464 citations · 538 across the 15 of their papers we have counts for
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stat.ML2021★ 2 cited
Learning Curves for Continual Learning in Neural Networks: Self-Knowledge Transfer and Forgetting
Ryo Karakida, Shotaro Akaho
Sequential training from task to task is becoming one of the major objects in deep learning applications such as continual learning and transfer learning. Nevertheless, it remains…
cs.LG2021
Reconsidering Dependency Networks from an Information Geometry Perspective
Kazuya Takabatake, Shotaro Akaho
Dependency networks (Heckerman et al., 2000) are potential probabilistic graphical models for systems comprising a large number of variables. Like Bayesian networks, the structure…
stat.ML2021★ 7 cited
Principal component analysis for Gaussian process posteriors
Hideaki Ishibashi, Shotaro Akaho
This paper proposes an extension of principal component analysis for Gaussian process (GP) posteriors, denoted by GP-PCA. Since GP-PCA estimates a low-dimensional space of GP poste…