Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Learning Hierarchical Priors in VAEs
Authors: Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 15,041 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,441 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate our approach, we consider the following experiments. In Sec. 4.1, we demonstrate that our method learns to represent the degree of freedom in the data of a moving pendulum. In Sec. 4.2, we generate human movements based on the learned latent representations of real-world data (CMU Graphics Lab Motion Capture Database). In Sec. 4.3, the marginal log-likelihood on standard datasets such as MNIST, Fashion-MNIST, and OMNIGLOT is evaluated. In Sec. 4.4, we compare our method on the high-dimensional image datasets 3D Faces and 3D Chairs. |
| Researcher Affiliation | Collaboration | 1Machine Learning Research Lab, Volkswagen Group, Germany 2Department of Informatics, Technical University of Munich, Germany |
| Pseudocode | Yes | Algorithm 1 (REWO) Reconstruction-error-based weighting of the objective function |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | CMU Graphics Lab Motion Capture Database (http://mocap.cs.cmu.edu), MNIST [18, 17], Fashion-MNIST [29], and OMNIGLOT [16] |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python, specific deep learning frameworks like TensorFlow or PyTorch, with their versions). |
| Experiment Setup | Yes | In the GECO update scheme (Eq. (5)), β increases/decreases until ˆCt = κ2. ... βt = βt 1 exp ν fβ(βt 1, ˆCt κ2; τ) (ˆCt κ2) ... Initialise β 1 ... ˆCt = (1 α) ˆCba + α ˆCt 1 |