Spain

ECAI 2020

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multi-task

deep learning

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

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.

An ASP semantics for Constraints involving Conditional Aggregates

Implementing Dynamic Answer Set Programming over finite traces

Robust Constraint Acquisition by Sequential Analysis

Strong Refinements for Hard Problems in Argumentation Dynamics

Bridging the Gap Between Training and Inference for Spatio-Temporal Forecasting

Polynomial-time Implicit Learnability in SMT

Generating Natural Language Adversarial Examples on a Large Scale with Generative Models

Joint Modeling of Local and Global Behavior Dynamics for Session-based Recommendation

Learning Chordal Markov Networks via Stochastic Local Search

Learning Variable Ordering Heuristics with Multi-Armed Bandits and Restarts

https://d3smihljt9218e.cloudfront.net/transcript/1217/subtitle/56002b458749a3d93827827c61f7f9b5.vtt

English (automatic)

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An ASP semantics for Constraints involving Conditional Aggregates

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An ASP semantics for Constraints involving Conditional Aggregates

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