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Found 7 papers, 2 papers with code
Verified Multi-Step Synthesis using Large Language Models and Monte Carlo Tree Search
The base model with VMCTS is even competitive with ChatGPT4 augmented with plugins and multiple re-tries on these problems.
Amortized Rejection Sampling in Universal Probabilistic Programming
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance.
Efficient Bayesian Inference for Nested Simulators
We introduce two approaches for conducting efficient Bayesian inference in stochastic simulators containing nested stochastic sub-procedures, i. e., internal procedures for which the density cannot be calculated directly such as rejection sampling loops.
Querying Word Embeddings for Similarity and Relatedness
Word embeddings obtained from neural network models such as Word2Vec Skipgram have become popular representations of word meaning and have been evaluated on a variety of word similarity and relatedness norming data.
Faithful Inversion of Generative Models for Effective Amortized Inference
Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently.
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning
We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning.
Composing inference algorithms as program transformations
Probabilistic inference procedures are usually coded painstakingly from scratch, for each target model and each inference algorithm.
