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Beyond BackPropagation: Novel Ideas for Training Neural Architectures
Mateusz Malinowski · Grzegorz Swirszcz · Viorica Patraucean · Marco Gori · Yanping Huang · Sindy Löwe · Anna Choromanska

Sat Dec 12 06:00 AM -- 04:30 PM (PST) @
Event URL: https://beyondbackprop.github.io/ »

Is backpropagation the ultimate tool on the path to achieving synthetic intelligence as its success and widespread adoption would suggest?

Many have questioned the biological plausibility of backpropagation as a learning mechanism since its discovery. The weight transport and timing problems are the most disputable. The same properties of backpropagation training also have practical consequences. For instance, backpropagation training is a global and coupled procedure that limits the amount of possible parallelism and yields high latency.

These limitations have motivated us to discuss possible alternative directions. In this workshop, we want to promote such discussions by bringing together researchers from various but related disciplines, and to discuss possible solutions from engineering, machine learning and neuroscientific perspectives.

Author Information

Mateusz Malinowski (DeepMind)

Mateusz Malinowski is a research scientist at DeepMind, where he works at the intersection of computer vision, natural language understanding, and deep learning. He was granted PhD (Dr.-Ing.) with the highest honor (summa cum laude) at Max Planck Institute for Informatics in 2017 in computer vision for his pioneering work on visual question answering, where he proposed the task and developed methods that answer questions about the content of images. Prior to this, he graduated with honors from Saarland University in computer science. Before that, he studied computer science at Wroclaw University in Poland.

Grzegorz Swirszcz (DeepMind)
Viorica Patraucean (DeepMind)
Marco Gori (University of Siena)
Yanping Huang (Google Brain)
Sindy Löwe (University of Amsterdam)
Anna Choromanska (NYU Tandon School of Engineering)

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