no code implementations • 18 Mar 2024 • Julia Wolleb, Florentin Bieder, Paul Friedrich, Peter Zhang, Alicia Durrer, Philippe C. Cattin
As diffusion-based methods require a lot of GPU memory and have long sampling times, we present a novel and fast unsupervised anomaly detection approach based on latent Bernoulli diffusion models.
no code implementations • 13 Mar 2023 • Peter Zhang
We construct a theoretical framework to certify whether any surjective contract family is optimal, and bound its sub-optimality.
no code implementations • 18 Nov 2022 • Víctor Blanco, Alberto Japón, Justo Puerto, Peter Zhang
In this paper, we introduce Optimal Classification Forests, a new family of classifiers that takes advantage of an optimal ensemble of decision trees to derive accurate and interpretable classifiers.
no code implementations • 31 May 2021 • Runshan Fu, Yangfan Liang, Peter Zhang
We show that even with unbiased input data, when a model is mis-specified: (1) population-level mean prediction error can still be negligible, but group-level mean prediction errors can be large; (2) such errors are not equal across groups; and (3) the difference between errors, i. e., bias, can take the worst-case realization.
1 code implementation • 3 Feb 2021 • Mohamed Chaabane, Peter Zhang, J. Ross Beveridge, Stephen O'Hara
DEFT has comparable accuracy and speed to the top methods on 2D online tracking leaderboards while having significant advantages in robustness when applied to more challenging tracking data.
Ranked #3 on Multi-Object Tracking on KITTI Tracking test