Automating Deep Learning Design using Neural Architecture Search
Deep Learning has had tremendous success in multiple tasks, mainly due to human expertise and ingenuity. Over the years, novel and optimized methods have been proposed, which incrementally improve the efficiency of the Deep Learning algorithms. However, designing optimal models, and applying existing ones to new tasks is extremely hard, requiring tremendous human effort, expertise and trial and error. This is a clear barrier to the mass application of Deep Learning solutions, such as Convolutional Neural Networks. Thus, Neural Architecture Search (NAS) emerged as a logical solution, where the goal is to automatically design optimal networks for a given problem, by automating the design and evaluation processes. NAS-designed networks have surpassed human-design networks, and shown to be efficient in multiple tasks. However, NAS methods still have drawbacks, such as human biases introduced during the development of the methods, and huge computational costs. In this talk, we will introduce NAS concepts, provide an introduction to the field, and show how NAS methods can still be improved, and leveraged in combination with human-crafted networks.
Vasco Lopes received the B.Sc. and M.Sc. degrees in computer science and engineering in 2017 and 2019, respectively, from the University of Beira Interior, Covilhã, Portugal, where he is currently working toward a Ph.D. degree in Computer Vision and Neural Architecture Search. Vasco has worked on several research projects, such as uPATO, RobotChain, and INDTech 4.0, where he developed Machine Learning solutions for the automation of PSA production lines. Since 2019 he has collaborated as a Teaching Assistant in UBI, and in 2020, he co-founded DeepNeuronic, a company that develops Computer Vision solutions to automate daily processes. Vasco was the recipient of the APRP Best Dissertation in Pattern Recognition 2019 Award.