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..
Approximate Inference Turns Deep Networks into Gaussian Processes
Authors: Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present empirical results where we visualize the feature-map obtained on benchmark datasets such as MNIST and CIFAR, and demonstrate their use for DNN hyperparameter tuning. The code to reproduce our results is available at https://github.com/team-approx-bayes/dnn2gp. |
| Researcher Affiliation | Academia | Mohammad Emtiyaz Khan RIKEN Center for AI Project Tokyo, Japan EMAIL Alexander Immer* EPFL Lausanne, Switzerland EMAIL Ehsan Abedi* EPFL Lausanne, Switzerland EMAIL Maciej Korzepa* Technical University of Denmark Kgs. Lyngby, Denmark EMAIL |
| Pseudocode | No | The paper does not contain explicitly labeled pseudocode or algorithm blocks. Algorithm descriptions (e.g., RMSprop, VON, VOGGN) are given in paragraph text and mathematical equations. |
| Open Source Code | Yes | The code to reproduce our results is available at https://github.com/team-approx-bayes/dnn2gp. |
| Open Datasets | Yes | We present empirical results where we visualize the feature-map obtained on benchmark datasets such as MNIST and CIFAR [...] We consider a version of the Snelson dataset [20] [...] We generate a synthetic regression dataset (N = 100; see Fig. 5) [...] Next, we discuss results for a real dataset: UCI Red Wine Quality (N = 1599) |
| Dataset Splits | No | The paper does not explicitly state specific training/validation/test dataset splits (e.g., percentages or sample counts). While it discusses hyperparameter tuning, which implies a validation set, the details of the splits are not provided. |
| Hardware Specification | No | The paper mentions using the 'RAIDEN computing system' in the acknowledgements but does not provide specific hardware details such as GPU/CPU models, memory, or other specifications used for the experiments. |
| Software Dependencies | No | The paper mentions algorithms like Adam and VOGN but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We use a single hidden-layer MLP with 32 units and sigmoidal transfer function. [...] For Laplace, we use Adam [11], and, for VI, we use VOGN [10]. [...] We consider Le Net-5 [12] [...] We fit the data by using a neural network with single hidden layer of 20 units and tanh nonlinearity. [...] We use an MLP with 2 hidden layers 20 units each and tanh transfer function. We consider tuning the regularizer δ, the noise-variance σ, and the DNN width. We use the Laplace approximation and tune one parameter at a time while keeping the others fixed (we use respectively σ = 0.64, δ = 30 and σ = 0.64, δ = 3, 1 hidden layer). |