumor Segmentation

Liver lesion segmentation is a difficult yet critical task for medical image
analysis. Recently, deep learning based image segmentation methods have
achieved promising performance, which can be divided into three categories: 2D,
2.5D and 3D, based on the dimensionality of the models. However, 2.5D and 3D
methods can have very high complexity and 2D methods may not perform
satisfactorily. To obtain competitive performance with low complexity, in this
paper, we propose a Feature-fusion Encoder-Decoder Network (FED-Net) based 2D
segmentation model to tackle the challenging problem of liver lesion
segmentation from CT images. Our feature fusion method is based on the
attention mechanism, which fuses high-level features carrying semantic
information with low-level features having image details. Additionally, to
compensate for the information loss during the upsampling process, a dense
upsampling convolution and a residual convolutional structure are proposed. We
tested our method on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS)
Challenge and achieved competitive results compared with other state-of-the-art
methods.
Skip prediction using boosting trees based on acoustic features of tracks in sessions
Authors: Andrés Ferraro, Dmitry Bogdanov, Xavier Serra
Date: 2019-03-28 00:00:00
Tasks: Sequential skip prediction

The Spotify Sequential Skip Prediction Challenge focuses on predicting if a
track in a session will be skipped by the user or not. In this paper, we
describe our approach to this problem and the final system that was submitted
to the challenge by our team from the Music Technology Group (MTG) under the
name "aferraro". This system consists in combining the predictions of multiple
boosting trees models trained with features extracted from the sessions and the
tracks. The proposed approach achieves good overall performance (MAA of 0.554),
with our model ranked 14th out of more than 600 submissions in the final
leaderboard.
FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation
Authors: Huikai Wu, Junge Zhang, Kaiqi Huang, Kongming Liang, Yizhou Yu
Date: 2019-03-28 00:00:00
Tasks: Semantic Segmentation

Modern approaches for semantic segmentation usually employ dilated
convolutions in the backbone to extract high-resolution feature maps, which
brings heavy computation complexity and memory footprint. To replace the time
and memory consuming dilated convolutions, we propose a novel joint upsampling
module named Joint Pyramid Upsampling (JPU) by formulating the task of
extracting high-resolution feature maps into a joint upsampling problem. With
the proposed JPU, our method reduces the computation complexity by more than
three times without performance loss. Experiments show that JPU is superior to
other upsampling modules, which can be plugged into many existing approaches to
reduce computation complexity and improve performance. By replacing dilated
convolutions with the proposed JPU module, our method achieves the
state-of-the-art performance in Pascal Context dataset (mIoU of 53.13%) and
ADE20K dataset (final score of 0.5584) while running 3 times faster.
Pyramid Mask Text Detector
Authors: Jingchao Liu, Xuebo Liu, Jie Sheng, Ding Liang, Xin Li, Qingjie Liu
Date: 2019-03-28 00:00:00
Tasks: Clustering, Instance Segmentation, Scene Text Detection, Scene Text Recognition, Semantic Segmentation, Text Detection

Scene text detection, an essential step of scene text recognition system, is
to locate text instances in natural scene images automatically. Some recent
attempts benefiting from Mask R-CNN formulate scene text detection task as an
instance segmentation problem and achieve remarkable performance. In this
paper, we present a new Mask R-CNN based framework named Pyramid Mask Text
Detector (PMTD) to handle the scene text detection. Instead of binary text mask
generated by the existing Mask R-CNN based methods, our PMTD performs
pixel-level regression under the guidance of location-aware supervision,
yielding a more informative soft text mask for each text instance. As for the
generation of text boxes, PMTD reinterprets the obtained 2D soft mask into 3D
space and introduces a novel plane clustering algorithm to derive the optimal
text box on the basis of 3D shape. Experiments on standard datasets demonstrate
that the proposed PMTD brings consistent and noticeable gain and clearly
outperforms state-of-the-art methods. Specifically, it achieves an F-measure of
80.13% on ICDAR 2017 MLT dataset.
Step Change Improvement in ADMET Prediction with PotentialNet Deep Featurization
Authors: Evan N. Feinberg, Robert Sheridan, Elizabeth Joshi, Vijay S. Pande, Alan C. Cheng
Date: 2019-03-28 00:00:00
Tasks: molecular representation

The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET)
properties of drug candidates are estimated to account for up to 50% of all
clinical trial failures. Predicting ADMET properties has therefore been of
great interest to the cheminformatics and medicinal chemistry communities in
recent decades. Traditional cheminformatics approaches, whether the learner is
a random forest or a deep neural network, leverage fixed fingerprint feature
representations of molecules. In contrast, in this paper, we learn the features
most relevant to each chemical task at hand by representing each molecule
explicitly as a graph, where each node is an atom and each edge is a bond. By
applying graph convolutions to this explicit molecular representation, we
achieve, to our knowledge, unprecedented accuracy in prediction of ADMET
properties. By challenging our methodology with rigorous cross-validation
procedures and prospective analyses, we show that deep featurization better
enables molecular predictors to not only interpolate but also extrapolate to
new regions of chemical space.
Cherenkov Detectors Fast Simulation Using Neural Networks
Authors: Denis Derkach, Nikita Kazeev, Fedor Ratnikov, Andrey Ustyuzhanin, Alexandra Volokhova
Date: 2019-03-28 00:00:00

We propose a way to simulate Cherenkov detector response using a generative
adversarial neural network to bypass low-level details. This network is trained
to reproduce high level features of the simulated detector events based on
input observables of incident particles. This allows the dramatic increase of
simulation speed. We demonstrate that this approach provides simulation
precision which is consistent with the baseline and discuss possible
implications of these results.
A Fast Free-viewpoint Video Synthesis Algorithm for Sports Scenes
Authors: Jun Chen, Ryosuke Watanabe, Keisuke Nonaka, Tomoaki Konno, Hiroshi Sankoh, Sei Naito
Date: 2019-03-28 00:00:00

In this paper, we report on a parallel freeviewpoint video synthesis algorithm that can efficiently reconstruct a high-quality 3D scene representation of sports scenes. The proposed method focuses on a scene that is captured by multiple synchronized cameras featuring wide-baselines. The following strategies are introduced to accelerate the production of a free-viewpoint video taking the improvement of visual quality into account: (1) a sparse point cloud is reconstructed using a volumetric visual hull approach, and an exact 3D ROI is found for each object using an efficient connected components labeling algorithm. Next, the reconstruction of a dense point cloud is accelerated by implementing visual hull only in the ROIs; (2) an accurate polyhedral surface mesh is built by estimating the exact intersections between grid cells and the visual hull; (3) the appearance of the reconstructed presentation is reproduced in a view-dependent manner that respectively renders the non-occluded and occluded region with the nearest camera and its neighboring cameras. The production for volleyball and judo sequences demonstrates the effectiveness of our method in terms of both execution time and visual quality.
A dataset for resolving referring expressions in spoken dialogue via contextual query rewrites (CQR)
Authors: Michael Regan, Pushpendre Rastogi, Arpit Gupta, Lambert Mathias
Date: 2019-03-28 00:00:00
Tasks: Spoken Dialogue Systems, Spoken Language Understanding

We present Contextual Query Rewrite (CQR) a dataset for multi-domain
task-oriented spoken dialogue systems that is an extension of the Stanford
dialog corpus (Eric et al., 2017a). While previous approaches have addressed
the issue of diverse schemas by learning candidate transformations (Naik et
al., 2018), we instead model the reference resolution task as a user query
reformulation task, where the dialog state is serialized into a natural
language query that can be executed by the downstream spoken language
understanding system. In this paper, we describe our methodology for creating
the query reformulation extension to the dialog corpus, and present an initial
set of experiments to establish a baseline for the CQR task. We have released
the corpus to the public [1] to support further research in this area.
Wasserstein Dependency Measure for Representation Learning
Authors: Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron van den Oord, Sergey Levine, Pierre Sermanet
Date: 2019-03-28 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Object Recognition, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning, speech-recognition, Speech Recognition, Video Understanding

Mutual information maximization has emerged as a powerful learning objective
for unsupervised representation learning obtaining state-of-the-art performance
in applications such as object recognition, speech recognition, and
reinforcement learning. However, such approaches are fundamentally limited
since a tight lower bound of mutual information requires sample size
exponential in the mutual information. This limits the applicability of these
approaches for prediction tasks with high mutual information, such as in video
understanding or reinforcement learning. In these settings, such techniques are
prone to overfit, both in theory and in practice, and capture only a few of the
relevant factors of variation. This leads to incomplete representations that
are not optimal for downstream tasks. In this work, we empirically demonstrate
that mutual information-based representation learning approaches do fail to
learn complete representations on a number of designed and real-world tasks. To
mitigate these problems we introduce the Wasserstein dependency measure, which
learns more complete representations by using the Wasserstein distance instead
of the KL divergence in the mutual information estimator. We show that a
practical approximation to this theoretically motivated solution, constructed
using Lipschitz constraint techniques from the GAN literature, achieves
substantially improved results on tasks where incomplete representations are a
major challenge.
BubbleNets: Learning to Select the Guidance Frame in Video Object Segmentation by Deep Sorting Frames
Authors: Brent A. Griffin, Jason J. Corso
Date: 2019-03-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

Semi-supervised video object segmentation has made significant progress on real and challenging videos in recent years. The current paradigm for segmentation methods and benchmark datasets is to segment objects in video provided a single annotation in the first frame. However, we find that segmentation performance across the entire video varies dramatically when selecting an alternative frame for annotation. This paper address the problem of learning to suggest the single best frame across the video for user annotation-this is, in fact, never the first frame of video. We achieve this by introducing BubbleNets, a novel deep sorting network that learns to select frames using a performance-based loss function that enables the conversion of expansive amounts of training examples from already existing datasets. Using BubbleNets, we are able to achieve an 11% relative improvement in segmentation performance on the DAVIS benchmark without any changes to the underlying method of segmentation.
What you get is what you see: Decomposing Epistemic Planning using Functional STRIPS
Authors: Guang Hu, Tim Miller, Nir Lipovetzky
Date: 2019-03-28 00:00:00

Epistemic planning --- planning with knowledge and belief --- is essential in
many multi-agent and human-agent interaction domains. Most state-of-the-art
epistemic planners solve this problem by compiling to propositional classical
planning, for example, generating all possible knowledge atoms, or compiling
epistemic formula to normal forms. However, these methods become
computationally infeasible as problems grow. In this paper, we decompose
epistemic planning by delegating reasoning about epistemic formula to an
external solver. We do this by modelling the problem using \emph{functional
STRIPS}, which is more expressive than standard STRIPS and supports the use of
external, black-box functions within action models. Exploiting recent work that
demonstrates the relationship between what an agent `sees' and what it knows,
we allow modellers to provide new implementations of externals functions. These
define what agents see in their environment, allowing new epistemic logics to
be defined without changing the planner. As a result, it increases the
capability and flexibility of the epistemic model itself, and avoids the
exponential pre-compilation step. We ran evaluations on well-known epistemic
planning benchmarks to compare with an existing state-of-the-art planner, and
on new scenarios based on different external functions. The results show that
our planner scales significantly better than the state-of-the-art planner
against which we compared, and can express problems more succinctly.
Atrial Fibrillation Detection Using Deep Features and Convolutional Networks
Authors: Sara Ross-Howe, H. R. Tizhoosh
Date: 2019-03-28 00:00:00
Tasks: Arrhythmia Detection, Atrial Fibrillation Detection, Electrocardiography (ECG), General Classification

Atrial fibrillation is a cardiac arrhythmia that affects an estimated 33.5
million people globally and is the potential cause of 1 in 3 strokes in people
over the age of 60. Detection and diagnosis of atrial fibrillation (AFIB) is
done noninvasively in the clinical environment through the evaluation of
electrocardiograms (ECGs). Early research into automated methods for the
detection of AFIB in ECG signals focused on traditional bio-medical signal
analysis to extract important features for use in statistical classification
models. Artificial intelligence models have more recently been used that employ
convolutional and/or recurrent network architectures. In this work, significant
time and frequency domain characteristics of the ECG signal are extracted by
applying the short-time Fourier trans-form and then visually representing the
information in a spectrogram. Two different classification approaches were
investigated that utilized deep features in the spectrograms construct-ed from
ECG segments. The first approach used a pretrained DenseNet model to extract
features that were then classified using Support Vector Machines, and the
second approach used the spectrograms as direct input into a convolutional
network. Both approaches were evaluated against the MIT-BIH AFIB dataset, where
the convolutional network approach achieved a classification accuracy of
93.16%. While these results do not surpass established automated atrial
fibrillation detection methods, they are promising and warrant further
investigation given they did not require any noise prefiltering, hand-crafted
features, nor a reliance on beat detection.
How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?
Authors: Quan Vuong, Sharad Vikram, Hao Su, Sicun Gao, Henrik I. Christensen
Date: 2019-03-28 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Recently, reinforcement learning (RL) algorithms have demonstrated remarkable
success in learning complicated behaviors from minimally processed input.
However, most of this success is limited to simulation. While there are
promising successes in applying RL algorithms directly on real systems, their
performance on more complex systems remains bottle-necked by the relative data
inefficiency of RL algorithms. Domain randomization is a promising direction of
research that has demonstrated impressive results using RL algorithms to
control real robots. At a high level, domain randomization works by training a
policy on a distribution of environmental conditions in simulation. If the
environments are diverse enough, then the policy trained on this distribution
will plausibly generalize to the real world. A human-specified design choice in
domain randomization is the form and parameters of the distribution of
simulated environments. It is unclear how to the best pick the form and
parameters of this distribution and prior work uses hand-tuned distributions.
This extended abstract demonstrates that the choice of the distribution plays a
major role in the performance of the trained policies in the real world and
that the parameter of this distribution can be optimized to maximize the
performance of the trained policies in the real world
A Large-Scale Multi-Length Headline Corpus for Analyzing Length-Constrained Headline Generation Model Evaluation
Authors: Yuta Hitomi, Yuya Taguchi, Hideaki Tamori, Ko Kikuta, Jiro Nishitoba, Naoaki Okazaki, Kentaro Inui, Manabu Okumura
Date: 2019-03-28 00:00:00
Proceeding: WS 2019 10
Tasks: Articles, Headline Generation

Browsing news articles on multiple devices is now possible. The lengths of news article headlines have precise upper bounds, dictated by the size of the display of the relevant device or interface. Therefore, controlling the length of headlines is essential when applying the task of headline generation to news production. However, because there is no corpus of headlines of multiple lengths for a given article, previous research on controlling output length in headline generation has not discussed whether the system outputs could be adequately evaluated without multiple references of different lengths. In this paper, we introduce two corpora, which are Japanese News Corpus (JNC) and JApanese MUlti-Length Headline Corpus (JAMUL), to confirm the validity of previous evaluation settings. The JNC provides common supervision data for headline generation. The JAMUL is a large-scale evaluation dataset for headlines of three different lengths composed by professional editors. We report new findings on these corpora; for example, although the longest length reference summary can appropriately evaluate the existing methods controlling output length, this evaluation setting has several problems.
An Improved Approach for Semantic Graph Composition with CCG
Authors: Austin Blodgett, Nathan Schneider
Date: 2019-03-28 00:00:00
Proceeding: WS 2019 5
Tasks: Abstract Meaning Representation, AMR Parsing, Relation

This paper builds on previous work using Combinatory Categorial Grammar (CCG)
to derive a transparent syntax-semantics interface for Abstract Meaning
Representation (AMR) parsing. We define new semantics for the CCG combinators
that is better suited to deriving AMR graphs. In particular, we define
relation-wise alternatives for the application and composition combinators:
these require that the two constituents being combined overlap in one AMR
relation. We also provide a new semantics for type raising, which is necessary
for certain constructions. Using these mechanisms, we suggest an analysis of
eventive nouns, which present a challenge for deriving AMR graphs. Our
theoretical analysis will facilitate future work on robust and transparent AMR
parsing using CCG.
Evaluating Sentence-Level Relevance Feedback for High-Recall Information Retrieval
Authors: Haotian Zhang, Gordon V. Cormack, Maura R. Grossman, Mark D. Smucker
Date: 2018-03-23 00:00:00
Tasks: Active Learning, Information Retrieval, Retrieval, Sentence, Vocal Bursts Intensity Prediction

This study uses a novel simulation framework to evaluate whether the time and
effort necessary to achieve high recall using active learning is reduced by
presenting the reviewer with isolated sentences, as opposed to full documents,
for relevance feedback. Under the weak assumption that more time and effort is
required to review an entire document than a single sentence, simulation
results indicate that the use of isolated sentences for relevance feedback can
yield comparable accuracy and higher efficiency, relative to the
state-of-the-art Baseline Model Implementation (BMI) of the AutoTAR Continuous
Active Learning ("CAL") method employed in the TREC 2015 and 2016 Total Recall
Track.
ThunderNet: Towards Real-time Generic Object Detection
Authors: Zheng Qin, Zeming Li, Zhaoning Zhang, Yiping Bao, Gang Yu, Yuxing Peng, Jian Sun
Date: 2019-03-28 00:00:00
Tasks: Object, object-detection, Object Detection

Real-time generic object detection on mobile platforms is a crucial but challenging computer vision task. However, previous CNN-based detectors suffer from enormous computational cost, which hinders them from real-time inference in computation-constrained scenarios. In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight two-stage detector named ThunderNet. In the backbone part, we analyze the drawbacks in previous lightweight backbones and present a lightweight backbone designed for object detection. In the detection part, we exploit an extremely efficient RPN and detection head design. To generate more discriminative feature representation, we design two efficient architecture blocks, Context Enhancement Module and Spatial Attention Module. At last, we investigate the balance between the input resolution, the backbone, and the detection head. Compared with lightweight one-stage detectors, ThunderNet achieves superior performance with only 40% of the computational cost on PASCAL VOC and COCO benchmarks. Without bells and whistles, our model runs at 24.1 fps on an ARM-based device. To the best of our knowledge, this is the first real-time detector reported on ARM platforms. Our code and models are available at \url{https://github.com/qinzheng93/ThunderNet}.