pwc-archive/papers-with-abstracts huggingface dataset:
On the Influence of Bias-Correction on Distributed Stochastic Optimization
Authors: Kun Yuan, Sulaiman A. Alghunaim, Bicheng Ying, Ali H. Sayed
Date: 2019-03-26 00:00:00
Tasks: Stochastic Optimization

Various bias-correction methods such as EXTRA, gradient tracking methods, and exact diffusion have been proposed recently to solve distributed {\em deterministic} optimization problems. These methods employ constant step-sizes and converge linearly to the {\em exact} solution under proper conditions. However, their performance under stochastic and adaptive settings is less explored. It is still unknown {\em whether}, {\em when} and {\em why} these bias-correction methods can outperform their traditional counterparts (such as consensus and diffusion) with noisy gradient and constant step-sizes. This work studies the performance of exact diffusion under the stochastic and adaptive setting, and provides conditions under which exact diffusion has superior steady-state mean-square deviation (MSD) performance than traditional algorithms without bias-correction. In particular, it is proven that this superiority is more evident over sparsely-connected network topologies such as lines, cycles, or grids. Conditions are also provided under which exact diffusion method match or may even degrade the performance of traditional methods. Simulations are provided to validate the theoretical findings.
GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving
Authors: Buyu Li, Wanli Ouyang, Lu Sheng, Xingyu Zeng, Xiaogang Wang
Date: 2019-03-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Object Detection, Autonomous Driving, Monocular 3D Object Detection, Object, object-detection, Object Detection, Vehicle Pose Estimation

We present an efficient 3D object detection framework based on a single RGB
image in the scenario of autonomous driving. Our efforts are put on extracting
the underlying 3D information in a 2D image and determining the accurate 3D
bounding box of the object without point cloud or stereo data. Leveraging the
off-the-shelf 2D object detector, we propose an artful approach to efficiently
obtain a coarse cuboid for each predicted 2D box. The coarse cuboid has enough
accuracy to guide us to determine the 3D box of the object by refinement. In
contrast to previous state-of-the-art methods that only use the features
extracted from the 2D bounding box for box refinement, we explore the 3D
structure information of the object by employing the visual features of visible
surfaces. The new features from surfaces are utilized to eliminate the problem
of representation ambiguity brought by only using a 2D bounding box. Moreover,
we investigate different methods of 3D box refinement and discover that a
classification formulation with quality aware loss has much better performance
than regression. Evaluated on the KITTI benchmark, our approach outperforms
current state-of-the-art methods for single RGB image based 3D object
detection.
Optimize TSK Fuzzy Systems for Regression Problems: Mini-Batch Gradient Descent with Regularization, DropRule and AdaBound (MBGD-RDA)
Authors: Dongrui Wu, Ye Yuan, Yihua Tan
Date: 2019-03-26 00:00:00

Takagi-Sugeno-Kang (TSK) fuzzy systems are very useful machine learning models for regression problems. However, to our knowledge, there has not existed an efficient and effective training algorithm that ensures their generalization performance, and also enables them to deal with big data. Inspired by the connections between TSK fuzzy systems and neural networks, we extend three powerful neural network optimization techniques, i.e., mini-batch gradient descent, regularization, and AdaBound, to TSK fuzzy systems, and also propose three novel techniques (DropRule, DropMF, and DropMembership) specifically for training TSK fuzzy systems. Our final algorithm, mini-batch gradient descent with regularization, DropRule and AdaBound (MBGD-RDA), can achieve fast convergence in training TSK fuzzy systems, and also superior generalization performance in testing. It can be used for training TSK fuzzy systems on datasets of any size; however, it is particularly useful for big datasets, on which currently no other efficient training algorithms exist.
A Probabilistic Generative Model of Linguistic Typology
Authors: Johannes Bjerva, Yova Kementchedjhieva, Ryan Cotterell, Isabelle Augenstein
Date: 2019-03-26 00:00:00
Proceeding: NAACL 2019 6
Tasks: model

In the principles-and-parameters framework, the structural features of languages depend on parameters that may be toggled on or off, with a single parameter often dictating the status of multiple features. The implied covariance between features inspires our probabilisation of this line of linguistic inquiry---we develop a generative model of language based on exponential-family matrix factorisation. By modelling all languages and features within the same architecture, we show how structural similarities between languages can be exploited to predict typological features with near-perfect accuracy, outperforming several baselines on the task of predicting held-out features. Furthermore, we show that language embeddings pre-trained on monolingual text allow for generalisation to unobserved languages. This finding has clear practical and also theoretical implications: the results confirm what linguists have hypothesised, i.e.~that there are significant correlations between typological features and languages.
Reconstruction of r-Regular Objects from Trinary Images
Authors: Helene Svane, Andrew du Plessis
Date: 2019-03-26 00:00:00
Tasks: Object

We study digital images of r-regular objects where a pixel is black if it is
completely inside the object, white if it is completely inside the complement
of the object, and grey otherwise. We call such images trinary. We discuss
possible configurations of pixels in trinary images of r-regular objects at
certain resolutions and propose a method for reconstructing objects from such
images. We show that the reconstructed object is close to the original object
in Hausdorff norm, and that there is a homeomorphism of the plane taking the
reconstructed set to the original.
Exploring Confidence Measures for Word Spotting in Heterogeneous Datasets
Authors: Fabian Wolf, Philipp Oberdiek, Gernot A. Fink
Date: 2019-03-26 00:00:00
Tasks: Attribute, Retrieval

In recent years, convolutional neural networks (CNNs) took over the field of
document analysis and they became the predominant model for word spotting.
Especially attribute CNNs, which learn the mapping between a word image and an
attribute representation, showed exceptional performances. The drawback of this
approach is the overconfidence of neural networks when used out of their
training distribution. In this paper, we explore different metrics for
quantifying the confidence of a CNN in its predictions, specifically on the
retrieval problem of word spotting. With these confidence measures, we limit
the inability of a retrieval list to reject certain candidates. We investigate
four different approaches that are either based on the network's attribute
estimations or make use of a surrogate model. Our approach also aims at
answering the question for which part of a dataset the retrieval system gives
reliable results. We further show that there exists a direct relation between
the proposed confidence measures and the quality of an estimated attribute
representation.
TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo
Authors: Andrea Romanoni, Matteo Matteucci
Date: 2019-03-26 00:00:00
Proceeding: ICCV 2019 10

One of the most successful approaches in Multi-View Stereo estimates a depth
map and a normal map for each view via PatchMatch-based optimization and fuses
them into a consistent 3D points cloud. This approach relies on
photo-consistency to evaluate the goodness of a depth estimate. It generally
produces very accurate results; however, the reconstructed model often lacks
completeness, especially in correspondence of broad untextured areas where the
photo-consistency metrics are unreliable. Assuming the untextured areas
piecewise planar, in this paper we generate novel PatchMatch hypotheses so to
expand reliable depth estimates in neighboring untextured regions. At the same
time, we modify the photo-consistency measure such to favor standard or novel
PatchMatch depth hypotheses depending on the textureness of the considered
area. We also propose a depth refinement step to filter wrong estimates and to
fill the gaps on both the depth maps and normal maps while preserving the
discontinuities. The effectiveness of our new methods has been tested against
several state of the art algorithms in the publicly available ETH3D dataset
containing a wide variety of high and low-resolution images.
Classifying Partially Labeled Networked Data via Logistic Network Lasso
Authors: Nguyen Tran, Henrik Ambos, Alexander Jung
Date: 2019-03-26 00:00:00

We apply the network Lasso to classify partially labeled data points which
are characterized by high-dimensional feature vectors. In order to learn an
accurate classifier from limited amounts of labeled data, we borrow statistical
strength, via an intrinsic network structure, across the dataset. The resulting
logistic network Lasso amounts to a regularized empirical risk minimization
problem using the total variation of a classifier as a regularizer. This
minimization problem is a non-smooth convex optimization problem which we solve
using a primal-dual splitting method. This method is appealing for big data
applications as it can be implemented as a highly scalable message passing
algorithm.
A New Approach for Semi-automatic Building and Extending a Multilingual Terminology Thesaurus
Authors: Adam Rambousek, Ales Horak, Vit Suchomel, Vit Baisa
Date: 2019-03-26 00:00:00

This paper describes a new system for semi-automatically building, extending
and managing a terminological thesaurus---a multilingual terminology dictionary
enriched with relationships between the terms themselves to form a thesaurus.
The system allows to radically enhance the workflow of current terminology
expert groups, where most of the editing decisions still come from
introspection. The presented system supplements the lexicographic process with
natural language processing techniques, which are seamlessly integrated to the
thesaurus editing environment. The system's methodology and the resulting
thesaurus are closely connected to new domain corpora in the six languages
involved. They are used for term usage examples as well as for the automatic
extraction of new candidate terms. The terminological thesaurus is now
accessible via a web-based application, which a) presents rich detailed
information on each term, b) visualizes term relations, and c) displays
real-life usage examples of the term in the domain-related documents and in the
context-based similar terms. Furthermore, the specialized corpora are used to
detect candidate translations of terms from the central language (Czech) to the
other languages (English, French, German, Russian and Slovak) as well as to
detect broader Czech terms, which help to place new terms in the actual
thesaurus hierarchy. This project has been realized as a terminological
thesaurus of land surveying, but the presented tools and methodology are
reusable for other terminology domains.
High-Level Perceptual Similarity is Enabled by Learning Diverse Tasks
Authors: Amir Rosenfeld, Richard Zemel, John K. Tsotsos
Date: 2019-03-26 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Predicting human perceptual similarity is a challenging subject of ongoing
research. The visual process underlying this aspect of human vision is thought
to employ multiple different levels of visual analysis (shapes, objects,
texture, layout, color, etc). In this paper, we postulate that the perception
of image similarity is not an explicitly learned capability, but rather one
that is a byproduct of learning others. This claim is supported by leveraging
representations learned from a diverse set of visual tasks and using them
jointly to predict perceptual similarity. This is done via simple feature
concatenation, without any further learning. Nevertheless, experiments
performed on the challenging Totally-Looks-Like (TLL) benchmark significantly
surpass recent baselines, closing much of the reported gap towards prediction
of human perceptual similarity. We provide an analysis of these results and
discuss them in a broader context of emergent visual capabilities and their
implications on the course of machine-vision research.
Language Model Adaptation for Language and Dialect Identification of Text
Authors: Tommi Jauhiainen, Krister Lindén, Heidi Jauhiainen
Date: 2019-03-26 00:00:00
Tasks: Dialect Identification, Language Identification, Language Modeling, Language Modelling

This article describes an unsupervised language model adaptation approach
that can be used to enhance the performance of language identification methods.
The approach is applied to a current version of the HeLI language
identification method, which is now called HeLI 2.0. We describe the HeLI 2.0
method in detail. The resulting system is evaluated using the datasets from the
German dialect identification and Indo-Aryan language identification shared
tasks of the VarDial workshops 2017 and 2018. The new approach with language
identification provides considerably higher F1-scores than the previous HeLI
method or the other systems which participated in the shared tasks. The results
indicate that unsupervised language model adaptation should be considered as an
option in all language identification tasks, especially in those where
encountering out-of-domain data is likely.
Interference Prediction in Wireless Networks: Stochastic Geometry meets Recursive Filtering
Authors: Jorge F. Schmidt, Udo Schilcher, Mahin K. Atiq, Christian Bettstetter
Date: 2019-03-26 00:00:00
Tasks: Management, Scheduling

This article proposes and evaluates a technique to predict the level of interference in wireless networks. We design a recursive predictor that estimates future interference values by filtering measured interference at a given location. The predictor's parameterization is done offline by translating the autocorrelation of interference into an autoregressive moving average (ARMA) representation. This ARMA model is inserted into a steady-state Kalman filter enabling nodes to predict with low computational effort. Results show a good accuracy of predicted values versus true values for relevant time horizons. Although the predictor is parameterized for Poisson-distributed nodes, Rayleigh fading, and fixed message lengths, a sensitivity analysis shows that it also tends to work well in more general network scenarios. Numerical examples for underlay device-to-device communications, a common wireless sensor technology, and coexistence scenarios of Wi-Fi and LTE illustrate its broad applicability. The predictor can be applied as part of interference management to improve medium access, scheduling, and radio resource allocation.
Photo-Realistic Facial Details Synthesis from Single Image
Authors: Anpei Chen, Zhang Chen, Guli Zhang, Ziheng Zhang, Kenny Mitchell, Jingyi Yu
Date: 2019-03-26 00:00:00
Proceeding: ICCV 2019 10
Tasks: Face Generation

We present a single-image 3D face synthesis technique that can handle challenging facial expressions while recovering fine geometric details. Our technique employs expression analysis for proxy face geometry generation and combines supervised and unsupervised learning for facial detail synthesis. On proxy generation, we conduct emotion prediction to determine a new expression-informed proxy. On detail synthesis, we present a Deep Facial Detail Net (DFDN) based on Conditional Generative Adversarial Net (CGAN) that employs both geometry and appearance loss functions. For geometry, we capture 366 high-quality 3D scans from 122 different subjects under 3 facial expressions. For appearance, we use additional 20K in-the-wild face images and apply image-based rendering to accommodate lighting variations. Comprehensive experiments demonstrate that our framework can produce high-quality 3D faces with realistic details under challenging facial expressions.
Active Stacking for Heart Rate Estimation
Authors: Dongrui Wu, Feifei Liu, Chengyu Liu
Date: 2019-03-26 00:00:00
Tasks: Active Learning, Heart rate estimation, regression

Heart rate estimation from electrocardiogram signals is very important for
the early detection of cardiovascular diseases. However, due to large
individual differences and varying electrocardiogram signal quality, there does
not exist a single reliable estimation algorithm that works well on all
subjects. Every algorithm may break down on certain subjects, resulting in a
significant estimation error. Ensemble regression, which aggregates the outputs
of multiple base estimators for more reliable and stable estimates, can be used
to remedy this problem. Moreover, active learning can be used to optimally
select a few trials from a new subject to label, based on which a stacking
ensemble regression model can be trained to aggregate the base estimators. This
paper proposes four active stacking approaches, and demonstrates that they all
significantly outperform three common unsupervised ensemble regression
approaches, and a supervised stacking approach which randomly selects some
trials to label. Remarkably, our active stacking approaches only need three or
four labeled trials from each subject to achieve an average root mean squared
estimation error below three beats per minute, making them very convenient for
real-world applications. To our knowledge, this is the first research on active
stacking, and its application to heart rate estimation.
Improve Diverse Text Generation by Self Labeling Conditional Variational Auto Encoder
Authors: Yuchi Zhang, Yongliang Wang, Liping Zhang, Zhiqiang Zhang, Kun Gai
Date: 2019-03-26 00:00:00
Tasks: Decoder, Diversity, Text Generation

Diversity plays a vital role in many text generating applications. In recent
years, Conditional Variational Auto Encoders (CVAE) have shown promising
performances for this task. However, they often encounter the so called
KL-Vanishing problem. Previous works mitigated such problem by heuristic
methods such as strengthening the encoder or weakening the decoder while
optimizing the CVAE objective function. Nevertheless, the optimizing direction
of these methods are implicit and it is hard to find an appropriate degree to
which these methods should be applied. In this paper, we propose an explicit
optimizing objective to complement the CVAE to directly pull away from
KL-vanishing. In fact, this objective term guides the encoder towards the "best
encoder" of the decoder to enhance the expressiveness. A labeling network is
introduced to estimate the "best encoder". It provides a continuous label in
the latent space of CVAE to help build a close connection between latent
variables and targets. The whole proposed method is named Self Labeling
CVAE~(SLCVAE). To accelerate the research of diverse text generation, we also
propose a large native one-to-many dataset. Extensive experiments are conducted
on two tasks, which show that our method largely improves the generating
diversity while achieving comparable accuracy compared with state-of-art
algorithms.
Data-Driven Microstructure Property Relations
Authors: Julian Lißner, Felix Fritzen
Date: 2019-03-26 00:00:00
Tasks: Prediction

An image based prediction of the effective heat conductivity for highly
heterogeneous microstructured materials is presented. The synthetic materials
under consideration show different inclusion morphology, orientation, volume
fraction and topology. The prediction of the effective property is made
exclusively based on image data with the main emphasis being put on the 2-point
spatial correlation function. This task is implemented using both unsupervised
and supervised machine learning methods. First, a snapshot proper orthogonal
decomposition (POD) is used to analyze big sets of random microstructures and
thereafter compress significant characteristics of the microstructure into a
low-dimensional feature vector. In order to manage the related amount of data
and computations, three different incremental snapshot POD methods are
proposed. In the second step, the obtained feature vector is used to predict
the effective material property by using feed forward neural networks.
Numerical examples regarding the incremental basis identification and the
prediction accuracy of the approach are presented. A Python code illustrating
the application of the surrogate is freely available.
Musical Tempo and Key Estimation using Convolutional Neural Networks with Directional Filters
Authors: Hendrik Schreiber, Meinard Müller
Date: 2019-03-26 00:00:00

In this article we explore how the different semantics of spectrograms' time
and frequency axes can be exploited for musical tempo and key estimation using
Convolutional Neural Networks (CNN). By addressing both tasks with the same
network architectures ranging from shallow, domain-specific approaches to deep
variants with directional filters, we show that axis-aligned architectures
perform similarly well as common VGG-style networks developed for computer
vision, while being less vulnerable to confounding factors and requiring fewer
model parameters.
Pixelation is NOT Done in Videos Yet
Authors: Jizhe Zhou, Chi-Man Pun, YingYu Wang
Date: 2019-03-26 00:00:00
Tasks: Clustering, Face Detection, Face Recognition, Position

This paper introduces an algorithm to protect the privacy of individuals in
streaming video data by blurring faces such that face cannot be reliably
recognized. This thwarts any possible face recognition, but because all facial
details are obscured, the result is of limited use. We propose a new clustering
algorithm to create raw trajectories for detected faces. Associating faces
across frames to form trajectories, it auto-generates cluster number and
discovers new clusters through deep feature and position aggregated affinities.
We introduce a Gaussian Process to refine the raw trajectories. We conducted an
online experiment with 47 participants to evaluate the effectiveness of face
blurring compared to the original photo (as-is), and users' experience
(satisfaction, information sufficiency, enjoyment, social presence, and filter
likeability)
Network reconstruction and community detection from dynamics
Authors: Tiago P. Peixoto
Date: 2019-03-26 00:00:00
Tasks: Community Detection

We present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior that at the same time infers the communities present in the network. We show that the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform the existence of communities are also inherently used to improve the accuracy of the reconstruction which, in turn, can better inform the uncovering of communities. We illustrate the use of our method with observations arising from epidemic models and the Ising model, both on synthetic and empirical networks, as well as on data containing only functional information.