Sequential Monte Carlo learning for time series structure discovery
This paper presents a new approach to automatically discovering accurate models of
complex time series data. Working within a Bayesian nonparametric prior over a symbolic …
complex time series data. Working within a Bayesian nonparametric prior over a symbolic …
Recursive Monte Carlo and variational inference with auxiliary variables
AK Lew, M Cusumano-Towner… - Uncertainty in …, 2022 - proceedings.mlr.press
A key design constraint when implementing Monte Carlo and variational inference
algorithms is that it must be possible to cheaply and exactly evaluate the marginal densities …
algorithms is that it must be possible to cheaply and exactly evaluate the marginal densities …
Rethinking Deep CNN Training: A Novel Approach for Quality-Aware Dataset Optimization
B Rusyn, O Lutsyk, R Kosarevych, O Kapshii… - IEEE …, 2024 - ieeexplore.ieee.org
An informativeness of the data has always been of great interest within a machine learning
community. Nowadays, with a skyrocketing advancement of the artificial intelligence and …
community. Nowadays, with a skyrocketing advancement of the artificial intelligence and …
Scalable Structure Learning, Inference, and Analysis with Probabilistic Programs
FAK Saad - 2022 - dspace.mit.edu
How can we automate and scale up the processes of learning accurate probabilistic models
of complex data and obtaining principled solutions to probabilistic inference and analysis …
of complex data and obtaining principled solutions to probabilistic inference and analysis …