Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Variational Implicit Processes
Authors: Chao Ma, Yingzhen Li, Jose Miguel Hernandez-Lobato
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that VIPs return better uncertainty estimates and lower errors over existing inference methods for challenging models such as Bayesian neural networks, and Gaussian processes. |
| Researcher Affiliation | Collaboration | 1Department of Engineering, University of Cambridge, Cambridge, UK 2Microsoft Research Cambridge, Cambridge, UK. |
| Pseudocode | Yes | Algorithm 1 Variational Implicit Processes (VIP) |
| Open Source Code | No | The paper does not contain an explicit statement providing concrete access to source code for the methodology described. |
| Open Datasets | Yes | We use an IP with a Bayesian neural network (1-10-10-1 architecture) as the prior. We use α = 0 for the wake-step training. We also compare VIP with the exact full GP with optimized compositional kernel (RBF+Periodic), and another BNN with identical architecture but trained using variational dropout (VDO) with dropout rate p = 0.99 and length scale l = 0.001. The (hyper-)parameters are optimized using 500 epochs (batch training) with Adam optimizer (learning rate = 0.01). ... We compare the VIP (α = 0) with a variationally sparse GP (SVGP, 100 inducing points), an exact GP and VDO on the solar irradiance dataset (Lean et al., 1995). ... using real-world multivariate regression datasets from the UCI data repository (Lichman et al., 2013). ... Harvard Clean Energy Project Data, the world s largest materials high-throughput virtual screening effort (Hachmann et al., 2014). |
| Dataset Splits | Yes | The observational noise variance for VIP and VDO is tuned over a validation set, as detailed in Appendix F. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' but does not specify software names with version numbers for its dependencies. |
| Experiment Setup | Yes | The (hyper-)parameters are optimized using 500 epochs (batch training) with Adam optimizer (learning rate = 0.01). |