Spain

ECAI 2020

Knowledge Distillation for Model-Agnostic Meta-Learning

few-shot learning

meta-learning

knowledge distillation

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!<br><br><br>

To gain access to this event page you are required to register.<br> Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

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.

A Logic of Explicit and Implicit Distributed Belief

New randomized strategies for the color coding algorithm

MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs

Outside-the-Box: Abstraction-Based Monitoring of Neural Networks

Span-based Joint Entity and Relation Extraction with Transformer Pre-training

Modeling the Incongruity between Sentence  Snippets for Sarcasm Detection

Dynamics in Abstract Argumentation Frameworks with Recursive Attack and Support Relations

Analysis of reduced costs filtering for alldifferent and minimum weight alldifferent global constraints

Automatic Generation of Typicality Measures for Spatial Language in Grounded Settings

Speeding Up CDCL Inference With Duplicate Learnt Clauses

https://d3smihljt9218e.cloudfront.net/transcript/1116/subtitle/79161abc6e337b6c6af9d1cf8b588ff1.vtt

English (automatic)

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A Logic of Explicit and Implicit Distributed Belief

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Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model Compression

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Next from ECAI 2020

A Logic of Explicit and Implicit Distributed Belief

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Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model Compression

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