New 8.9M-event dataset from Pendle, Uniswap v3, Aave and Morpho plus UWM loss yields 56.41% average reduction in time-prediction error for TPP models while preserving event-type accuracy.
Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network.Physica D: Nonlinear Phenomena, 404:132306
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
method 1polarities
use method 1representative citing papers
A review of multi-fidelity surrogates from co-kriging to neural networks for composite mechanics, with applications in prediction, optimization, and workflow integration.
citing papers explorer
-
Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols
New 8.9M-event dataset from Pendle, Uniswap v3, Aave and Morpho plus UWM loss yields 56.41% average reduction in time-prediction error for TPP models while preserving event-type accuracy.
-
Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks
A review of multi-fidelity surrogates from co-kriging to neural networks for composite mechanics, with applications in prediction, optimization, and workflow integration.