Awesome
NAOMI
Code for NeurIPS 2019 paper titled NAOMI: Non-Autoregressive Multiresolution Sequence Imputation
Code is written with PyTorch v0.4.1 (Python 3.6.5). Billiards data can be downloaded here, basketball data is available from STATS.
To train the model:
First open visdom, then adjust hyperparameters in train_model.sh
and run the shell file.
Detailed explanations of hyperparameters:
• --model
: “NAOMI” or “SingleRes”
• --task
: “basketball” or “billiard”
• --y_dim
: 10 for basketball and 2 for billiard
• --rnn_dim
and --n_layers
: gru cell size for all models, including forward and backward rnns
• --dec1_dim
to --dec16_dim
: For NAOMI, these values correspond to dimensions of different decoders. For SingleRes, only dec1_dim is used for decoder.
• --pre_start_lr
: initial learning rate for supervised pretrain
• --pretrain
: supervised pretrain epochs
• --highest
: largest stepsize for NAOMI decoders, should be 2^n
• --discrim_rnn_dim
and --discrim_layers
: discriminator rnn size
• --policy_learning_rate
: learning rate for generator in adversarial training
• --discrim_learning_rate
: learning rate for discriminator in adversarial training
• --pretrain_disc_iter
: number of iterations to pretrain discriminator
• --max_iter_num
: number of adversarial training iterations
Citation
If you find this repository, e.g., the code and the datasets, useful in your research, please cite the following paper:
@inproceedings{liu2019naomi,
title={NAOMI: Non-Autoregressive Multiresolution Sequence Imputation},
author={Liu, Yukai and Yu, Rose and Zheng, Stephan and Zhan, Eric and Yue, Yisong},
booktitle={Advances in Neural Information Processing Systems(NeurIPS '19)},
year={2019}
}