MIP uses memory-bank prompts and variance-based invariant training to improve urban flow prediction under distribution shifts on METR-LA and NYCBike1.
Stone: A spatio-temporal ood learning framework kills both spatial and temporal shifts,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2024 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts
MIP uses memory-bank prompts and variance-based invariant training to improve urban flow prediction under distribution shifts on METR-LA and NYCBike1.