Spain

ECAI 2020

Exploiting Incomparability in Solution Dominance: Improving General Purpose Constraint-Based Mining

dominance programming

constraint modelling

constraint programming

pattern mining

itemset mining

data mining

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

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.

The Impact of Linguistic Knowledge in Different Strategies to Learn Cross-Lingual Distributional Models

Speeding up Word Mover’s Distance and its variants via properties of distances between embeddings

Learning First-Order Symbolic Representations from the Structure of the State Space

Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives

Basic Utility Theory for Belief Functions

Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation

Mid-weight Image Super-Resolution with Bypass Connection Attention Network

Learning to Reuse Translations: Guiding Neural Machine Translation with Examples

Metric Learning for Ordered Labeled Trees with pq-grams

An Efficient Agreement Mechanism in CapsNets By Pairwise Product

https://d3smihljt9218e.cloudfront.net/transcript/1048/subtitle/169df9d2ebbf6979b343788179a66380.vtt

English (automatic)

Exploiting Incomparability in Solution Dominance: Improving General Purpose Constraint-Based Mining

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The Impact of Linguistic Knowledge in Different Strategies to Learn Cross-Lingual Distributional Models

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Exploiting Incomparability in Solution Dominance: Improving General Purpose Constraint-Based Mining

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Next from ECAI 2020

The Impact of Linguistic Knowledge in Different Strategies to Learn Cross-Lingual Distributional Models

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A Noise-tolerant Differentiable Learning Approach for Single Occurrence Regular Expression with Interleaving

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