Learning with Limited Labeled Data – Shioulin Sam, PhD | ODSC Europe 2019
Supervised machine learning requires large labeled datasets – a prohibitive limitation in many real-world applications. What if machines could learn with fewer labeled examples? This video focuses on two enabling techniques. First, we explore an algorithmic solution that relies on collaboration between humans and machines to label smartly. Second, we dig into recent advancements that enable machines to understand language even when there is limited labeled data.