Implicit Manifold Gaussian Process Regression
Authors: Bernardo Fichera, Slava Borovitskiy, Andreas Krause, Aude G Billard
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our technique on a synthetic low-dimensional example and test it in a high-dimensional large dataset setting of predicting rotation angles of rotated MNIST images, improving over the standard Gaussian process regression. Section 5.1: Synthetic Examples. Section 5.2: High Dimensional Datasets. |
| Researcher Affiliation | Academia | Bernardo Fichera1 Viacheslav Borovitskiy2 Andreas Krause2 Aude Billard1 1 EPFL 2 ETH Zürich |
| Pseudocode | No | The paper describes the algorithm in Section 4.4 "Resulting Algorithm" using numbered steps, but it is not presented in a formal pseudocode or algorithm block format. |
| Open Source Code | Yes | Code available at https://github.com/nash169/manifold-gp. |
| Open Datasets | Yes | We consider two MNIST-based datasets. The last dataset, CT slices, has dimensionality of d = 385, we split it to have N = 24075 training samples and 24075 testing samples. Dataset names can be complemented by the fraction of labeled samples, e.g. MR-MNIST-10% refers to n = 10%N. |
| Dataset Splits | No | The paper specifies training and testing samples (e.g., "N = 10000 training samples and 1000 testing samples" for SR-MNIST; "N = 24075 training samples and 24075 testing samples" for CT slices), but it does not explicitly mention a separate validation dataset or its split. |
| Hardware Specification | No | The paper mentions "GPU acceleration" and "high-memory hardware setups" but does not provide specific hardware details such as exact GPU or CPU models, processor types, or memory amounts used for the experiments. |
| Software Dependencies | No | The paper mentions software like FAISS, GPyTorch, PyTorch, and SciPy, but it does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | We run 100 iterations of hyperparameter optimization using Adam with a fixed learning rate of 0.01. For MNIST, with use IMGP with ν = 2; for CT slices with ν = 3. Given the limited number of iterations per run we opted to fix the number of eigenpairs at 20%N and 2%N, for SR-MNIST and MR-MNIST, respectively. |