|Title:||Options for single-pass and single-shot learning mechanisms|
As well as being a notable feature of biological learning mechanisms and therefore worthy of study in its own right, single-shot and single-pass learning offers many advantages including (potentially huge) gains in speed and energy use, as well as continuous and adaptive learning. I would like to explore the options primarily in the context of - but not limited to - neuromorphic hardware and neurally-inspired learning and inference algorithms.
It may be useful to describe relevant aspects of the recently published BitBrain algorithm as one example.
|Schedule ID||start time||end time||location|
|338||May 02 2023, 14:00||May 02 2023, 15:00||Sala panorama|
|389||May 08 2023, 16:00||May 08 2023, 17:00||Lecture room|
|372||May 05 2023, 14:00||May 05 2023, 15:00||Lecture room|