Spain

ECAI 2020

Predicting Long-Term Skeletal Motions by a Spatio-Temporal Hierarchical Recurrent Network

motion prediction

lie algebra

neural network

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

technical paper

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.

Bidding in Spades

Adversarial Domain Adaptation Being Aware of Class Relationships

A Novel Framework with Information Fusion and Neighborhood Enhancement for User Identity Linkage

A Graph-based Measurement for Text Imbalance Classiﬁcation

MobiSenseUs - Inferring Objective and Subjective Well-Being from Mobile Data

Matching Questions and Answers in Dialogues from Online Forums

Sequence Prediction Model for Aspect-level Sentiment Classification

Task-Aware Performance Prediction for Efficient Architecture Search

Graphical Granger Causality by Information-Theoretic Criteria

Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data

https://d3smihljt9218e.cloudfront.net/transcript/957/subtitle/9d7e831cbb6a464230de0eccfc5c9a7a.vtt

English (automatic)

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Bidding in Spades

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Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video

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Bidding in Spades

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Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video

Downloads