Spain

ECAI 2020

Verification of Recurrent Neural Networks for Cognitive Tasks via Reachability Analysis

cognitive neuroscience

formal verification

neural networks

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

Reuse, Reduce and Recycle: Optimizing Reiter’s HS-Tree for Sequential Diagnosis

On Partial Multi-Task Learning

Toward Metrics for Differentiating Out-of-Distribution Sets

Planning for Compilation of a Quantum Algorithm for Graph Coloring

Document and Word Representations Generated by Graph Convolutional Network and BERT for Short Text Classification

Mining Insights from Large-scale Corpora Using Fine-tuned Language Models

Branch Location Problems with Maximum Satisfiability

Simplifying Graph Attention Networks with Source-Target Separation

Private Knowledge Transfer via Model Distillation with Generative Adversarial Networks

From Eigentrust to a Trust-measuring Algorithm in the Max-Plus Algebra

https://d3smihljt9218e.cloudfront.net/transcript/1144/subtitle/7f919d56b16cf3a458bfa49394b788bd.vtt

English (automatic)

Downloads

Next from ECAI 2020

Reuse, Reduce and Recycle: Optimizing Reiter’s HS-Tree for Sequential Diagnosis

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Verification of Neural Networks: Enhancing Scalability through Pruning

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Next from ECAI 2020

Reuse, Reduce and Recycle: Optimizing Reiter’s HS-Tree for Sequential Diagnosis

Similar lecture

Verification of Neural Networks: Enhancing Scalability through Pruning

Downloads