CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding

Kaiyuan Chen, Xingzhuo Guo, Yu Zhang, Jianmin Wang, Mingsheng Long
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:6757-6775, 2024.

Abstract

Predictive Coding (PC) is a theoretical framework in cognitive science suggesting that the human brain processes cognition through spatiotemporal prediction of visual world. Existing studies have developed spatiotemporal prediction neural networks based on the PC theroy, emulating its two core mechanisms: Correcting predictions from residuals and Hierarchical learning. However, these models do not show the enhancement of prediction skills on real-world forecasting tasks, and ignore the Precision Weighting mechanism of PC theory. Precision weight posits that the brain allocates more attention to signals with lower Precision, contributing to the the cognitive ability of human brains. This work introduces the Cognitive Diffusion Probabilistic Models (CogDPM) which demonstrates the connection between diffusion probabilistic models and PC theory. CogDPM features a precision estimation method based on the hierarchical sampling capabilities of diffusion models, and allocate the guidance with precision weights estimated by the inherent property of diffusion models. We experimentally show that the precision weights is an estimator of model’s predictability on the rigid body and fluid motion dataset. We also apply CogDPM to real-world prediction tasks using the U.K. precipitation and ERA surface wind datasets. Our results demonstrate that CogDPM outperforms both existing domain-specific operational models and general deep prediction models in providing more proficient forecasting.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-chen24o, title = {{C}og{DPM}: Diffusion Probabilistic Models via Cognitive Predictive Coding}, author = {Chen, Kaiyuan and Guo, Xingzhuo and Zhang, Yu and Wang, Jianmin and Long, Mingsheng}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {6757--6775}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24o/chen24o.pdf}, url = {https://proceedings.mlr.press/v235/chen24o.html}, abstract = {Predictive Coding (PC) is a theoretical framework in cognitive science suggesting that the human brain processes cognition through spatiotemporal prediction of visual world. Existing studies have developed spatiotemporal prediction neural networks based on the PC theroy, emulating its two core mechanisms: Correcting predictions from residuals and Hierarchical learning. However, these models do not show the enhancement of prediction skills on real-world forecasting tasks, and ignore the Precision Weighting mechanism of PC theory. Precision weight posits that the brain allocates more attention to signals with lower Precision, contributing to the the cognitive ability of human brains. This work introduces the Cognitive Diffusion Probabilistic Models (CogDPM) which demonstrates the connection between diffusion probabilistic models and PC theory. CogDPM features a precision estimation method based on the hierarchical sampling capabilities of diffusion models, and allocate the guidance with precision weights estimated by the inherent property of diffusion models. We experimentally show that the precision weights is an estimator of model’s predictability on the rigid body and fluid motion dataset. We also apply CogDPM to real-world prediction tasks using the U.K. precipitation and ERA surface wind datasets. Our results demonstrate that CogDPM outperforms both existing domain-specific operational models and general deep prediction models in providing more proficient forecasting.} }
Endnote
%0 Conference Paper %T CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding %A Kaiyuan Chen %A Xingzhuo Guo %A Yu Zhang %A Jianmin Wang %A Mingsheng Long %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-chen24o %I PMLR %P 6757--6775 %U https://proceedings.mlr.press/v235/chen24o.html %V 235 %X Predictive Coding (PC) is a theoretical framework in cognitive science suggesting that the human brain processes cognition through spatiotemporal prediction of visual world. Existing studies have developed spatiotemporal prediction neural networks based on the PC theroy, emulating its two core mechanisms: Correcting predictions from residuals and Hierarchical learning. However, these models do not show the enhancement of prediction skills on real-world forecasting tasks, and ignore the Precision Weighting mechanism of PC theory. Precision weight posits that the brain allocates more attention to signals with lower Precision, contributing to the the cognitive ability of human brains. This work introduces the Cognitive Diffusion Probabilistic Models (CogDPM) which demonstrates the connection between diffusion probabilistic models and PC theory. CogDPM features a precision estimation method based on the hierarchical sampling capabilities of diffusion models, and allocate the guidance with precision weights estimated by the inherent property of diffusion models. We experimentally show that the precision weights is an estimator of model’s predictability on the rigid body and fluid motion dataset. We also apply CogDPM to real-world prediction tasks using the U.K. precipitation and ERA surface wind datasets. Our results demonstrate that CogDPM outperforms both existing domain-specific operational models and general deep prediction models in providing more proficient forecasting.
APA
Chen, K., Guo, X., Zhang, Y., Wang, J. & Long, M.. (2024). CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:6757-6775 Available from https://proceedings.mlr.press/v235/chen24o.html.

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