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.
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
Authors: Marco Fraccaro, Simon Kamronn, Ulrich Paquet, Ole Winther
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,880 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,359 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 | The model is trained end-to-end on videos of a variety of simulated physical systems, and outperforms competing methods in generative and missing data imputation tasks. and KVAEs are tested on videos of a variety of simulated physical systems in section 5. |
| Researcher Affiliation | Collaboration | Technical University of Denmark Deep Mind |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Further implementation details can be found in the supplementary material (appendix A) and in the Tensorflow [1] code released at github.com/simonkamronn/kvae. |
| Open Datasets | No | The paper uses simulated data that it generates itself: 'We simulate 5000 sequences of 20 time steps each of a ball moving in a two-dimensional box, where each video frame is a 32x32 binary image.' and 'Training, validation and test set are formed by 500 sequences of 15 frames of 16x16 pixels.' No concrete access information (link, DOI, formal citation for a public dataset) is provided. |
| Dataset Splits | No | The paper mentions 'Training, validation and test set are formed by 500 sequences of 15 frames of 16x16 pixels.' but does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) to reproduce the data partitioning. |
| Hardware Specification | Yes | We thank NVIDIA Corporation for the donation of TITAN X GPUs. |
| Software Dependencies | No | The paper mentions 'Tensorflow [1]' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The minimum number of latent dimensions that the KVAE requires to model the ball s dynamics are at R2 and zt R4, as at the very least the ball s position in the box s 2d plane has to be encoded in at, and zt has to encode the ball s position and velocity. The dynamics parameter network uses K = 3 to interpolate three modes... and We use a KVAE with at R2, zt R3 and K = 2 |