Causal Discovery in Physical Systems from Videos
Authors: Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, Animesh Garg
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 liyunzhu@mit.edu Antonio Torralba MIT CSAIL torralba@csail.mit.edu Animashree Anandkumar Caltech, Nvidia anima@caltech.edu Dieter Fox University of Washington, Nvidia fox@cs.washington.edu Animesh Garg University of Toronto, Vector Institute, Nvidia garg@cs.toronto.edu |
| 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. |