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..
Geometric Trajectory Diffusion Models
Authors: Jiaqi Han, Minkai Xu, Aaron Lou, Haotian Ye, Stefano Ermon
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on both unconditional and conditional generation in various scenarios, including physical simulation, molecular dynamics, and pedestrian motion. Empirical results on a wide suite of metrics demonstrate that Geo TDM can generate realistic geometric trajectories with significantly higher quality. |
| Researcher Affiliation | Academia | Jiaqi Han, Minkai Xu, Aaron Lou, Haotian Ye, Stefano Ermon Stanford University |
| Pseudocode | Yes | Algorithm 1 Training Procedure of Geo TDM-uncond Algorithm 2 Sampling Procedure of Geo TDM-uncond Algorithm 3 Training Procedure of Geo TDM-cond Algorithm 4 Sampling Procedure of Geo TDM-cond |
| Open Source Code | Yes | Code is available at https://github.com/ hanjq17/Geo TDM. |
| Open Datasets | Yes | We employ the MD17 [5] dataset, which contains the DFT-simulated molecular dynamics (MD) trajectories of 8 small molecules... We apply our model to ETH-UCY [35, 28] dataset, a challenging and large-scale benchmark for pedestrian trajectory forecasting. |
| Dataset Splits | Yes | For all three datasets, we use 3000 trajectories for training, 2000 for validation, and 2000 for testing. For each molecule, we construct a training set of 5000 trajectories, and 1000/1000 for validation and testing, uniformly sampled along the time dimension. |
| Hardware Specification | Yes | We use Distributed Data Parallel on 4 Nvidia A6000 GPUs to train all the models. Our CPUs were standard intel CPUs. |
| Software Dependencies | No | The paper mentions using an "Adam optimizer" and refers to implementations from other papers [43], [11], but does not list specific software libraries (like PyTorch, TensorFlow, or specific GNN frameworks) with their version numbers required for reproducibility. |
| Experiment Setup | Yes | We provide the detailed hyper-parameters of Geo TDM in Table 7. We adopt Adam optimizer with betas (0.9, 0.999) and ϵ = 10 8. For all experiments, we use the linear noise schedule [18] with βstart = 0.02 and βend = 0.0001. |