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Found 3 papers, 2 papers with code
Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning
The results demonstrate that our approach achieves high prediction accuracy, facilitates interpretable feature extraction, and enables multicriteria design optimization, leading to significant reduction of computing power and time (10x reduction of required DFT calculations) in high-performance catalyst discovery.
Probabilistic Modeling for Sequences of Sets in Continuous-Time
In this work, we develop a general framework for modeling set-valued data in continuous-time, compatible with any intensity-based recurrent neural point process model.
Probabilistic Querying of Continuous-Time Event Sequences
Continuous-time event sequences, i. e., sequences consisting of continuous time stamps and associated event types ("marks"), are an important type of sequential data with many applications, e. g., in clinical medicine or user behavior modeling.
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