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cs.LG2026
Stochastic Gradient Optimization with Model-Assisted Sampling
Jonne Pohjankukka, Jukka Heikkonen
This work addresses the problem of variance in stochastic gradient estimation for machine learning optimization. Deep learning relies on mini-batch methods such as stochastic gradi…
cs.LG2020★ 1 cited
Utilizing remote sensing data in forest inventory sampling via Bayesian optimization
Jonne Pohjankukka, Sakari Tuominen, Jukka Heikkonen
In large-area forest inventories a trade-off between the amount of data to be sampled and the costs of collecting the data is necessary. It is not always possible to have a very la…