Geometric Trajectory Diffusion Models
Authors: Jiaqi Han, Minkai Xu, Aaron Lou, Haotian Ye, Stefano Ermon
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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. |