Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families
Authors: Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate SNEPPPs on real, and synthetic benchmarks, and provide a software implementation.We empirically demonstrate the efficacy and efficiency of our open source method on a large scale case study of wildfire data from NASA with about 100 million events. |
| Researcher Affiliation | Collaboration | Russell Tsuchida1, Cheng Soon Ong1, 2, Dino Sejdinovic3 1Data61-CSIRO 2Australian National University 3University of Adelaide |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states it provides a “software implementation” and describes its method as “open source”, but it does not provide a specific repository link, explicit code release statement (e.g., ‘code available at URL’), or mention of code in supplementary materials. |
| Open Datasets | Yes | Using NASA wildfire data (right) (NASA FIRMS 2023).We use a massive freely available dataset (NASA FIRMS 2023).We also perform benchmarks on three real datasets bei, copper and clmfires also considered by Kim, Asami, and Toda (2022). |
| Dataset Splits | No | The paper mentions data splitting by subsampling and thinning (“we split the original dataset into disjoint training and test sets by subsampling”, “artificially but exactly obtain multiple independent realisations from a single realisation of data by a process called splitting or thinning”), but does not provide specific percentages, sample counts, or detailed methodology for these splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or processor types) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a “TensorFlow library” and “Adam” for optimization but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper mentions using “Adam to optimise parameters” and suggests a learning rate based on a theoretical β, but it does not provide specific hyperparameter values (e.g., Adam’s learning rate, batch size, number of epochs) or other concrete system-level training settings. |