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Found 4 papers, 2 papers with code
Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning
Specifically, we design an office navigation environment, where the agent's goal is to find a particular office, and office locations differ in different buildings (i. e., tasks).
Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins
Context enrichment, or rebuilding fragmented context, using keyless joins is an implicit or explicit step in machine learning (ML) pipelines over structured data sources.
Leveraging Organizational Resources to Adapt Models to New Data Modalities
As applications in large organizations evolve, the machine learning (ML) models that power them must adapt the same predictive tasks to newly arising data modalities (e. g., a new video content launch in a social media application requires existing text or image models to extend to video).
CrossTrainer: Practical Domain Adaptation with Loss Reweighting
Domain adaptation provides a powerful set of model training techniques given domain-specific training data and supplemental data with unknown relevance.
