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We create a richer model by leveraging two statistical ideas: (1) Bayesian nonparametric modeling, which al- lows us to relax the rigid compositionality of ...
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Abstract. We are interested in learning programs for multiple related tasks given only a few train- ing examples per task. Since the program for a single task ...
A nonparametric hierarchical Bayesian prior over programs which shares statistical strength across multiple tasks is introduced and an MCMC algorithm is ...
Jun 21, 2010 · ABSTRACT. We are interested in learning programs for multiple related tasks given only a few training examples per task. Since the program for a ...
Goal: Programming by Demonstration. If the user demonstrates italicizing the first occurrence, can we generalize to the remaining?
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Learn how to fit and compare ecological models in a Bayesian hierarchical framework, which separates the observation process from the ecological process when ...
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The hierarchical Bayesian framework provides a strong prior that allows us to rapidly infer the characteristics of new environments based on previous ...
Feb 1, 2023 · This paper proposes a hierarchical Bayesian approach to Federated Learning. The proposed Bayesian model makes the block-coordinate descent ...
Bayesian one-shot learning. This paper suggests a hierarchical generative model that can build whole objects from individual parts. The figure below (copied ...
Feb 29, 2024 · This approach extends the principles of Bayesian statistics to accommodate hierarchical relationships among variables, making it a versatile ...