README.md

BiTimelyGPT

BiTimelyGPT is a bidirectional generative pre-training Transformer designed for representation learning using healthcare time-series data, including continuously monitored biosignals and irregularly sampled time series from longitudinal clinical records.

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This figure indicates an overview of BiTimelyGPT architecture.

Panel a. BiTimelyGPT architecture is stacked with $L$ bidirectional Retention layers

Panel b. Bidirectional Alternating AutoRegressive Modeling (BAAR) framework alternately models left-to-right and right-to-left information across layers, thereby pre-training deep bidirectional contextualized representations.

It pre-trains by a Next-Previous-Token Prediction strategy on the final two layers and fine-tunes the [SOS] token in the final layer for discriminative tasks.

Panel c. Bidirectional Retention Layer alternates between forward and backward Retention across layers.

This figure depicts the process of BiTimelyGPT's Next-Previous-Token Prediction pre-training.

The input of time-series sequence with $T$ timesteps and $V$ features, $x \in R^{T \times V}$ is tokenized via a convolution-subsampling module. The convolution-subsampling tokenizer comprises of two 1-D convolution layers with a kernel size of 3 and stride of 2. The resulting sequence of token has dimension $N \times V$ , reducing the sequence length by 1/4, i.e, $N = T /4$. These tokens are projected onto the input embedding $X \in R^{N \times d}$ by an input projection layer. By adding [SOS] and [EOS] tokens, the sequence dimension becomes $(N + 2) \times d$. Given the L bidirectional generative layers, BiTimelyGPT alternates between forward and backward Retention layers to train bidirectional contextualized representations. Moreover, the Retention mechanism provides an efficient chunk-wise forward pass that segments an input sequence into multiple chunks. Given a chunk size of $C$, the $N \times d$ input embedding is reshaped into a $C \times N/C \times d$ tensor. The output projection layer takes the output embedding with the shape $N \times d$ in the last two layers, which are used to predict the original sequence of tokens with the shape $N \times V$ for Next-Previous-Token Prediction task.

Relevant Publications

This published code is referenced from:

Ziyang Song, Qincheng Lu, Mike He Zhu, David Buckeridge, and Yue Li. (2024). Bidirectional generative pre-training for improving healthcare time-series representation learning. Machine Learning for HealthCare (MLHC), Proceedings of Machine Learning Research (JMLR Proceedings track).