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Supertagging the Long Tail with Tree-Structured Decoding of Complex Categories


Authors:  JakobPrange, NathanSchneider, VivekSrikumar....
Published date-12/02/2020
Tasks:  StructuredPrediction

Abstract: Although current CCG supertaggers achieve high accuracy on the standard WSJ test set, few systems make use of the categories' internal structure that will drive the syntactic derivation during parsing. …

A Photogrammetry-based Framework to Facilitate Image-based Modeling and Automatic Camera Tracking


Authors:  SebastianBullinger, ChristophBodensteiner, MichaelArens....
Published date-12/02/2020
Tasks:  StructurefromMotion

Abstract: We propose a framework that extends Blender to exploit Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques for image-based modeling tasks such as sculpting or camera and motion tracking. …

Single-Shot Lightweight Model For The Detection of Lesions And The Prediction of COVID-19 From Chest CT Scans


Authors:  AramTer-Sarkisov....
Published date-12/02/2020
Tasks:  COVID-19Diagnosis

Abstract: We introduce a lightweight model based on Mask R-CNN with ResNet18 and ResNet34 backbone models that segments lesions and predicts COVID-19 from chest CT scans in a single shot. The …

Learning Spatial Attention for Face Super-Resolution


Authors:  ChaofengChen, DihongGong, HaoWang....
Published date-12/02/2020
Tasks:  ImageSuper-Resolution, Multi-TaskLearning, SSIM, SuperResolution, Super-Resolution

Abstract: General image super-resolution techniques have difficulties in recovering detailed face structures when applying to low resolution face images. Recent deep learning based methods tailored for face images have achieved improved …

Residuals-based distributionally robust optimization with covariate information


Authors:  RohitKannan, GüzinBayraksan, JamesR.Luedtke....
Published date-12/02/2020

Abstract: We consider data-driven approaches that integrate a machine learning prediction model within distributionally robust optimization (DRO) given limited joint observations of uncertain parameters and covariates. Our framework is flexible in …

Algebraically-Informed Deep Networks (AIDN): A Deep Learning Approach to Represent Algebraic Structures


Authors:  MustafaHajij, GhadaZamzmi, MatthewDawson....
Published date-12/02/2020

Abstract: One of the central problems in the interface of deep learning and mathematics is that of building learning systems that can automatically uncover underlying mathematical laws from observed data. In …

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