Preventing Model Collapse in Gaussian Process Latent Variable Models
Authors: Ying Li, Zhidi Lin, Feng Yin, Michael Minyi Zhang
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed GPLVM, named advised RFLVM, is evaluated across diverse datasets and consistently outperforms various salient competing models, including state-of-the-art variational autoencoders (VAEs) and other GPLVM variants, in terms of informative latent representations and missing data imputation.6. Experiments We showcase the impact of the projection variance and kernel flexibility on model collapse in 6.1 and 6.2. In 6.3 and 6.4, we further corroborate the superior performance of advised RFLVM in latent representation learning on various real-world datasets. |
| Researcher Affiliation | Academia | 1Department of Statistics & Actuarial Science, The University of Hong Kong, Hong Kong, China 2School of Science & Engineering, The Chinese University of Hong Kong, Shenzhen, China. |
| Pseudocode | Yes | Algorithm 1: advised RFLVM: Auto-Differentiable Variational Inference for SM-Embedded RFLVMs |
| Open Source Code | Yes | More experimental details can be found in App. E, and the code is publicly available at https://github.com/zhidilin/advised GPLVM. |
| Open Datasets | Yes | We evaluate the advised RFLVM on the MNIST dataset (Le Cun, 1998). ... CIFAR-10: To create a final dataset of size 2000, we subsampled 400 images from each class within [airplane, automobile, bird, cat, deer]. |
| Dataset Splits | Yes | Table 1: Classification accuracy evaluated by fitting a KNN classifier (k = 1) with five-fold cross-validation. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided. |
| Software Dependencies | No | Mentions 'Py Torch (Paszke et al., 2019)', 'GPy library', 'GPy Torch', and 'sklearn.decomposition module within the scikit-learn library (Buitinck et al., 2013)', but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Table 4: Default hyperparameter settings. PARAMETER VALUE # MIXTURE DENSITIES IN SM KERNEL (m) 2 DIM. OF RANDOM FEATURE (L) 50 DIM. OF LATENT SPACE (Q) 2 OPTIMIZER ADAM (KINGMA & BA, 2014) LEARNING RATE 0.005 BETA (0.9, 0.99) # ITERATIONS 10000 |