Predicate Exchange: Inference with Declarative Knowledge
Authors: Zenna Tavares, Javier Burroni, Edgar Minasyan, Armando Solar-Lezama, Rajesh Ranganath
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement predicate exchange as a language agnostic tool which performs a nonstandard execution of a probabilistic program. We demonstrate the approach on sequence models of health and inverse rendering. |
| Researcher Affiliation | Academia | 1MIT, USA 2College of Information and Computer Science, University of Massachusetts, Amherst, USA. 3Princeton University, USA 4NYU, USA. Correspondence to: Zenna Tavares <zenna@mit.edu>. |
| Pseudocode | Yes | Algorithm 1 Soft Execution: softexecute(π, α, D) Algorithm 2 Predicate Exchange: predexchange |
| Open Source Code | Yes | For a concrete implementation, we build predicate exchange into the OMEGA probabilistic programming language1 (Tavares et al., 2019), 1OMEGA is available at http://github.com/zenna/Omega.jl |
| Open Datasets | Yes | We compare the independent RNN model to the one with declarative knowledge on a second patient from Physionet (Moody et al., 2001). |
| Dataset Splits | No | The paper mentions learning from a 'partial trajectory' or using 'first ten data points' for the glucose model, but does not provide specific train/validation/test split percentages or counts, nor does it specify a separate validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions software components like 'No U-Turn Sampler', 'Hamiltonion Monte Carlo', 'reverse-mode automatic differentiation', and 'OMEGA probabilistic programming language', but does not specify their version numbers. |
| Experiment Setup | Yes | In practice, we simulate four chains with α logarithmically spaced between log10(α1) = 5 and log10(αM) = 5, and swap states that are adjacent in temperature (α1 with α2, α2 with α3, etc) every 10 iterations. (...) For finite dimensional continuous models we use the No U-Turn Sampler (Hoffman & Gelman, 2014), a variant of Hamiltonion Monte Carlo (HMC). |