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Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling

This repo contains the official PyTorch implementation for the ICML 2023 paper "Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling" [arXiv]

Dependencies

see requirements.txt

Dataset preparation

NeurIPS Papers: Please follow [GitHub] or directly download from [Kaggle]. Rename the ZIP file as nips-papers.zip if necessary and move it to data folder within poisson-jump.

How to train JUMP models

1. Train on univariate toy data (example)

# train nbinom
python train_toy.py --config-path ./configs/synthetic/jump/nbinom_jump.json --train --num-runs 5 --num-gpus 1 --verbose

• num-runs: number of experiment runs
• num-gpus: number of GPUs available (used for hyperparameter sweeps)
• train: whether to train or to summarize existing experiment results
• verbose: print progress bar and other training logs

2. Train on image data (example)

# cifar-10 (1 GPU)
python train.py --config-path ./configs/cifar10_ordinal_jump.json --verbose
# cifar-10 (4 GPUs)
torchrun --standalone --nproc_per_node 4 --rdzv_backend c10d train.py --config-path ./configs/cifar10_ordinal_jump.json --distributed-mode elastic --num-gpus 4 --verbose

Citation

@inproceedings{chen2023learning,
	title={Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling},
	author={Chen, Tianqi and Zhou, Mingyuan},
	booktitle={International Conference on Machine Learning (ICML)},
	year={2023},
}

License

MIT license

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