4 citations · 16 across the 14 of their papers we have counts for
Showing 2020Show all
2 papers · 1 filter
stat.ML2020★ 2 cited
Selective Forgetting of Deep Networks at a Finer Level than Samples
Tomohiro Hayase, Suguru Yasutomi, Takashi Katoh
Selective forgetting or removing information from deep neural networks (DNNs) is essential for continual learning and is challenging in controlling the DNNs. Such forgetting is cru…
stat.ML2020★ 1 cited
The Spectrum of Fisher Information of Deep Networks Achieving Dynamical Isometry
Tomohiro Hayase, Ryo Karakida
The Fisher information matrix (FIM) is fundamental to understanding the trainability of deep neural nets (DNN), since it describes the parameter space's local metric. We investigat…