pwc-archive/papers-with-abstracts huggingface dataset:
Optimizing regularized Cholesky score for order-based learning of Bayesian networks
Authors: Qiaoling Ye, Arash A. Amini, Qing Zhou
Date: 2019-04-28 00:00:00

Bayesian networks are a class of popular graphical models that encode causal
and conditional independence relations among variables by directed acyclic
graphs (DAGs). We propose a novel structure learning method, annealing on
regularized Cholesky score (ARCS), to search over topological sorts, or
permutations of nodes, for a high-scoring Bayesian network. Our scoring
function is derived from regularizing Gaussian DAG likelihood, and its
optimization gives an alternative formulation of the sparse Cholesky
factorization problem from a statistical viewpoint, which is of independent
interest. We combine global simulated annealing over permutations with a fast
proximal gradient algorithm, operating on triangular matrices of edge
coefficients, to compute the score of any permutation. Combined, the two
approaches allow us to quickly and effectively search over the space of DAGs
without the need to verify the acyclicity constraint or to enumerate possible
parent sets given a candidate topological sort. The annealing aspect of the
optimization is able to consistently improve the accuracy of DAGs learned by
local search algorithms. In addition, we develop several techniques to
facilitate the structure learning, including pre-annealing data-driven tuning
parameter selection and post-annealing constraint-based structure refinement.
Through extensive numerical comparisons, we show that ARCS achieves substantial
improvements over existing methods, demonstrating its great potential to learn
Bayesian networks from both observational and experimental data.
Deferred Neural Rendering: Image Synthesis using Neural Textures
Authors: Justus Thies, Michael Zollhöfer, Matthias Nießner
Date: 2019-04-28 00:00:00
Tasks: Image Generation, Neural Rendering, Novel View Synthesis

The modern computer graphics pipeline can synthesize images at remarkable
visual quality; however, it requires well-defined, high-quality 3D content as
input. In this work, we explore the use of imperfect 3D content, for instance,
obtained from photo-metric reconstructions with noisy and incomplete surface
geometry, while still aiming to produce photo-realistic (re-)renderings. To
address this challenging problem, we introduce Deferred Neural Rendering, a new
paradigm for image synthesis that combines the traditional graphics pipeline
with learnable components. Specifically, we propose Neural Textures, which are
learned feature maps that are trained as part of the scene capture process.
Similar to traditional textures, neural textures are stored as maps on top of
3D mesh proxies; however, the high-dimensional feature maps contain
significantly more information, which can be interpreted by our new deferred
neural rendering pipeline. Both neural textures and deferred neural renderer
are trained end-to-end, enabling us to synthesize photo-realistic images even
when the original 3D content was imperfect. In contrast to traditional,
black-box 2D generative neural networks, our 3D representation gives us
explicit control over the generated output, and allows for a wide range of
application domains. For instance, we can synthesize temporally-consistent
video re-renderings of recorded 3D scenes as our representation is inherently
embedded in 3D space. This way, neural textures can be utilized to coherently
re-render or manipulate existing video content in both static and dynamic
environments at real-time rates. We show the effectiveness of our approach in
several experiments on novel view synthesis, scene editing, and facial
reenactment, and compare to state-of-the-art approaches that leverage the
standard graphics pipeline as well as conventional generative neural networks.
Periodic Bandits and Wireless Network Selection
Authors: Shunhao Oh, Anuja Meetoo Appavoo, Seth Gilbert
Date: 2019-04-28 00:00:00

Bandit-style algorithms have been studied extensively in stochastic and
adversarial settings. Such algorithms have been shown to be useful in
multiplayer settings, e.g. to solve the wireless network selection problem,
which can be formulated as an adversarial bandit problem. A leading bandit
algorithm for the adversarial setting is EXP3. However, network behavior is
often repetitive, where user density and network behavior follow regular
patterns. Bandit algorithms, like EXP3, fail to provide good guarantees for
periodic behaviors. A major reason is that these algorithms compete against
fixed-action policies, which is ineffective in a periodic setting.
  In this paper, we define a periodic bandit setting, and periodic regret as a
better performance measure for this type of setting. Instead of comparing an
algorithm's performance to fixed-action policies, we aim to be competitive with
policies that play arms under some set of possible periodic patterns $F$ (for
example, all possible periodic functions with periods $1,2,\cdots,P$). We
propose Periodic EXP4, a computationally efficient variant of the EXP4
algorithm for periodic settings. With $K$ arms, $T$ time steps, and where each
periodic pattern in $F$ is of length at most $P$, we show that the periodic
regret obtained by Periodic EXP4 is at most $O\big(\sqrt{PKT \log K + KT \log
|F|}\big)$. We also prove a lower bound of $\Omega\big(\sqrt{PKT + KT
\frac{\log |F|}{\log K}} \big)$ for the periodic setting, showing that this is
optimal within log-factors. As an example, we focus on the wireless network
selection problem. Through simulation, we show that Periodic EXP4 learns the
periodic pattern over time, adapts to changes in a dynamic environment, and far
outperforms EXP3.
Cough Detection Using Hidden Markov Models
Authors: Aydin Teyhouee, Nathaniel D. Osgood
Date: 2019-04-28 00:00:00
Tasks: Time Series, Time Series Analysis

Respiratory infections and chronic respiratory diseases impose a heavy health
burden worldwide. Coughing is one of the most common symptoms of many such
infections, and can be indicative of flare-ups of chronic respiratory diseases.
Whether at a clinical or public health level, the capacity to identify bouts of
coughing can aid understanding of population and individual health status.
Developing health monitoring models in the context of respiratory diseases and
also seasonal diseases with symptoms such as cough has the potential to improve
quality of life, help clinicians and public health authorities with their
decisions and decrease the cost of health services. In this paper, we
investigated the ability to which a simple machine learning approach in the
form of Hidden Markov Models (HMMs) could be used to classify different states
of coughing using univariate (with a single energy band as the input feature)
and multivariate (with a multiple energy band as the input features) binned
time series using both of cough data. We further used the model to distinguish
cough events from other events and environmental noise. Our Hidden Markov
algorithm achieved 92% AUR (Area Under Receiver Operating Characteristic Curve)
in classifying coughing events in noisy environments. Moreover, comparison of
univariate with multivariate HMMs suggest a high accuracy of multivariate HMMs
for cough event classifications.
Domain Agnostic Learning with Disentangled Representations
Authors: Xingchao Peng, Zijun Huang, Ximeng Sun, Kate Saenko
Date: 2019-04-28 00:00:00
Tasks: General Classification, image-classification, Image Classification, Multi-target Domain Adaptation

Unsupervised model transfer has the potential to greatly improve the
generalizability of deep models to novel domains. Yet the current literature
assumes that the separation of target data into distinct domains is known as a
priori. In this paper, we propose the task of Domain-Agnostic Learning (DAL):
How to transfer knowledge from a labeled source domain to unlabeled data from
arbitrary target domains? To tackle this problem, we devise a novel Deep
Adversarial Disentangled Autoencoder (DADA) capable of disentangling
domain-specific features from class identity. We demonstrate experimentally
that when the target domain labels are unknown, DADA leads to state-of-the-art
performance on several image classification datasets.
Learning walk and trot from the same objective using different types of exploration
Authors: Zinan Liu, Kai Ploeger, Svenja Stark, Elmar Rueckert, Jan Peters
Date: 2019-04-28 00:00:00

In quadruped gait learning, policy search methods that scale high dimensional
continuous action spaces are commonly used. In most approaches, it is necessary
to introduce prior knowledge on the gaits to limit the highly non-convex search
space of the policies. In this work, we propose a new approach to encode the
symmetry properties of the desired gaits, on the initial covariance of the
Gaussian search distribution, allowing for strategic exploration. Using
episode-based likelihood ratio policy gradient and relative entropy policy
search, we learned the gaits walk and trot on a simulated quadruped. Comparing
these gaits to random gaits learned by initialized diagonal covariance matrix,
we show that the performance can be significantly enhanced.
Blended Matching Pursuit
Authors: Cyrille W. Combettes, Sebastian Pokutta
Date: 2019-04-28 00:00:00
Proceeding: NeurIPS 2019 12

Matching pursuit algorithms are an important class of algorithms in signal processing and machine learning. We present a blended matching pursuit algorithm, combining coordinate descent-like steps with stronger gradient descent steps, for minimizing a smooth convex function over a linear space spanned by a set of atoms. We derive sublinear to linear convergence rates according to the smoothness and sharpness orders of the function and demonstrate computational superiority of our approach. In particular, we derive linear rates for a wide class of non-strongly convex functions, and we demonstrate in experiments that our algorithm enjoys very fast rates of convergence and wall-clock speed while maintaining a sparsity of iterates very comparable to that of the (much slower) orthogonal matching pursuit.
Support Vector Regression via a Combined Reward Cum Penalty Loss Function
Authors: Pritam Anand, Reshma Rastogi, Suresh Chandra
Date: 2019-04-28 00:00:00
Tasks: regression

In this paper, we introduce a novel combined reward cum penalty loss function to handle the regression problem. The proposed combined reward cum penalty loss function penalizes the data points which lie outside the $\epsilon$-tube of the regressor and also assigns reward for the data points which lie inside of the $\epsilon$-tube of the regressor. The combined reward cum penalty loss function based regression (RP-$\epsilon$-SVR) model has several interesting properties which are investigated in this paper and are also supported with the experimental results.
OPIEC: An Open Information Extraction Corpus
Authors: Kiril Gashteovski, Sebastian Wanner, Sven Hertling, Samuel Broscheit, Rainer Gemulla
Date: 2019-04-28 00:00:00
Proceeding: AKBC 2019
Tasks: Knowledge Base Construction, Open-Ended Question Answering, Open Information Extraction, Question Answering, Specificity

Open information extraction (OIE) systems extract relations and their
arguments from natural language text in an unsupervised manner. The resulting
extractions are a valuable resource for downstream tasks such as knowledge base
construction, open question answering, or event schema induction. In this
paper, we release, describe, and analyze an OIE corpus called OPIEC, which was
extracted from the text of English Wikipedia. OPIEC complements the available
OIE resources: It is the largest OIE corpus publicly available to date (over
340M triples) and contains valuable metadata such as provenance information,
confidence scores, linguistic annotations, and semantic annotations including
spatial and temporal information. We analyze the OPIEC corpus by comparing its
content with knowledge bases such as DBpedia or YAGO, which are also based on
Wikipedia. We found that most of the facts between entities present in OPIEC
cannot be found in DBpedia and/or YAGO, that OIE facts often differ in the
level of specificity compared to knowledge base facts, and that OIE open
relations are generally highly polysemous. We believe that the OPIEC corpus is
a valuable resource for future research on automated knowledge base
construction.
An approach to image denoising using manifold approximation without clean images
Authors: Rohit Jena
Date: 2019-04-28 00:00:00
Tasks: Deblurring, Denoising, Image Denoising, Image Enhancement, Image Restoration, Super-Resolution

Image restoration has been an extensively researched topic in numerous
fields. With the advent of deep learning, a lot of the current algorithms were
replaced by algorithms that are more flexible and robust. Deep networks have
demonstrated impressive performance in a variety of tasks like blind denoising,
image enhancement, deblurring, super-resolution, inpainting, among others. Most
of these learning-based algorithms use a large amount of clean data during the
training process. However, in certain applications in medical image processing,
one may not have access to a large amount of clean data. In this paper, we
propose a method for denoising that attempts to learn the denoising process by
pushing the noisy data close to the clean data manifold, using only noisy
images during training. Furthermore, we use perceptual loss terms and an
iterative refinement step to further refine the clean images without losing
important features.
Real numbers, data science and chaos: How to fit any dataset with a single parameter
Authors: Laurent Boué
Date: 2019-04-28 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

We show how any dataset of any modality (time-series, images, sound...) can be approximated by a well-behaved (continuous, differentiable...) scalar function with a single real-valued parameter. Building upon elementary concepts from chaos theory, we adopt a pedagogical approach demonstrating how to adjust this parameter in order to achieve arbitrary precision fit to all samples of the data. Targeting an audience of data scientists with a taste for the curious and unusual, the results presented here expand on previous similar observations regarding expressiveness power and generalization of machine learning models.
RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion
Authors: Muhammad Sarmad, Hyunjoo Jenny Lee, Young Min Kim
Date: 2019-04-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: Generative Adversarial Network, Reinforcement Learning, Reinforcement Learning (RL)

We present RL-GAN-Net, where a reinforcement learning (RL) agent provides
fast and robust control of a generative adversarial network (GAN). Our
framework is applied to point cloud shape completion that converts noisy,
partial point cloud data into a high-fidelity completed shape by controlling
the GAN. While a GAN is unstable and hard to train, we circumvent the problem
by (1) training the GAN on the latent space representation whose dimension is
reduced compared to the raw point cloud input and (2) using an RL agent to find
the correct input to the GAN to generate the latent space representation of the
shape that best fits the current input of incomplete point cloud. The suggested
pipeline robustly completes point cloud with large missing regions. To the best
of our knowledge, this is the first attempt to train an RL agent to control the
GAN, which effectively learns the highly nonlinear mapping from the input noise
of the GAN to the latent space of point cloud. The RL agent replaces the need
for complex optimization and consequently makes our technique real time.
Additionally, we demonstrate that our pipelines can be used to enhance the
classification accuracy of point cloud with missing data.
Deep-MAPS: Machine Learning based Mobile Air Pollution Sensing
Authors: Jun Song, Ke Han
Date: 2019-04-28 00:00:00
Tasks: BIG-bench Machine Learning, Management

Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. This paper proposes a machine learning based mobile air pollution sensing framework, called Deep-MAPS, and demonstrates its scientific and financial values in the following aspects. (1) Based on a network of fixed and mobile air quality sensors, we perform spatial inference of PM2.5 concentrations in Beijing (3,025 km2, 19 Jun-16 Jul 2018) for a spatial-temporal resolution of 1km-by-1km and 1 hour, with over 85% accuracy. (2) We leverage urban big data to generate insights regarding the potential cause of pollution, which facilitates evidence-based sustainable urban management. (3) To achieve such spatial-temporal coverage and accuracy, Deep-MAPS can save up to 90% hardware investment, compared with ubiquitous sensing that relies primarily on fixed sensors.
Synthetic Data Generation and Adaption for Object Detection in Smart Vending Machines
Authors: Kai Wang, Fuyuan Shi, Wenqi Wang, Yibing Nan, Shiguo Lian
Date: 2019-04-28 00:00:00
Tasks: object-detection, Object Detection, Synthetic Data Generation

This paper presents an improved scheme for the generation and adaption of
synthetic images for the training of deep Convolutional Neural Networks(CNNs)
to perform the object detection task in smart vending machines. While
generating synthetic data has proved to be effective for complementing the
training data in supervised learning methods, challenges still exist for
generating virtual images which are similar to those of the complex real scenes
and minimizing redundant training data. To solve these problems, we consider
the simulation of cluttered objects placed in a virtual scene and the
wide-angle camera with distortions used to capture the whole scene in the data
generation process, and post-processed the generated images with a
elaborately-designed generative network to make them more similar to the real
images. Various experiments have been conducted to prove the efficiency of
using the generated virtual images to enhance the detection precision on
existing datasets with limited real training data and the generalization
ability of applying the trained network to datasets collected in new
environment.
Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networks
Authors: Koby Bibas, Yaniv Fogel, Meir Feder
Date: 2019-04-28 00:00:00

The Predictive Normalized Maximum Likelihood (pNML) scheme has been recently suggested for universal learning in the individual setting, where both the training and test samples are individual data. The goal of universal learning is to compete with a ``genie'' or reference learner that knows the data values, but is restricted to use a learner from a given model class. The pNML minimizes the associated regret for any possible value of the unknown label. Furthermore, its min-max regret can serve as a pointwise measure of learnability for the specific training and data sample. In this work we examine the pNML and its associated learnability measure for the Deep Neural Network (DNN) model class. As shown, the pNML outperforms the commonly used Empirical Risk Minimization (ERM) approach and provides robustness against adversarial attacks. Together with its learnability measure it can detect out of distribution test examples, be tolerant to noisy labels and serve as a confidence measure for the ERM. Finally, we extend the pNML to a ``twice universal'' solution, that provides universality for model class selection and generates a learner competing with the best one from all model classes.
3D Dynamic Point Cloud Denoising via Spatial-Temporal Graph Learning
Authors: Wei Hu, Qianjiang Hu, Zehua Wang, Xiang Gao
Date: 2019-04-28 00:00:00
Tasks: Denoising, graph construction, Graph Learning

The prevalence of accessible depth sensing and 3D laser scanning techniques has enabled the convenient acquisition of 3D dynamic point clouds, which provide efficient representation of arbitrarily-shaped objects in motion. Nevertheless, dynamic point clouds are often perturbed by noise due to hardware, software or other causes. While a plethora of methods have been proposed for static point cloud denoising, few efforts are made for the denoising of dynamic point clouds with varying number of irregularly-sampled points in each frame. In this paper, we represent dynamic point clouds naturally on graphs and address the denoising problem by inferring the underlying graph via spatio-temporal graph learning, exploiting both the intra-frame similarity and inter-frame consistency. Firstly, assuming the availability of a relevant feature vector per node, we pose spatial-temporal graph learning as optimizing a Mahalanobis distance metric $\mathbf{M}$, which is formulated as the minimization of graph Laplacian regularizer. Secondly, to ease the optimization of the symmetric and positive definite metric matrix $\mathbf{M}$, we decompose it into $\mathbf{M}=\mathbf{R}^{\top}\mathbf{R}$ and solve $\mathbf{R}$ instead via proximal gradient. Finally, based on the spatial-temporal graph learning, we formulate dynamic point cloud denoising as the joint optimization of the desired point cloud and underlying spatio-temporal graph, which leverages both intra-frame affinities and inter-frame consistency and is solved via alternating minimization. Experimental results show that the proposed method significantly outperforms independent denoising of each frame from state-of-the-art static point cloud denoising approaches.
Robust subspace clustering by Cauchy loss function
Authors: Xuelong. Li, Quanmao Lu, Yongsheng Dong, DaCheng Tao
Date: 2019-04-28 00:00:00
Tasks: Clustering

Subspace clustering is a problem of exploring the low-dimensional subspaces
of high-dimensional data. State-of-the-arts approaches are designed by
following the model of spectral clustering based method. These methods pay much
attention to learn the representation matrix to construct a suitable similarity
matrix and overlook the influence of the noise term on subspace clustering.
However, the real data are always contaminated by the noise and the noise
usually has a complicated statistical distribution. To alleviate this problem,
we in this paper propose a subspace clustering method based on Cauchy loss
function (CLF). Particularly, it uses CLF to penalize the noise term for
suppressing the large noise mixed in the real data. This is due to that the
CLF's influence function has a upper bound which can alleviate the influence of
a single sample, especially the sample with a large noise, on estimating the
residuals. Furthermore, we theoretically prove the grouping effect of our
proposed method, which means that highly correlated data can be grouped
together. Finally, experimental results on five real datasets reveal that our
proposed method outperforms several representative clustering methods.