Spain

ECAI 2020

EPNE: Evolutionary Pattern Preserving Network Embedding

network embedding; temporal networks; evolutionary pattern

**Epigenetic Inheritance across species**

We are happy to invite you to register for the GECKO Conference – inspired by the Gametic Epigenetics Consortium Against Obesity (hence, GECKO). Professor Romain Barrès Head of the GECKO Consortium, and his research group will be hosting the conference, which will take place virtually between **May 25-28, 2021**. **Sessions will be live daily from 8.00-10.30 and 17.00-19.30 CET.**

The theme of the conference is intentionally broad. We will have sessions dedicated to the technical aspects of studying epigenetic inheritance, how we can use a comparative approach to understand inheritance mechanisms, the evolutionary explanations for epigenetic inheritance and a session focused on how our research could influence reproductive biology. 

We already have a fantastic line-up of speakers, including Noora Kotaja (University of Turku), Qi Chen (University of California), Michael Wilson (University of Toronto), Isabelle Mansuy (University and ETH Zurich), Oliver Rando (University of Massachusetts), Anita Öst (Linköping University) and Tobias Uller (Lund University).

The conference format will include panel discussions, short talks selected from submitted abstracts, poster sessions and a set of short and sharp talks to accompany these.

The deadline for submitting an abstract to be considered for a talk or poster is **May 4th .** 
Those selected to present will be informed in the following week, with **final posters due May 17th .**
![](https://assets.underline.io/uploads/markdown_image/1/image/ea7614ef58a8f691e8295529020987ea.png)
[![](https://assets.underline.io/uploads/markdown_image/1/image/6b2bc323e45f696b7cd29d1650a5e2cc.jpg)](https://zmxuhia1.paperform.co/)

![](https://assets.underline.io/uploads/markdown_image/1/image/ea7614ef58a8f691e8295529020987ea.png)

[![](https://assets.underline.io/uploads/markdown_image/1/image/ac0bcdd33edcb55782729d3b03c29bdc.png)](https://drive.google.com/file/d/1AzPiQQYPvStIzAApkEk4AqejZs6HHhEc/view?usp=sharing)

### ** Program at a Glance **
![](https://assets.underline.io/uploads/markdown_image/1/image/17617a252626cfe73fc1a894e7d68ab1.png)
![](https://assets.underline.io/uploads/markdown_image/1/image/ea7614ef58a8f691e8295529020987ea.png)


# About GECKO

The Gametic Epigenetics Consortium Against Obesity (GECKO) is a research consortium funded by a Novo Nordisk Foundation Challenge grant from 2019 to 2025. The main goal of the consortium is to understand the effect of diet on the epigenetic signatures that are passed through sperm to offspring, and the effect these signatures have on the developing offspring and future generations. 

The GECKO is comprised of three leading research groups headed by Professor Romain Barrès at the University of Copenhagen, Professor Stephen Simpson at the University of Sydney and Professor Marcelo Nobrega at the University of Chicago.



<iframe width=1280 height=720 frameborder="0" scrolling="no" src="https://screencast-o-matic.com/player/crevlKVV5uC?ff=1&title=0&controls=1" allowfullscreen="true"></iframe>





Find out more information and register to the event **for free** by making an account in Underline.

GECKO Conference 2021

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.

Lifting Majority to Unanimity in Opinion Diffusion

Dynamic thresholding for learning sparse neural networks

A Region Selection Model to Identify Unknown Unknowns in Image Datasets

End-To-End Speech Emotion Recognition Based on Time and Frequency Information Using Deep Neural Networks

Unsupervised Learning of Interpretable Dialog Models

NASS: Optimizing Secure Inference via Neural Architecture Search

A Multi-Task Learning Neural Network for Emotion-Cause Pair Extraction

Building a Multi-domain Neural Machine Translation Model using Knowledge Distillation

QuantumWeighted Model Counting

A Variant of Recurrent Entity Networks for Targeted Aspect-Based Sentiment Analysis

https://d3smihljt9218e.cloudfront.net/transcript/1009/subtitle/67d945888dc5ab6c466dcb2a4672341b.vtt

English (automatic)

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Lifting Majority to Unanimity in Opinion Diffusion

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Lifting Majority to Unanimity in Opinion Diffusion

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