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..
Rényi Neural Processes
Authors: Xuesong Wang, He Zhao, Edwin V. Bonilla
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach across multiple benchmarks including regression and image inpainting tasks, and show significant performance improvements of RNPs in real-world problems. Our extensive experiments show consistently better loglikelihoods over state-of-the-art NP models. |
| Researcher Affiliation | Academia | 1CSIRO s Data61, Australia. Correspondence to: Xuesong Wang <EMAIL>. |
| Pseudocode | Yes | A.1. Pseudocode Algorithm 1 R enyi Neural Processes |
| Open Source Code | Yes | Our code is published at https://github.com/csiro-funml/renyineuralprocesses |
| Open Datasets | Yes | We evaluate the proposed method on multiple regression tasks: 1D regression [...] image inpainting [...] on three image datasets: MNIST, SVHN and Celeb A. [...] We also tested TND-D on the Extended MNIST dataset with 47 classes |
| Dataset Splits | Yes | The number of context points is randomly sampled M U(3, 50), and the number of target points is N U(3, 50 M) (Nguyen & Grover, 2022). We choose 100,000 functions for training, and sample another 3,000 functions for testing. [...] The number of context points for inpainting tasks is M U(3, 200) and the target point count is N U(3, 200 M). [...] We choose 20,000 functions for training, and sample another 1,000 functions for evaluation. [...] We use classes 0-10 for meta training and hold out classes 11-46 for meta testing under prior misspecification. |
| Hardware Specification | No | All the models can be trained using a single GPU with 16GB memory. |
| Software Dependencies | No | The paper does not explicitly mention any specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | We set α = 0.7 to train for VI-based RNPs and analogously α = 0.3 for ML-based baselines. [...] The number of samples K for the Monte Carlo is 32 for training and 50 for inference. [...] The input features were normalized to [ 2, 2]. [...] The input coordinates were normalized to [ 1, 1] and pixel intensities were rescaled to [ 0.5, 0.5]. [...] The noise level β is set as 0.3 for both training and testing. |