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On Generalization of Graph Autoencoders with Adversarial

2021.ecmlpkdd.org

ational graph autoencoder (VGAE) We conduct extensive experiments on three main applications ie link prediction node clustering graph - Information from 2021.ecmlpkdd.org

Information accuracy 100%
Read more from 2021.ecmlpkdd.org information was found here: https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_604.pdf

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An optimized NL2SQL system for enterprise data mart ECML

2021.ecmlpkdd.org

our research on WikiSQL data which we extended to support multitable scenarios via our unique table expand process The data simulation - Information from 2021.ecmlpkdd.org

Information accuracy 100%
Read more from 2021.ecmlpkdd.org information was found here: https://2021.ecmlpkdd.org/wp-content/uploads/2021/09/sub_767.pdf

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Keynote Speakers ECML PKDD 2021

2021.ecmlpkdd.org

Joaquin Quiñonero Candela Facebook Talk AI fairness in practice In this talk Ill share learnings from my journey from deploying ML at Facebook scale to - Information from 2021.ecmlpkdd.org

Information accuracy 100%
Read more from 2021.ecmlpkdd.org information was found here: https://2021.ecmlpkdd.org/?page_id=1686

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On Generalization of Graph Autoencoders with Adversarial

2021.ecmlpkdd.org

powerful node embedding methods: graph autoencoder (GAE) and vari to control the strength of regularization for adversarial robustness [33] Besides - Information from 2021.ecmlpkdd.org

Information accuracy 100%
Read more from 2021.ecmlpkdd.org information was found here: https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_604.pdf

Adaptive Optimizers with Sparse Group Lasso for Neural

2021.ecmlpkdd.org

the sparse group lasso to a family of adaptive optimizers in deep learning Gupta V Koren T Singer Y: Shampoo: Preconditioned stochastic tensor - Information from 2021.ecmlpkdd.org

Information accuracy 100%
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On Generalization of Graph Autoencoders with Adversarial

2021.ecmlpkdd.org

ular graph autoencoder (GAE) [102427] and graph variational autoencoder For standard training GAE and VGAE we run the official Pytorch geometric - Information from 2021.ecmlpkdd.org

Information accuracy 100%
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Iterated Matrix Reordering ECML PKDD 2021

2021.ecmlpkdd.org

order for the rows and the columns that is the use of appropriate matrix larity matrix D Each entry Dij represents the distance between row i and j - Information from 2021.ecmlpkdd.org

Information accuracy 100%
Read more from 2021.ecmlpkdd.org information was found here: https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/paper.pdf

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Checking Robustness of Representations Learned by Deep

2021.ecmlpkdd.org

Keywords: deep neural networks · robust representations · spurious Carlini N Wagner D: Towards evaluating the robustness of neural networks In: - Information from 2021.ecmlpkdd.org

Information accuracy 100%
Read more from 2021.ecmlpkdd.org information was found here: https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_987.pdf

Augmenting OpenDomain Event Detection with Synthetic

2021.ecmlpkdd.org

Abstract Opendomain event detection (ODED) aims to identify event mentions of all possible types in text A challenge for ODED research - Information from 2021.ecmlpkdd.org

Information accuracy 100%
Read more from 2021.ecmlpkdd.org information was found here: https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_954.pdf

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The Bures Metric for Generative Adversarial Networks ECML

2021.ecmlpkdd.org

BuresGANs performances on synthetic data artificial and real images are com pared with the standard DCGAN [33] WGANGP MDGAN Unrolled GAN VEEGAN GDPP - Information from 2021.ecmlpkdd.org

Information accuracy 100%
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ZeroShot Scene Graph Relation Prediction through

2021.ecmlpkdd.org

The code is available at https://githubcom/Wayfear/Coacher Keywords: Scene graph generation · Relation prediction · Zeroshot · Commonsense · Knowledge graph - Information from 2021.ecmlpkdd.org

Information accuracy 100%
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SelfSupervised MultiTask Representation Learning for

2021.ecmlpkdd.org

Keywords: SelfSupervised Learning · MultiTask Learning · Medical Image Segmentation 1 Introduction Fueled by the recent success of convolutional neural - Information from 2021.ecmlpkdd.org

Information accuracy 100%
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“The End of History” for Natural Language Processing?

2021.ecmlpkdd.org

Transformer architecture that would increase its expressiveness which Together the training and the testing sets contain 6129 annotated spans While - Information from 2021.ecmlpkdd.org

Information accuracy 100%
Read more from 2021.ecmlpkdd.org information was found here: https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/ECML_Transformers__the_end_of_history.pdf

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Read more from 2021.ecmlpkdd.org

EDUCATION On Generalization of Graph Autoencoders with Adversarial

EDUCATION An optimized NL2SQL system for enterprise data mart ECML

EDUCATION Keynote Speakers ECML PKDD 2021

EDUCATION On Generalization of Graph Autoencoders with Adversarial

EDUCATION Adaptive Optimizers with Sparse Group Lasso for Neural

EDUCATION On Generalization of Graph Autoencoders with Adversarial

EDUCATION Iterated Matrix Reordering ECML PKDD 2021

EDUCATION Checking Robustness of Representations Learned by Deep

EDUCATION Augmenting OpenDomain Event Detection with Synthetic

EDUCATION The Bures Metric for Generative Adversarial Networks ECML

EDUCATION ZeroShot Scene Graph Relation Prediction through

EDUCATION SelfSupervised MultiTask Representation Learning for

EDUCATION “The End of History” for Natural Language Processing?

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On Generalization of Graph Autoencoders with Adversarial

An optimized NL2SQL system for enterprise data mart ECML

Keynote Speakers ECML PKDD 2021

On Generalization of Graph Autoencoders with Adversarial

Adaptive Optimizers with Sparse Group Lasso for Neural

On Generalization of Graph Autoencoders with Adversarial

Iterated Matrix Reordering ECML PKDD 2021

Checking Robustness of Representations Learned by Deep

Augmenting OpenDomain Event Detection with Synthetic

The Bures Metric for Generative Adversarial Networks ECML

ZeroShot Scene Graph Relation Prediction through

SelfSupervised MultiTask Representation Learning for

“The End of History” for Natural Language Processing?