2024-09-12-bui24a.md

title	abstract	openreview	section	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Revisiting Kernel Attention with Correlated Gaussian Process Representation	Transformers have increasingly become the de facto method to model sequential data with state-of-the-art performance. Due to its widespread use, being able to estimate and calibrate its modeling uncertainty is important to understand and design robust transformer models. To achieve this, previous works have used Gaussian processes (GPs) to perform uncertainty calibration for the attention units of transformers and attained notable successes. However, such approaches have to confine the transformers to the space of symmetric attention to ensure the necessary symmetric requirement of their GP’s kernel specification, which reduces the representation capacity of the model. To mitigate this restriction, we propose the Correlated Gaussian Process Transformer (CGPT), a new class of transformers whose self-attention units are modeled as cross-covariance between two correlated GPs (CGPs). This allows asymmetries in attention and can enhance the representation capacity of GP-based transformers. We also derive a sparse approximation for CGP to make it scale better. Our empirical studies show that both CGP-based and sparse CGP-based transformers achieve better performance than state-of-the-art GP-based transformers on a variety of benchmark tasks.	xlIK0vu3MW	Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	bui24a	0	Revisiting Kernel Attention with Correlated Gaussian Process Representation	450	470	450-470	450	false	Bui, Long Minh and Tran Huu, Tho and Dinh, Duy and Nguyen, Tan Minh and Hoang, Trong Nghia	given family Long Minh Bui given family Tho Tran Huu given family Duy Dinh given family Tan Minh Nguyen given family Trong Nghia Hoang	2024-09-12		Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence	244	inproceedings	date-parts 2024 9 12	https://raw.githubusercontent.com/mlresearch/v244/main/assets/bui24a/bui24a.pdf