Spain

ECAI 2020

PushNet: Efficient and Adaptive Neural Message Passing

graph neural networks

semi-supervised learning

deep learning

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

technical paper

Point Cloud Segmentation with Deep Reinforcement Learning

Algebraic Circuits for Decision Theoretic Inference and Learning

AI Paradigms and AI Safety: Mapping Artefacts and Techniques to Safety Issues

Neighborhood-based Pooling for Population-level Label Distribution Learning

Decoding the Imitation Security Game: Handling Attacker Imitative Behavior Deception

Tackling Imitative Attacker Deception in Repeated Bayesian Stackelberg Security Games

STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment

Item Response Theory to Estimate the Latent Ability of Speech Synthesizers

New computational models for the Choquet Integral

What makes a Better Companion? Towards Social & Engaging Peer Learning

https://d3smihljt9218e.cloudfront.net/transcript/1129/subtitle/74d2b684c92c911aa8bb4d6ac4137bb8.vtt

English (automatic)

PushNet: Efficient and Adaptive Neural Message Passing

Downloads

Next from ECAI 2020

Point Cloud Segmentation with Deep Reinforcement Learning

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Simplifying Graph Attention Networks with Source-Target Separation

PushNet: Efficient and Adaptive Neural Message Passing

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Next from ECAI 2020

Point Cloud Segmentation with Deep Reinforcement Learning

Similar lecture

Simplifying Graph Attention Networks with Source-Target Separation

Downloads