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Neural decoding from stereotactic EEG: accounting for electrode variability across subjects
Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining data across multiple subjects is crucial.
Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm
This paper explores the realm of infinite horizon average reward Constrained Markov Decision Processes (CMDPs).
Learning Elastic Costs to Shape Monge Displacements
Given a source and a target probability measure, the Monge problem studies efficient ways to map the former onto the latter. This efficiency is quantified by defining a cost function between source and target data.
The Sea Surface Height Edition J. Emmanuel Johnson
The ocean is a crucial component of the Earth's system.
Appendix A Prompt Retrieval A.1 Implementation Details
V ariable batch size According to our budget decay scheme, the total budget (200 human annotations + 800 GPT -3.5 annotations) allocated for human annotation is 100, 50, 25, 13 (rounded up from
