no code implementations • ICCV 2023 • Aristeidis Panos, Yuriko Kobe, Daniel Olmeda Reino, Rahaf Aljundi, Richard E. Turner
In this work, we develop a baseline method, First Session Adaptation (FSA), that sheds light on the efficacy of existing CIL approaches and allows us to assess the relative performance contributions from head and body adaption.
1 code implementation • 13 Dec 2021 • Constantinos Daskalakis, Petros Dellaportas, Aristeidis Panos
In particular, we bound the Kullback-Leibler divergence between an exact GP and one resulting from one of the afore-described low-rank approximations to its kernel, as well as between their corresponding predictive densities, and we also bound the error between predictive mean vectors and between predictive covariance matrices computed using the exact versus using the approximate GP.
1 code implementation • 30 May 2021 • Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas
We adopt the interpretability offered by a parametric, Hawkes-process-inspired conditional probability mass function for the marks and apply variational inference techniques to derive a general and scalable inferential framework for marked point processes.
3 code implementations • 3 Apr 2020 • Constantinos Daskalakis, Petros Dellaportas, Aristeidis Panos
In particular, we bound the Kullback-Leibler divergence between an exact GP and one resulting from one of the afore-described low-rank approximations to its kernel, as well as between their corresponding predictive densities, and we also bound the error between predictive mean vectors and between predictive covariance matrices computed using the exact versus using the approximate GP.
no code implementations • 6 Jul 2018 • Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias
We introduce fully scalable Gaussian processes, an implementation scheme that tackles the problem of treating a high number of training instances together with high dimensional input data.