Search papers, labs, and topics across Lattice.
Affiliation:
3
0
5
KENDO achieves up to 5x faster Bayesian optimization and 27x faster active learning while enhancing predictive performance.
Achieving up to 95% success in robotic manipulation tasks, this framework redefines the boundaries of sample efficiency and policy performance in real-world online reinforcement learning.
Entity type information can dramatically boost SciNER performance, enabling LLMs to match fully supervised models without extensive human input.
