Deep learning is a topic of broad interest, to both researchers who develop new architectures and theories, and engineers who apply deep learning models to real world tasks, including but not limited to brain-like computing and pattern recognition. Brain-like computing combines computational techniques with cognitive principles and systems resembling the human brain. Meanwhile, pattern recognition is a conventional area of artificial intelligence, which focuses on the recognition of patterns and regularities in data. Brain-like computing and pattern recognition have been greatly improved by deep learning in the last few years, but many challenges still remain, such as limited computing resources and adversarial attacks. This special session aims at bringing together researchers in areas relevant to deep learning, to discuss the utility of deep learning for brain-like computing and pattern recognition, the advances, the challenges we face, and to brainstorm about new solutions and directions.
Papers submitted to this Special Session are reviewed according to the same rules as the submissions to the regular sessions of WCCI 2020. Authors who submit papers to this session are invited to select “Special Session IJCNN-35”. Submissions to regular and special sessions follow identical format, instructions, deadlines and procedures of the other papers.