Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Unlocking Point Processes through Point Set Diffusion
Authors: David Lüdke, Enric Rabasseda Raventós, Marcel Kollovieh, Stephan Günnemann
ICLR 2025 | Venue PDF | LLM Run Details | Input Tokens: 21,422 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,938 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real-world datasets demonstrate that POINT SET DIFFUSION achieves state-of-the-art performance in unconditional and conditional generation of spatial and spatiotemporal point processes while providing up to orders of magnitude faster sampling. |
| Researcher Affiliation | Academia | David L udke , Enric Rabasseda Ravent os , Marcel Kollovieh, Stephan G unnemann Department of Informatics & Munich Data Science Institute Technical University of Munich, Germany EMAIL |
| Pseudocode | Yes | Algorithm 1 Conditional sampling Require: Xc 0 = C(X0) 1: XT λϵ 2: for t = T, . . . , 1 do 3: e X0 pθ(X0|Xt) 4: e Xt 1 q(Xt 1| e X0, Xt) (reverse 3.2) 5: Xc t 1 q(Xc t 1|Xc 0) (forward 3.1) 6: Xt 1 = C ( e Xt 1) C(Xc t 1) 7: end for 8: return C (X0) |
| Open Source Code | Yes | Code is available at https://www.cs.cit.tum.de/daml/point-set-diffusion |
| Open Datasets | Yes | We evaluate our model on four benchmark datasets with their proposed pre-processing and splits: three real-world datasets Japan Earthquakes (U.S. Geological Survey, 2024), New Jersey COVID-19 Cases (The New York Times, 2024), and Citibike Pickups (Citi Bike, 2024) and one synthetic dataset, Pinwheel, based on a multivariate Hawkes process (Soni, 2019). |
| Dataset Splits | Yes | We follow Chen et al. (2021) and evaluate our model on four benchmark datasets with their proposed pre-processing and splits |
| Hardware Specification | Yes | All models have been trained on an NVIDIA A100-PCIE-40GB. |
| Software Dependencies | No | We use Adam as the optimizer and a fixed weight decay of 0.0001 to avoid overfitting. |
| Experiment Setup | Yes | Hyperparameters: We use the same hyperparameters for all datasets and types of point processes. In a hyperparameter study A.8, we have found T = 100 for our cosine noise schedule (Nichol et al., 2021) to give a good trade off between sampling time and quality. Further, we leverage a hidden dimension and embedding size of 32. For training, we use a batch size of 128 and a learning rate of 0.001. We use Adam as the optimizer and a fixed weight decay of 0.0001 to avoid overfitting. To avoid exploding gradients, we clip the gradients to have a norm lower than 2. |