pwc-archive/papers-with-abstracts huggingface dataset:
Semi-analytical formulas for the Hertzsprung-Russell Diagram
Authors: Lorenzo Zaninetti
Date: 2008-11-27 00:00:00

The absolute visual magnitude as function of the observed colour (B-V), also
named Hertzsprung-Russell diagram can be described through five equations; that
in presence of calibrated stars means eight constants. The developed framework
allows to deduce the remaining physical parameters that are mass, radius and
luminosity. This new technique is applied to the first 10 pc, the first 50 pc,
the Hyades and to the determination of the distance of a cluster. The case of
the white dwarfs is analysed assuming the absence of calibrated data: our
equation produces a smaller $\chi^2$ in respect to the standard
colour-magnitude calibration when applied to the Villanova Catalog of
Spectroscopically Identified White Dwarfs. The theoretical basis of the
formulae for the colours and the bolometric correction of the stars are
clarified through a Taylor expansion in the temperature of the Planck
distribution.
DeepPerimeter: Indoor Boundary Estimation from Posed Monocular Sequences
Authors: Ameya Phalak, Zhao Chen, Darvin Yi, Khushi Gupta, Vijay Badrinarayanan, Andrew Rabinovich
Date: 2019-04-25 00:00:00
Tasks: Clustering, Depth Estimation

We present DeepPerimeter, a deep learning based pipeline for inferring a full indoor perimeter (i.e. exterior boundary map) from a sequence of posed RGB images. Our method relies on robust deep methods for depth estimation and wall segmentation to generate an exterior boundary point cloud, and then uses deep unsupervised clustering to fit wall planes to obtain a final boundary map of the room. We demonstrate that DeepPerimeter results in excellent visual and quantitative performance on the popular ScanNet and FloorNet datasets and works for room shapes of various complexities as well as in multiroom scenarios. We also establish important baselines for future work on indoor perimeter estimation, topics which will become increasingly prevalent as application areas like augmented reality and robotics become more significant.
Learning Semantic Vector Representations of Source Code via a Siamese Neural Network
Authors: David Wehr, Halley Fede, Eleanor Pence, Bo Zhang, Guilherme Ferreira, John Walczyk, Joseph Hughes
Date: 2019-04-26 00:00:00
Tasks: BIG-bench Machine Learning

The abundance of open-source code, coupled with the success of recent
advances in deep learning for natural language processing, has given rise to a
promising new application of machine learning to source code. In this work, we
explore the use of a Siamese recurrent neural network model on Python source
code to create vectors which capture the semantics of code. We evaluate the
quality of embeddings by identifying which problem from a programming
competition the code solves. Our model significantly outperforms a
bag-of-tokens embedding, providing promising results for improving code
embeddings that can be used in future software engineering tasks.
CoachAI: A Conversational Agent Assisted Health Coaching Platform
Authors: Ahmed Fadhil, Gianluca Schiavo, Yunlong Wang
Date: 2019-04-26 00:00:00
Tasks: Chatbot, Management

Poor lifestyle represents a health risk factor and is the leading cause of
morbidity and chronic conditions. The impact of poor lifestyle can be
significantly altered by individual behavior change. Although the current shift
in healthcare towards a long lasting modifiable behavior, however, with
increasing caregiver workload and individuals' continuous needs of care, there
is a need to ease caregiver's work while ensuring continuous interaction with
users. This paper describes the design and validation of CoachAI, a
conversational agent assisted health coaching system to support health
intervention delivery to individuals and groups. CoachAI instantiates a text
based healthcare chatbot system that bridges the remote human coach and the
users. This research provides three main contributions to the preventive
healthcare and healthy lifestyle promotion: (1) it presents the conversational
agent to aid the caregiver; (2) it aims to decrease caregiver's workload and
enhance care given to users, by handling (automating) repetitive caregiver
tasks; and (3) it presents a domain independent mobile health conversational
agent for health intervention delivery. We will discuss our approach and
analyze the results of a one month validation study on physical activity,
healthy diet and stress management.
Lifting AutoEncoders: Unsupervised Learning of a Fully-Disentangled 3D Morphable Model using Deep Non-Rigid Structure from Motion
Authors: Mihir Sahasrabudhe, Zhixin Shu, Edward Bartrum, Riza Alp Guler, Dimitris Samaras, Iasonas Kokkinos
Date: 2019-04-26 00:00:00
Tasks: Image Manipulation, Image Reconstruction

In this work we introduce Lifting Autoencoders, a generative 3D surface-based
model of object categories. We bring together ideas from non-rigid structure
from motion, image formation, and morphable models to learn a controllable,
geometric model of 3D categories in an entirely unsupervised manner from an
unstructured set of images. We exploit the 3D geometric nature of our model and
use normal information to disentangle appearance into illumination, shading and
albedo. We further use weak supervision to disentangle the non-rigid shape
variability of human faces into identity and expression. We combine the 3D
representation with a differentiable renderer to generate RGB images and append
an adversarially trained refinement network to obtain sharp, photorealistic
image reconstruction results. The learned generative model can be controlled in
terms of interpretable geometry and appearance factors, allowing us to perform
photorealistic image manipulation of identity, expression, 3D pose, and
illumination properties.
On Exact Computation with an Infinitely Wide Neural Net
Authors: Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang
Date: 2019-04-26 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Gaussian Processes, GPU

How well does a classic deep net architecture like AlexNet or VGG19 classify on a standard dataset such as CIFAR-10 when its width --- namely, number of channels in convolutional layers, and number of nodes in fully-connected internal layers --- is allowed to increase to infinity? Such questions have come to the forefront in the quest to theoretically understand deep learning and its mysteries about optimization and generalization. They also connect deep learning to notions such as Gaussian processes and kernels. A recent paper [Jacot et al., 2018] introduced the Neural Tangent Kernel (NTK) which captures the behavior of fully-connected deep nets in the infinite width limit trained by gradient descent; this object was implicit in some other recent papers. An attraction of such ideas is that a pure kernel-based method is used to capture the power of a fully-trained deep net of infinite width. The current paper gives the first efficient exact algorithm for computing the extension of NTK to convolutional neural nets, which we call Convolutional NTK (CNTK), as well as an efficient GPU implementation of this algorithm. This results in a significant new benchmark for the performance of a pure kernel-based method on CIFAR-10, being $10\%$ higher than the methods reported in [Novak et al., 2019], and only $6\%$ lower than the performance of the corresponding finite deep net architecture (once batch normalization, etc. are turned off). Theoretically, we also give the first non-asymptotic proof showing that a fully-trained sufficiently wide net is indeed equivalent to the kernel regression predictor using NTK.
SWALP : Stochastic Weight Averaging in Low-Precision Training
Authors: Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa
Date: 2019-04-26 00:00:00

Low precision operations can provide scalability, memory savings, portability, and energy efficiency. This paper proposes SWALP, an approach to low precision training that averages low-precision SGD iterates with a modified learning rate schedule. SWALP is easy to implement and can match the performance of full-precision SGD even with all numbers quantized down to 8 bits, including the gradient accumulators. Additionally, we show that SWALP converges arbitrarily close to the optimal solution for quadratic objectives, and to a noise ball asymptotically smaller than low precision SGD in strongly convex settings.
Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding
Authors: Rujun Han, Mengyue Liang, Bashar Alhafni, Nanyun Peng
Date: 2019-04-26 00:00:00
Tasks: Relation, Relation Extraction, Temporal Relation Extraction, Word Embeddings

Learning causal and temporal relationships between events is an important
step towards deeper story and commonsense understanding. Though there are
abundant datasets annotated with event relations for story comprehension, many
have no empirical results associated with them. In this work, we establish
strong baselines for event temporal relation extraction on two under-explored
story narrative datasets: Richer Event Description (RED) and Causal and
Temporal Relation Scheme (CaTeRS). To the best of our knowledge, these are the
first results reported on these two datasets. We demonstrate that neural
network-based models can outperform some strong traditional linguistic
feature-based models. We also conduct comparative studies to show the
contribution of adopting contextualized word embeddings (BERT) for event
temporal relation extraction from stories. Detailed analyses are offered to
better understand the results.
Single Image Reflection Removal with Physically-Based Training Images
Authors: Soo-Min Kim, Yuchi Huo, Sung-Eui Yoon
Date: 2019-04-26 00:00:00
Proceeding: CVPR 2020 6
Tasks: Reflection Removal

Recently, deep learning-based single image reflection separation methods have been exploited widely. To benefit the learning approach, a large number of training image pairs (i.e., with and without reflections) were synthesized in various ways, yet they are away from a physically-based direction. In this paper, physically based rendering is used for faithfully synthesizing the required training images, and a corresponding network structure and loss term are proposed. We utilize existing RGBD/RGB images to estimate meshes, then physically simulate the light transportation between meshes, glass, and lens with path tracing to synthesize training data, which successfully reproduce the spatially variant anisotropic visual effect of glass reflection. For guiding the separation better, we additionally consider a module, backtrack network (BT-net) for backtracking the reflections, which removes complicated ghosting, attenuation, blurred and defocused effect of glass/lens. This enables obtaining a priori information before having the distortion. The proposed method considering additional a priori information with physically simulated training data is validated with various real reflection images and shows visually pleasant and numerical advantages compared with state-of-the-art techniques.
GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization
Authors: Lukas von Stumberg, Patrick Wenzel, Qadeer Khan, Daniel Cremers
Date: 2019-04-26 00:00:00

Direct SLAM methods have shown exceptional performance on odometry tasks. However, they are susceptible to dynamic lighting and weather changes while also suffering from a bad initialization on large baselines. To overcome this, we propose GN-Net: a network optimized with the novel Gauss-Newton loss for training weather invariant deep features, tailored for direct image alignment. Our network can be trained with pixel correspondences between images taken from different sequences. Experiments on both simulated and real-world datasets demonstrate that our approach is more robust against bad initialization, variations in day-time, and weather changes thereby outperforming state-of-the-art direct and indirect methods. Furthermore, we release an evaluation benchmark for relocalization tracking against different types of weather. Our benchmark is available at https://vision.in.tum.de/gn-net.
Spectral partitioning of time-varying networks with unobserved edges
Authors: Michael T. Schaub, Santiago Segarra, Hoi-To Wai
Date: 2019-04-26 00:00:00
Tasks: Community Detection

We discuss a variant of `blind' community detection, in which we aim to
partition an unobserved network from the observation of a (dynamical) graph
signal defined on the network. We consider a scenario where our observed graph
signals are obtained by filtering white noise input, and the underlying network
is different for every observation. In this fashion, the filtered graph signals
can be interpreted as defined on a time-varying network. We model each of the
underlying network realizations as generated by an independent draw from a
latent stochastic blockmodel (SBM). To infer the partition of the latent SBM,
we propose a simple spectral algorithm for which we provide a theoretical
analysis and establish consistency guarantees for the recovery. We illustrate
our results using numerical experiments on synthetic and real data,
highlighting the efficacy of our approach.
Accurate and Robust Alignment of Variable-stained Histologic Images Using a General-purpose Greedy Diffeomorphic Registration Tool
Authors: Ludovic Venet, Sarthak Pati, Paul Yushkevich, Spyridon Bakas
Date: 2019-04-26 00:00:00

Variously stained histology slices are routinely used by pathologists to
assess extracted tissue samples from various anatomical sites and determine the
presence or extent of a disease. Evaluation of sequential slides is expected to
enable a better understanding of the spatial arrangement and growth patterns of
cells and vessels. In this paper we present a practical two-step approach based
on diffeomorphic registration to align digitized sequential histopathology
stained slides to each other, starting with an initial affine step followed by
the estimation of a detailed deformation field.
Statistical feature embedding for heart sound classification
Authors: Mohammad Adiban, Bagher BabaAli, Saeedreza Shehnepoor
Date: 2019-04-26 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification, Sound Classification

Cardiovascular Disease (CVD) is considered as one of the principal causes of death in the world. Over recent years, this field of study has attracted researchers' attention to investigate heart sounds' patterns for disease diagnostics. In this study, an approach is proposed for normal/abnormal heart sound classification on the Physionet challenge 2016 dataset. For the first time, a fixed-length feature vector; called i-vector; is extracted from each heart sound using Mel Frequency Cepstral Coefficient (MFCC) features. Afterwards, Principal Component Analysis (PCA) transform and Variational Autoencoder (VAE) are applied on the i-vector to achieve dimension reduction. Eventually, the reduced size vector is fed to Gaussian Mixture Models (GMMs) and Support Vector Machine (SVM) for classification purpose. Experimental results demonstrate the proposed method could achieve a performance improvement of 16% based on Modified Accuracy (MAcc) compared with the baseline system on the Physoinet dataset.
Perceptual Attention-based Predictive Control
Authors: Keuntaek Lee, Gabriel Nakajima An, Viacheslav Zakharov, Evangelos A. Theodorou
Date: 2019-04-26 00:00:00
Tasks: Model Predictive Control, Uncertainty Quantification, Visual Navigation

In this paper, we present a novel information processing architecture for safe deep learning-based visual navigation of autonomous systems. The proposed information processing architecture is used to support a perceptual attention-based predictive control algorithm that leverages model predictive control (MPC), convolutional neural networks (CNNs), and uncertainty quantification methods. The novelty of our approach lies in using MPC to learn how to place attention on relevant areas of the visual input, which ultimately allows the system to more rapidly detect unsafe conditions. We accomplish this by using MPC to learn to select regions of interest in the input image, which are used to output control actions as well as estimates of epistemic and aleatoric uncertainty in the attention-aware visual input. We use these uncertainty estimates to quantify the safety of our network controller under the current navigation condition. The proposed architecture and algorithm is tested on a 1:5 scale terrestrial vehicle. Experimental results show that the proposed algorithm outperforms previous approaches on early detection of unsafe conditions, such as when novel obstacles are present in the navigation environment. The proposed architecture is the first step towards using deep learning-based perceptual control policies in safety-critical domains.
Recommending research articles to consumers of online vaccination information
Authors: Eliza Harrison, Paige Martin, Didi Surian, Adam G. Dunn
Date: 2019-04-26 00:00:00
Tasks: Articles, Dimensionality Reduction

Online health communications often provide biased interpretations of evidence and have unreliable links to the source research. We tested the feasibility of a tool for matching webpages to their source evidence. From 207,538 eligible vaccination-related PubMed articles, we evaluated several approaches using 3,573 unique links to webpages from Altmetric. We evaluated methods for ranking the source articles for vaccine-related research described on webpages, comparing simple baseline feature representation and dimensionality reduction approaches to those augmented with canonical correlation analysis (CCA). Performance measures included the median rank of the correct source article; the percentage of webpages for which the source article was correctly ranked first (recall@1); and the percentage ranked within the top 50 candidate articles (recall@50). While augmenting baseline methods using CCA generally improved results, no CCA-based approach outperformed a baseline method, which ranked the correct source article first for over one quarter of webpages and in the top 50 for more than half. Tools to help people identify evidence-based sources for the content they access on vaccination-related webpages are potentially feasible and may support the prevention of bias and misrepresentation of research in news and social media.
Robust Graph Data Learning via Latent Graph Convolutional Representation
Authors: Bo Jiang, Ziyan Zhang, Bin Luo
Date: 2019-04-26 00:00:00
Tasks: Graph Learning, Node Classification, Representation Learning

Graph Convolutional Representation (GCR) has achieved impressive performance for graph data representation. However, existing GCR is generally defined on the input fixed graph which may restrict the representation capacity and also be vulnerable to the structural attacks and noises. To address this issue, we propose a novel Latent Graph Convolutional Representation (LatGCR) for robust graph data representation and learning. Our LatGCR is derived based on reformulating graph convolutional representation from the aspect of graph neighborhood reconstruction. Given an input graph $\textbf{A}$, LatGCR aims to generate a flexible latent graph $\widetilde{\textbf{A}}$ for graph convolutional representation which obviously enhances the representation capacity and also performs robustly w.r.t graph structural attacks and noises. Moreover, LatGCR is implemented in a self-supervised manner and thus provides a basic block for both supervised and unsupervised graph learning tasks. Experiments on several datasets demonstrate the effectiveness and robustness of LatGCR.
Simulating Execution Time of Tensor Programs using Graph Neural Networks
Authors: Jakub M. Tomczak, Romain Lepert, Auke Wiggers
Date: 2019-04-26 00:00:00

Optimizing the execution time of tensor program, e.g., a convolution, involves finding its optimal configuration. Searching the configuration space exhaustively is typically infeasible in practice. In line with recent research using TVM, we propose to learn a surrogate model to overcome this issue. The model is trained on an acyclic graph called an abstract syntax tree, and utilizes a graph convolutional network to exploit structure in the graph. We claim that a learnable graph-based data processing is a strong competitor to heuristic-based feature extraction. We present a new dataset of graphs corresponding to configurations and their execution time for various tensor programs. We provide baselines for a runtime prediction task.
Learning to Prune: Speeding up Repeated Computations
Authors: Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, Ellen Vitercik
Date: 2019-04-26 00:00:00

It is common to encounter situations where one must solve a sequence of
similar computational problems. Running a standard algorithm with worst-case
runtime guarantees on each instance will fail to take advantage of valuable
structure shared across the problem instances. For example, when a commuter
drives from work to home, there are typically only a handful of routes that
will ever be the shortest path. A naive algorithm that does not exploit this
common structure may spend most of its time checking roads that will never be
in the shortest path. More generally, we can often ignore large swaths of the
search space that will likely never contain an optimal solution.
  We present an algorithm that learns to maximally prune the search space on
repeated computations, thereby reducing runtime while provably outputting the
correct solution each period with high probability. Our algorithm employs a
simple explore-exploit technique resembling those used in online algorithms,
though our setting is quite different. We prove that, with respect to our model
of pruning search spaces, our approach is optimal up to constant factors.
Finally, we illustrate the applicability of our model and algorithm to three
classic problems: shortest-path routing, string search, and linear programming.
We present experiments confirming that our simple algorithm is effective at
significantly reducing the runtime of solving repeated computations.
Unifying Part Detection and Association for Recurrent Multi-Person Pose Estimation
Authors: Rania Briq, Andreas Doering, Juergen Gall
Date: 2019-04-26 00:00:00
Tasks: Multi-Person Pose Estimation, Pose Estimation

We propose a joint model of human joint detection and association for 2D
multi-person pose estimation (MPPE). The approach unifies training of joint
detection and association without a need for further processing or
sophisticated heuristics in order to associate the joints with people
individually. The approach consists of two stages, where in the first stage
joint detection heatmaps and association features are extracted, and in the
second stage, whose input are the extracted features of the first stage, we
introduce a recurrent neural network (RNN) which predicts the heatmaps of a
single person's joints in each iteration. In addition, the network learns a
stopping criterion in order to halt once it has identified all individuals in
the image. This approach allowed us to eliminate several heuristic assumptions
and parameters needed for association which do not necessarily hold true.
Additionally, such an end-to-end approach allows the final objective to be
known and directly optimized over during training. We evaluated our model on
the challenging MSCOCO dataset and obtained an improvement over the baseline,
particularly in challenging scenes with occlusions.