Generalized Eigenvalue Problems with Generative Priors
Authors: Zhaoqiang Liu, Wen Li, Junren Chen
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results are provided to demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Zhaoqiang Liu Wen Li University of Electronic Science and Technology of China {zqliu12, liwenbnu}@gmail.com Junren Chen University of Hong Kong chenjr58@connect.hku.hk |
| Pseudocode | Yes | Algorithm 1 Projected Rayleigh Flow Method (PRFM) |
| Open Source Code | No | The code will be open sourced after acceptance. |
| Open Datasets | Yes | In this section, we conduct proof-of-concept numerical experiments on the MNIST dataset [47]... Additional results for MNIST and Celeb A [55] are provided in Appendices D and E. |
| Dataset Splits | No | The paper mentions training on the 'original MNIST training set' and evaluating on a 'testing set', but it does not explicitly specify a validation split percentage or count. |
| Hardware Specification | Yes | All experiments are carried out using Python 3.10.6 and Py Torch 2.0.0, with an NVIDIA RTX 3060 Laptop 6GB GPU. |
| Software Dependencies | Yes | All experiments are carried out using Python 3.10.6 and Py Torch 2.0.0, with an NVIDIA RTX 3060 Laptop 6GB GPU. |
| Experiment Setup | Yes | The VAE is trained using the Adam optimizer with a mini-batch size of 100 and a learning rate of 0.001 on the original MNIST training set. To approximately perform the projection step PG( ), we use a gradient descent method with the Adam optimizer, with a step size of 100 and a learning rate of 0.1. |