Spain

ECAI 2020

Powering Hidden Markov Model by Neural Network based Generative Models

sequential

flow

hidden markov model

neural network

generative model

The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. It is supported by the [Association for Uncertainty in Artificial Intelligence (AUAI).](https://www.auai.org/)

The 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021) is held fully online. We have an exciting program consisting of keynote talks by [Susan Murphy](https://www.auai.org/uai2021/program?block=A), [Eric Horvitz](https://www.auai.org/uai2021/program?block=B), [Lenka Zdeborová](https://www.auai.org/uai2021/program?block=C), [Judea Pearl](https://www.auai.org/uai2021/program?block=D) and [Ankur Moitra](https://www.auai.org/uai2021/program?block=E), a town hall meeting, peer-reviewed papers, and three workshops. The main conference is single-track and each paper is show-cased with a live component (either a live discussion and Q&A, or a live lightning talk). All papers are also featured in video presentations and in poster sessions in gather.town. The three parallel workshops on Friday allow for smaller meetings on current topics.

The walkthrough video below shows how you can navigate this site and use the different possibilities for interaction: UAIsland2021 on gather.town for posters and social interactions, the mementor platform for virtual mentoring sessions, and the chat and Q&A systems for discussions about talks and posters.

Have a great UAI 2021! 
<br>
<br>Cassio de Campos and Marloes Maathuis
<br>UAI 2021 Program Chairs
<br>uai2021programchairs@gmail.com

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A ticket is required to access this conference. Please use the provided link to register for the event.

https://auai.org/uai2021/registration

UAI 2021

The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. It is supported by the [Association for Uncertainty in Artificial Intelligence (AUAI).](https://www.auai.org/)

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.

Robust Multi-Instance Learning with Stable Instances

On the Interpretation of Convolutional Neural Networks for Text Classification

JND-GAN: Human-Vision-Systems Inspired Generative Adversarial Networks for Image-to-Image Translation

Multi-Label Learning with Deep Forest

Directional Adversarial Training for Recommender Systems

A Lightweight Recurrent Attention Network for Real-time Guidewire Segmentation and Tracking in Interventional X-ray Fluoroscopy

Continual egocentric object recognition

OpenSMax: Unknown Domain Generation Algorithm Detection

CAMUS: A Framework to Build Formal Specifications for Deep Perception Systems Using Simulators

A Neural Topical Expansion Framework for Unstructured Persona-oriented Dialogue Generation

https://d3smihljt9218e.cloudfront.net/transcript/973/subtitle/3ad261dc8eb4b2420ebbca2cc24c13fa.vtt

English (automatic)

Powering Hidden Markov Model by Neural Network based Generative Models

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Robust Multi-Instance Learning with Stable Instances

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Hierarchical Learning of Hidden Markov Models with Clustering Regularization

Powering Hidden Markov Model by Neural Network based Generative Models

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Robust Multi-Instance Learning with Stable Instances

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Hierarchical Learning of Hidden Markov Models with Clustering Regularization

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