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Graph convolutions that can finally model local structure


Authors:  RémyBrossard, OrielFrigo, DavidDehaene....
Published date-11/30/2020

Abstract: Despite quick progress in the last few years, recent studies have shown that modern graph neural networks can still fail at very simple tasks, like detecting small cycles. This hints …

Deep Implicit Templates for 3D Shape Representation


Authors:  ZerongZheng, TaoYu, QionghaiDai....
Published date-11/30/2020
Tasks:  3DShapeRepresentation

Abstract: Deep implicit functions (DIFs), as a kind of 3D shape representation, are becoming more and more popular in the 3D vision community due to their compactness and strong representation power. …

A Targeted Universal Attack on Graph Convolutional Network


Authors:  JiazhuDai, WeifengZhu, XiangfengLuo....
Published date-11/29/2020
Tasks:  AdversarialAttack

Abstract: Graph-structured data exist in numerous applications in real life. As a state-of-the-art graph neural network, the graph convolutional network (GCN) plays an important role in processing graph-structured data. However, a …

Intrinsic Knowledge Evaluation on Chinese Language Models


Authors:  ZhiruoWang, RenfenHu....
Published date-11/29/2020

Abstract: Recent NLP tasks have benefited a lot from pre-trained language models (LM) since they are able to encode knowledge of various aspects. However, current LM evaluations focus on downstream performance, …

Intrinsic Decomposition of Document Images In-the-Wild


Authors:  SagnikDas, HassanAhmedSial, KeMa....
Published date-11/29/2020
Tasks:  IntrinsicImageDecomposition, OpticalCharacterRecognition, ShadowRemoval

Abstract: Automatic document content processing is affected by artifacts caused by the shape of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised methods on real data are impossible due …

Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models


Authors:  MeiqiGuo, RebeccaHwa, Yu-RuLin....
Published date-11/29/2020

Abstract: We investigate the impact of political ideology biases in training data. Through a set of comparison studies, we examine the propagation of biases in several widely-used NLP models and its …

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