Associative-based Few-Shot Learning

Gradient-Based Optimization has achieved impressive results for supervised tasks such as image classification. Unfortunately, these models need hundreds to thousands of training examples per object. This presentation will introduce an Associative Processing Unit chip (APU), which like human brain can learn efficiently using only a few examples per object.

Dr. Avidan Akeribs is VP of GSI Technology's Associative Computing Business Unit. He has over 30 years of experience in parallel computing and In-Place Associative Computing. He has over 25 Granted Patents related to parallel and in-memory associative computing. Dr. Akeribs has a PhD in Applied mathematics & Computer Science from the Weismann Instiitute of Science, Israel. His specialties are Computational Memory, Associative Processing, Parallel Algorithms, and Machine Learning.