2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL 2024)
Workshop Ⅱ: MLNN 2024
Home / Workshop Ⅱ: MLNN 2024

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- Welcome to MLNN 2024 -

Websites: www.icmlnn.org        Conference Dates: April 19-21, 2024          Conference Venue: Zhuhai, China


As the branch of the 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL 2024), MLNN 2024 will focus on the core theories, key technologies and applications of learning systems and neural networks, covering many sub-fields such as deep learning, computer vision, natural language processing and reinforcement learning. Through invited presentations, keynote speeches, sub-conference presentations, poster presentations and other forms, the latest research results and technological innovations in related fields of academia and industry will be displayed.

This conference will provide an opportunity for participants to deeply understand the cutting-edge trends in the field of learning systems and neural networks, broaden their research horizons, and strengthen academic exchanges and cooperation. It also encourages the exchange of cutting-edge research information in different fields, connects the most advanced academic resources, transforms research results into industrial solutions, gathers talent, technology and capital, and promotes development.


CALL FOR PAPERS -

The topics of interest for submission include, but are not limited to:

Track 1: Neural NetworkTrack 2: Machine Learning

· Deep Neural Network

· Convolutional Neural Network

· Generative Adversarial Network

· Recurrent Neural Networks

· Neural Network Structure

· Neural Symbol Mixing Model

· Interpretability and Visualization Methods of Neural Networks

· Neural Network Snalysis and Research in Medical, Financial, Energy  and other fields


· Width Learning System

· Deep Learning

· Reinforcement Learning

· Learning Transfer

· Knowledge Graph

· Path Planning

· Transfer Learning

· Generative Adversarial Network

· Adversarial Learning

· Dual Learning

· Distributed Learning

· Meta-learning


- See the official website for more information -

Websites: www.icmlnn.org