Transformer neural processes: Uncertainty-aware meta learning via sequence modeling
Latent bottlenecked attentive neural processes
Neural Processes (NPs) are popular methods in meta-learning that can estimate predictive
uncertainty on target datapoints by conditioning on a context dataset. Previous state-of-the …
uncertainty on target datapoints by conditioning on a context dataset. Previous state-of-the …
NLI4CT: Multi-evidence natural language inference for clinical trial reports
How can we interpret and retrieve medical evidence to support clinical decisions? Clinical
trial reports (CTR) amassed over the years contain indispensable information for the …
trial reports (CTR) amassed over the years contain indispensable information for the …
Task-agnostic online reinforcement learning with an infinite mixture of gaussian processes
Continuously learning to solve unseen tasks with limited experience has been extensively
pursued in meta-learning and continual learning, but with restricted assumptions such as …
pursued in meta-learning and continual learning, but with restricted assumptions such as …