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ECAI 2020

Multi-Label Learning with Deep Forest

deep forest

multi-label learning

ensemble learning

deep learning

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!

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 MAIN CONFERENCE

technical paper

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

Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems

A Two-Stage Multi-Objective Deep Reinforcement Learning Framework

Online Probabilistic Interval-based Event Calculus

Saliency Detection with Deformable Convolution and Feature Attention

Multi-Label Learning with Deep Forest

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