Spain

ECAI 2020

IAD: A Benchmark Dataset and a New Method for Illegal Advertising Classification

illegal advertising

natural language process

attention mechanism

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

Preferences over rankings and how to control them using rewards

Monte Carlo Information-Oriented Planning

Enhanced foraging in robot swarms using collective Lévy walks

General Supervision via Probabilistic Transformations

Generating Ensembles of Multi-Label Classifiers Using Cooperative Coevolutionary Algorithms

Heuristics for Link Prediction in Multiplex Networks

SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience

Ensemble Knowledge Distillation for Learning Improved and Efficient Networks

EPNE: Evolutionary Pattern Preserving Network Embedding

Lifting Majority to Unanimity in Opinion Diffusion

https://d3smihljt9218e.cloudfront.net/transcript/995/subtitle/9bdc149589a06a2b1b7506216f36e0f0.vtt

English (automatic)

IAD: A Benchmark Dataset and a New Method for Illegal Advertising Classification

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Preferences over rankings and how to control them using rewards

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Simplifying Graph Attention Networks with Source-Target Separation

IAD: A Benchmark Dataset and a New Method for Illegal Advertising Classification

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Next from ECAI 2020

Preferences over rankings and how to control them using rewards

Similar lecture

Simplifying Graph Attention Networks with Source-Target Separation

Downloads