Hierarchical Multi-Instance Learning
Šimon explains the innovative approach of Hierarchical Multi-Instance Learning (HTML), which allows for direct ingestion of complex data without the need for traditional feature vector transformation. By inferring schemas from rich datasets, this method processes data hierarchically, ultimately generating a learnable embedding for structured data like JSON files. The conversation delves into the concept of multiple instance learning, highlighting its evolution and the ability to handle nested sets of vectors for more nuanced predictions.In this clip
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