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..
Multiple Futures Prediction
Authors: Charlie Tang, Russ R. Salakhutdinov
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our algorithms by predicting vehicle trajectories of both simulated and real data, demonstrating the state-of-the-art results on several vehicle trajectory datasets. |
| Researcher Affiliation | Industry | Yichuan Charlie Tang Apple Inc. EMAIL Ruslan Salakhutdinov Apple Inc. EMAIL |
| Pseudocode | Yes | We provide a detailed training algorithm pseudocode in the supplementary materials. |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the public release of its source code for the described methodology. |
| Open Datasets | Yes | We demonstrate our algorithms by predicting vehicle trajectories of both simulated and real data, demonstrating the state-of-the-art results on several vehicle trajectory datasets. ... First, we ๏ฌrst generate simulated trajectory data from the CARLA simulator [17]... We then experiment on a widely known standard dataset of real vehicle trajectories, the NGSIM [12] dataset. ... Finally, we also benchmark MFP with previously published results on the more recent large scale Argoverse motion forecasting dataset [9]. |
| Dataset Splits | Yes | We experiment with the US-101 and I-80 datasets, and follow the experimental protocol of [16], where the datasets are split into 70% training, 10% validation, and 20% testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using GRUs and the CARLA simulator, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We extract 8 seconds trajectories, using the ๏ฌrst 3 seconds as history to predict 5 seconds into the future. ... We trained MFP (with 1 to 5 modes) on the Town01 training set for 200K updates, with minibatch size 8. |