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.
Causal Discovery in Physical Systems from Videos
Authors: Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, Animesh Garg
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,078 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,130 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 | Experiments demonstrate that our model can correctly identify the interactions from a short sequence of images and make long-term future predictions. |
| Researcher Affiliation | Collaboration | Yunzhu Li MIT CSAIL EMAIL Antonio Torralba MIT CSAIL EMAIL Animashree Anandkumar Caltech, Nvidia EMAIL Dieter Fox University of Washington, Nvidia EMAIL Animesh Garg University of Toronto, Vector Institute, Nvidia EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link to a project page (https://yunzhuli.github.io/V-CDN/) for additional results, but not explicitly to the source code for the methodology. The project page is considered a demonstration/overview page, not a direct code repository. |
| Open Datasets | No | The paper describes experiments conducted in custom-built environments (Multi-Body Interaction, Fabric Manipulation) and does not provide concrete access information or citations for publicly available datasets. |
| Dataset Splits | No | The paper mentions using 'training set' and 'test set' but does not provide specific details on dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions software components and techniques like "graph neural networks" and "Gumbel-Softmax" but does not list specific software names with version numbers. |
| Experiment Setup | No | The paper mentions general training aspects like using reconstruction loss and stochastic gradient descent but does not provide specific hyperparameters such as learning rate, batch size, or number of epochs. |