pwc-archive/papers-with-abstracts huggingface dataset:
A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing
Authors: Hang Yan, Xipeng Qiu, Xuanjing Huang
Date: 2019-04-09 00:00:00
Proceeding: TACL 2020 1
Tasks: Chinese Word Segmentation, Dependency Parsing, Feature Engineering, Language Modeling, Language Modelling, Segmentation

Chinese word segmentation and dependency parsing are two fundamental tasks for Chinese natural language processing. The dependency parsing is defined on word-level. Therefore word segmentation is the precondition of dependency parsing, which makes dependency parsing suffer from error propagation and unable to directly make use of the character-level pre-trained language model (such as BERT). In this paper, we propose a graph-based model to integrate Chinese word segmentation and dependency parsing. Different from previous transition-based joint models, our proposed model is more concise, which results in fewer efforts of feature engineering. Our graph-based joint model achieves better performance than previous joint models and state-of-the-art results in both Chinese word segmentation and dependency parsing. Besides, when BERT is combined, our model can substantially reduce the performance gap of dependency parsing between joint models and gold-segmented word-based models. Our code is publicly available at https://github.com/fastnlp/JointCwsParser.
End-to-End Learning-Based Ultrasound Reconstruction
Authors: Walter Simson, Rüdiger Göbl, Magdalini Paschali, Markus Krönke, Klemens Scheidhauer, Wolfgang Weber, Nassir Navab
Date: 2019-04-09 00:00:00
Tasks: Diagnostic, Image Reconstruction

Ultrasound imaging is caught between the quest for the highest image quality,
and the necessity for clinical usability. Our contribution is two-fold: First,
we propose a novel fully convolutional neural network for ultrasound
reconstruction. Second, a custom loss function tailored to the modality is
employed for end-to-end training of the network. We demonstrate that training a
network to map time-delayed raw data to a minimum variance ground truth offers
performance increases in a clinical environment. In doing so, a path is
explored towards improved clinically viable ultrasound reconstruction. The
proposed method displays both promising image reconstruction quality and
acquisition frequency when integrated for live ultrasound scanning. A clinical
evaluation is conducted to verify the diagnostic usefulness of the proposed
method in a clinical setting.
Fast Enhanced CT Metal Artifact Reduction using Data Domain Deep Learning
Authors: Muhammad Usman Ghani, W. Clem Karl
Date: 2019-04-09 00:00:00
Tasks: Computed Tomography (CT), Diagnostic, Image Reconstruction, Metal Artifact Reduction, Transfer Learning

Filtered back projection (FBP) is the most widely used method for image reconstruction in X-ray computed tomography (CT) scanners. The presence of hyper-dense materials in a scene, such as metals, can strongly attenuate X-rays, producing severe streaking artifacts in the reconstruction. These metal artifacts can greatly limit subsequent object delineation and information extraction from the images, restricting their diagnostic value. This problem is particularly acute in the security domain, where there is great heterogeneity in the objects that can appear in a scene, highly accurate decisions must be made quickly. The standard practical approaches to reducing metal artifacts in CT imagery are either simplistic non-adaptive interpolation-based projection data completion methods or direct image post-processing methods. These standard approaches have had limited success. Motivated primarily by security applications, we present a new deep-learning-based metal artifact reduction (MAR) approach that tackles the problem in the projection data domain. We treat the projection data corresponding to metal objects as missing data and train an adversarial deep network to complete the missing data in the projection domain. The subsequent complete projection data is then used with FBP to reconstruct image intended to be free of artifacts. This new approach results in an end-to-end MAR algorithm that is computationally efficient so practical and fits well into existing CT workflows allowing easy adoption in existing scanners. Training deep networks can be challenging, and another contribution of our work is to demonstrate that training data generated using an accurate X-ray simulation can be used to successfully train the deep network when combined with transfer learning using limited real data sets. We demonstrate the effectiveness and potential of our algorithm on simulated and real examples.
Multi-Target Embodied Question Answering
Authors: Licheng Yu, Xinlei Chen, Georgia Gkioxari, Mohit Bansal, Tamara L. Berg, Dhruv Batra
Date: 2019-04-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: Embodied Question Answering, Navigate, Question Answering, Visual Question Answering (VQA)

Embodied Question Answering (EQA) is a relatively new task where an agent is
asked to answer questions about its environment from egocentric perception. EQA
makes the fundamental assumption that every question, e.g., "what color is the
car?", has exactly one target ("car") being inquired about. This assumption
puts a direct limitation on the abilities of the agent. We present a
generalization of EQA - Multi-Target EQA (MT-EQA). Specifically, we study
questions that have multiple targets in them, such as "Is the dresser in the
bedroom bigger than the oven in the kitchen?", where the agent has to navigate
to multiple locations ("dresser in bedroom", "oven in kitchen") and perform
comparative reasoning ("dresser" bigger than "oven") before it can answer a
question. Such questions require the development of entirely new modules or
components in the agent. To address this, we propose a modular architecture
composed of a program generator, a controller, a navigator, and a VQA module.
The program generator converts the given question into sequential executable
sub-programs; the navigator guides the agent to multiple locations pertinent to
the navigation-related sub-programs; and the controller learns to select
relevant observations along its path. These observations are then fed to the
VQA module to predict the answer. We perform detailed analysis for each of the
model components and show that our joint model can outperform previous methods
and strong baselines by a significant margin.
On the approximation of the solution of partial differential equations by artificial neural networks trained by a multilevel Levenberg-Marquardt method
Authors: Henri Calandra, Serge Gratton, Elisa Riccietti, Xavier Vasseur
Date: 2019-04-09 00:00:00

This paper is concerned with the approximation of the solution of partial
differential equations by means of artificial neural networks. Here a
feedforward neural network is used to approximate the solution of the partial
differential equation. The learning problem is formulated as a least squares
problem, choosing the residual of the partial differential equation as a loss
function, whereas a multilevel Levenberg-Marquardt method is employed as a
training method. This setting allows us to get further insight into the
potential of multilevel methods. Indeed, when the least squares problem arises
from the training of artificial neural networks, the variables subject to
optimization are not related by any geometrical constraints and the standard
interpolation and restriction operators cannot be employed any longer. A
heuristic, inspired by algebraic multigrid methods, is then proposed to
construct the multilevel transfer operators. Numerical experiments show
encouraging results related to the efficiency of the new multilevel
optimization method for the training of artificial neural networks, compared to
the standard corresponding one-level procedure.
Evaluating Competence Measures for Dynamic Regressor Selection
Authors: Thiago J. M. Moura, George D. C. Cavalcanti, Luiz S. Oliveira
Date: 2019-04-09 00:00:00
Tasks: regression

Dynamic regressor selection (DRS) systems work by selecting the most
competent regressors from an ensemble to estimate the target value of a given
test pattern. This competence is usually quantified using the performance of
the regressors in local regions of the feature space around the test pattern.
However, choosing the best measure to calculate the level of competence
correctly is not straightforward. The literature of dynamic classifier
selection presents a wide variety of competence measures, which cannot be used
or adapted for DRS. In this paper, we review eight measures used with
regression problems, and adapt them to test the performance of the DRS
algorithms found in the literature. Such measures are extracted from a local
region of the feature space around the test pattern, called region of
competence, therefore competence measures.To better compare the competence
measures, we perform a set of comprehensive experiments of 15 regression
datasets. Three DRS systems were compared against individual regressor and
static systems that use the Mean and the Median to combine the outputs of the
regressors from the ensemble. The DRS systems were assessed varying the
competence measures. Our results show that DRS systems outperform individual
regressors and static systems but the choice of the competence measure is
problem-dependent.
CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion
Authors: Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka, Nobukatsu Hojo
Date: 2019-04-09 00:00:00
Tasks: Voice Conversion

Non-parallel voice conversion (VC) is a technique for learning the mapping
from source to target speech without relying on parallel data. This is an
important task, but it has been challenging due to the disadvantages of the
training conditions. Recently, CycleGAN-VC has provided a breakthrough and
performed comparably to a parallel VC method without relying on any extra data,
modules, or time alignment procedures. However, there is still a large gap
between the real target and converted speech, and bridging this gap remains a
challenge. To reduce this gap, we propose CycleGAN-VC2, which is an improved
version of CycleGAN-VC incorporating three new techniques: an improved
objective (two-step adversarial losses), improved generator (2-1-2D CNN), and
improved discriminator (PatchGAN). We evaluated our method on a non-parallel VC
task and analyzed the effect of each technique in detail. An objective
evaluation showed that these techniques help bring the converted feature
sequence closer to the target in terms of both global and local structures,
which we assess by using Mel-cepstral distortion and modulation spectra
distance, respectively. A subjective evaluation showed that CycleGAN-VC2
outperforms CycleGAN-VC in terms of naturalness and similarity for every
speaker pair, including intra-gender and inter-gender pairs.
Towards Analyzing Semantic Robustness of Deep Neural Networks
Authors: Abdullah Hamdi, Bernard Ghanem
Date: 2019-04-09 00:00:00
Tasks: Adversarial Attack, Autonomous Driving, Image Classification

Despite the impressive performance of Deep Neural Networks (DNNs) on various vision tasks, they still exhibit erroneous high sensitivity toward semantic primitives (e.g. object pose). We propose a theoretically grounded analysis for DNN robustness in the semantic space. We qualitatively analyze different DNNs' semantic robustness by visualizing the DNN global behavior as semantic maps and observe interesting behavior of some DNNs. Since generating these semantic maps does not scale well with the dimensionality of the semantic space, we develop a bottom-up approach to detect robust regions of DNNs. To achieve this, we formalize the problem of finding robust semantic regions of the network as optimizing integral bounds and we develop expressions for update directions of the region bounds. We use our developed formulations to quantitatively evaluate the semantic robustness of different popular network architectures. We show through extensive experimentation that several networks, while trained on the same dataset and enjoying comparable accuracy, do not necessarily perform similarly in semantic robustness. For example, InceptionV3 is more accurate despite being less semantically robust than ResNet50. We hope that this tool will serve as a milestone towards understanding the semantic robustness of DNNs.
Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving
Authors: Jiwoong Choi, Dayoung Chun, Hyun Kim, Hyuk-Jae Lee
Date: 2019-04-09 00:00:00
Proceeding: ICCV 2019 10
Tasks: Autonomous Driving, Autonomous Vehicles, object-detection, Object Detection

The use of object detection algorithms is becoming increasingly important in autonomous vehicles, and object detection at high accuracy and a fast inference speed is essential for safe autonomous driving. A false positive (FP) from a false localization during autonomous driving can lead to fatal accidents and hinder safe and efficient driving. Therefore, a detection algorithm that can cope with mislocalizations is required in autonomous driving applications. This paper proposes a method for improving the detection accuracy while supporting a real-time operation by modeling the bounding box (bbox) of YOLOv3, which is the most representative of one-stage detectors, with a Gaussian parameter and redesigning the loss function. In addition, this paper proposes a method for predicting the localization uncertainty that indicates the reliability of bbox. By using the predicted localization uncertainty during the detection process, the proposed schemes can significantly reduce the FP and increase the true positive (TP), thereby improving the accuracy. Compared to a conventional YOLOv3, the proposed algorithm, Gaussian YOLOv3, improves the mean average precision (mAP) by 3.09 and 3.5 on the KITTI and Berkeley deep drive (BDD) datasets, respectively. Nevertheless, the proposed algorithm is capable of real-time detection at faster than 42 frames per second (fps) and shows a higher accuracy than previous approaches with a similar fps. Therefore, the proposed algorithm is the most suitable for autonomous driving applications.
Automated Search for Configurations of Deep Neural Network Architectures
Authors: Salah Ghamizi, Maxime Cordy, Mike Papadakis, Yves Le Traon
Date: 2019-04-09 00:00:00
Tasks: image-classification, Image Classification, valid

Deep Neural Networks (DNNs) are intensively used to solve a wide variety of
complex problems. Although powerful, such systems require manual configuration
and tuning. To this end, we view DNNs as configurable systems and propose an
end-to-end framework that allows the configuration, evaluation and automated
search for DNN architectures. Therefore, our contribution is threefold. First,
we model the variability of DNN architectures with a Feature Model (FM) that
generalizes over existing architectures. Each valid configuration of the FM
corresponds to a valid DNN model that can be built and trained. Second, we
implement, on top of Tensorflow, an automated procedure to deploy, train and
evaluate the performance of a configured model. Third, we propose a method to
search for configurations and demonstrate that it leads to good DNN models. We
evaluate our method by applying it on image classification tasks (MNIST,
CIFAR-10) and show that, with limited amount of computation and training, our
method can identify high-performing architectures (with high accuracy). We also
demonstrate that we outperform existing state-of-the-art architectures
handcrafted by ML researchers. Our FM and framework have been released %and are
publicly available to support replication and future research.
Hyper-Parameter Tuning for the (1+(λ,λ)) GA
Authors: Nguyen Dang, Carola Doerr
Date: 2019-04-09 00:00:00

It is known that the $(1+(\lambda,\lambda))$~Genetic Algorithm (GA) with
self-adjusting parameter choices achieves a linear expected optimization time
on OneMax if its hyper-parameters are suitably chosen. However, it is not very
well understood how the hyper-parameter settings influences the overall
performance of the $(1+(\lambda,\lambda))$~GA. Analyzing such multi-dimensional
dependencies precisely is at the edge of what running time analysis can offer.
To make a step forward on this question, we present an in-depth empirical study
of the self-adjusting $(1+(\lambda,\lambda))$~GA and its hyper-parameters. We
show, among many other results, that a 15\% reduction of the average running
time is possible by a slightly different setup, which allows non-identical
offspring population sizes of mutation and crossover phase, and more
flexibility in the choice of mutation rate and crossover bias --a
generalization which may be of independent interest. We also show indication
that the parametrization of mutation rate and crossover bias derived by
theoretical means for the static variant of the $(1+(\lambda,\lambda))$~GA
extends to the non-static case.
Rain O'er Me: Synthesizing real rain to derain with data distillation
Authors: Huangxing Lin, Yanlong Li, Xinghao Ding, Weihong Zeng, Yue Huang, John Paisley
Date: 2019-04-09 00:00:00
Tasks: Rain Removal

We present a supervised technique for learning to remove rain from images
without using synthetic rain software. The method is based on a two-stage data
distillation approach: 1) A rainy image is first paired with a coarsely
derained version using on a simple filtering technique ("rain-to-clean"). 2)
Then a clean image is randomly matched with the rainy soft-labeled pair.
Through a shared deep neural network, the rain that is removed from the first
image is then added to the clean image to generate a second pair
("clean-to-rain"). The neural network simultaneously learns to map both images
such that high resolution structure in the clean images can inform the
deraining of the rainy images. Demonstrations show that this approach can
address those visual characteristics of rain not easily synthesized by software
in the usual way.
Simultaneous Contact, Gait and Motion Planning for Robust Multi-Legged Locomotion via Mixed-Integer Convex Optimization
Authors: Bernardo Aceituno-Cabezas, Carlos Mastalli, Hongkai Dai, Michele Focchi, Andreea Radulescu, Darwin G. Caldwell, Jose Cappelletto, Juan C. Grieco, Gerardo Fernandez-Lopez, Claudio Semini
Date: 2019-04-09 00:00:00
Tasks: Friction, Motion Planning

Traditional motion planning approaches for multi-legged locomotion divide the
problem into several stages, such as contact search and trajectory generation.
However, reasoning about contacts and motions simultaneously is crucial for the
generation of complex whole-body behaviors. Currently, coupling theses problems
has required either the assumption of a fixed gait sequence and flat terrain
condition, or non-convex optimization with intractable computation time. In
this paper, we propose a mixed-integer convex formulation to plan
simultaneously contact locations, gait transitions and motion, in a
computationally efficient fashion. In contrast to previous works, our approach
is not limited to flat terrain nor to a pre-specified gait sequence. Instead,
we incorporate the friction cone stability margin, approximate the robot's
torque limits, and plan the gait using mixed-integer convex constraints. We
experimentally validated our approach on the HyQ robot by traversing different
challenging terrains, where non-convexity and flat terrain assumptions might
lead to sub-optimal or unstable plans. Our method increases the motion
generality while keeping a low computation time.
A Concept-Value Network as a Brain Model
Authors: Kieran Greer
Date: 2019-04-09 00:00:00
Tasks: model

This paper suggests a statistical framework for describing the relations between the physical and conceptual entities of a brain-like model. Features and concept instances are put into context, where the paper suggests that features may be the electrical wiring, although chemical connections are also possible. With this idea, the actual length of the connection is important, because it is related to firing rates and neuron synchronization, but the signal type is less important. The paper then suggests that concepts are neuron groups that link feature sets and concept instances are determined by chemical signals from those groups. Therefore, features become the static horizontal framework of the neural system and concepts are vertically interconnected combinations of these. With regards to functionality, the neuron is then considered to be functional and the more horizontal memory structures can even be glial. This would also suggest that features can be distributed entities and not concentrated to a single area. Another aspect could be signal 'breaks' that compartmentalise a pattern and may help with neural binding.
3DPeople: Modeling the Geometry of Dressed Humans
Authors: Albert Pumarola, Jordi Sanchez, Gary P. T. Choi, Alberto Sanfeliu, Francesc Moreno-Noguer
Date: 2019-04-09 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Human Shape Estimation, Optical Flow Estimation

Recent advances in 3D human shape estimation build upon parametric
representations that model very well the shape of the naked body, but are not
appropriate to represent the clothing geometry. In this paper, we present an
approach to model dressed humans and predict their geometry from single images.
We contribute in three fundamental aspects of the problem, namely, a new
dataset, a novel shape parameterization algorithm and an end-to-end deep
generative network for predicting shape.
  First, we present 3DPeople, a large-scale synthetic dataset with 2.5 Million
photo-realistic images of 80 subjects performing 70 activities and wearing
diverse outfits. Besides providing textured 3D meshes for clothes and body, we
annotate the dataset with segmentation masks, skeletons, depth, normal maps and
optical flow. All this together makes 3DPeople suitable for a plethora of
tasks.
  We then represent the 3D shapes using 2D geometry images. To build these
images we propose a novel spherical area-preserving parameterization algorithm
based on the optimal mass transportation method. We show this approach to
improve existing spherical maps which tend to shrink the elongated parts of the
full body models such as the arms and legs, making the geometry images
incomplete.
  Finally, we design a multi-resolution deep generative network that, given an
input image of a dressed human, predicts his/her geometry image (and thus the
clothed body shape) in an end-to-end manner. We obtain very promising results
in jointly capturing body pose and clothing shape, both for synthetic
validation and on the wild images.
Classification of Imbalanced Data with a Geometric Digraph Family
Authors: Artür Manukyan, Elvan Ceyhan
Date: 2019-04-09 00:00:00
Tasks: Classification, General Classification

We use a geometric digraph family called class cover catch digraphs (CCCDs)
to tackle the class imbalance problem in statistical classification. CCCDs
provide graph theoretic solutions to the class cover problem and have been
employed in classification. We assess the classification performance of CCCD
classifiers by extensive Monte Carlo simulations, comparing them with other
classifiers commonly used in the literature. In particular, we show that CCCD
classifiers perform relatively well when one class is more frequent than the
other in a two-class setting, an example of the class imbalance problem. We
also point out the relationship between class imbalance and class overlapping
problems, and their influence on the performance of CCCD classifiers and other
classification methods as well as some state-of-the-art algorithms which are
robust to class imbalance by construction. Experiments on both simulated and
real data sets indicate that CCCD classifiers are robust to the class imbalance
problem. CCCDs substantially undersample from the majority class while
preserving the information on the discarded points during the undersampling
process. Many state-of-the-art methods, however, keep this information by means
of ensemble classifiers, but CCCDs yield only a single classifier with the same
property, making it both appealing and fast.