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Found 5 papers, 2 papers with code
Foundational Inference Models for Dynamical Systems
The resulting models, which we call foundational inference models (FIM), can be (i) copied and matched along the time dimension to increase their resolution; and (ii) copied and composed to build inference models of any dimensionality, without the need of any finetuning.
Neural Markov Jump Processes
Markov jump processes are continuous-time stochastic processes with a wide range of applications in both natural and social sciences.
Dynamic Review-based Recommenders
In the present work we leverage the known power of reviews to enhance rating predictions in a way that (i) respects the causality of review generation and (ii) includes, in a bidirectional fashion, the ability of ratings to inform language review models and vice-versa, language representations that help predict ratings end-to-end.
Recurrent Point Review Models
Specifically, we use the dynamic representations of recurrent point process models, which encode the history of how business or service reviews are received in time, to generate instantaneous language models with improved prediction capabilities.
Recurrent Point Processes for Dynamic Review Models
Recent progress in recommender system research has shown the importance of including temporal representations to improve interpretability and performance.
