Spain

ECAI 2020

Learning Variable Ordering Heuristics with Multi-Armed Bandits and Restarts

heuristic search

solvers and tools

constraint satisfaction

constraint programming

bandit algorithms

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.

Learning Joint Job Embeddings using a Job-Oriented Asymmetrical Pairing System

Prioritized Simple Contrapositive Assumption-Based Frameworks

ET-GAN: Cross-Language Emotion Transfer Based on Cycle-Consistent Generative Adversarial Networks

Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy

Search for Better Students to Learn Distilled Knowledge

Learning to continually learn

Behavior based Dynamic Summarization on Product Aspects via Reinforcement Neighbour Selection

From Public Announcements to Asynchronous Announcements

Quantitative Stream Reasoning with Weighted LARS

Algorithms for Dynamic Argumentation Frameworks: An Incremental SAT-Based Approach

https://d3smihljt9218e.cloudfront.net/transcript/1228/subtitle/061374ce41fed0c36dd1798e61a9a639.vtt

English (automatic)

Learning Variable Ordering Heuristics with Multi-Armed Bandits and Restarts

Downloads

Next from ECAI 2020

Learning Joint Job Embeddings using a Job-Oriented Asymmetrical Pairing System

Similar lecture

Solving Explainability Queries with Quantification: The Case of Feature Relevancy

Learning Variable Ordering Heuristics with Multi-Armed Bandits and Restarts

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from ECAI 2020

Learning Joint Job Embeddings using a Job-Oriented Asymmetrical Pairing System

Similar lecture

Solving Explainability Queries with Quantification: The Case of Feature Relevancy

Downloads