Spain

ECAI 2020

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

gaussian markov random fields

spatial and temporal dependency

time series forecasting

transformer

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 2021

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.

Powering Hidden Markov Model by Neural Network based Generative Models

Robust Multi-Instance Learning with Stable Instances

On the Interpretation of Convolutional Neural Networks for Text Classification

JND-GAN: Human-Vision-Systems Inspired Generative Adversarial Networks for Image-to-Image Translation

Multi-Label Learning with Deep Forest

Directional Adversarial Training for Recommender Systems

A Lightweight Recurrent Attention Network for Real-time Guidewire Segmentation and Tracking in Interventional X-ray Fluoroscopy

Continual egocentric object recognition

OpenSMax: Unknown Domain Generation Algorithm Detection

CAMUS: A Framework to Build Formal Specifications for Deep Perception Systems Using Simulators

https://d3smihljt9218e.cloudfront.net/transcript/968/subtitle/97b507f2230688d07f16cb428a4f453d.vtt

English (automatic)

Downloads

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Powering Hidden Markov Model by Neural Network based Generative Models

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Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting

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Next from ECAI 2020

Powering Hidden Markov Model by Neural Network based Generative Models

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Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting

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