Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks
Authors: Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V. Le, Honglak Lee
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate this question by performing the first large-scale empirical study and demonstrate state-of-the-art performance by learning large models on three different datasets: one for modeling object interactions, one for modeling human motion, and one for modeling car driving. |
| Researcher Affiliation | Collaboration | Ruben Villegas1,4 Arkanath Pathak3 Harini Kannan2 Dumitru Erhan2 Quoc V. Le2 Honglak Lee2 1 University of Michigan 2 Google Research 3 Google 4 Adobe Research |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that the source code for the methodology is openly available, nor does it provide a direct link to a code repository. It mentions a website for qualitative results and supplementary material for other details, but not code. |
| Open Datasets | Yes | We use the action-conditioned towel pick dataset from Ebert et al. [2018]... We use the Human 3.6M dataset [Ionescu et al., 2014]... We use the KITTI driving dataset [Geiger et al., 2013] |
| Dataset Splits | Yes | We use the train/test split from Villegas et al. [2017b]... We use the train/test split from Lotter et al. [2017] in our experiments. |
| Hardware Specification | No | Details of the devices we use to scale up computation can be found in the supplementary material. The main paper does not provide specific hardware details for the experiments. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | To increase the capacity of our baseline model, we use hyperparameters K and M, which denote the factors by which the number of neurons in each layer of the encoder, decoder and LSTMs are increased. In our experiments we increase both K and M together until we reach the device limits. Due to the LSTM having more parameters, we stop increasing the capacity of the LSTM at M = 3 but continue to increase K up to 5. During training time, the models are conditioned on 2 input frames and predict 10 frames into the future. During test time, the models predict 18 frames into the future. |