208 citations · 209 across the 4 of their papers we have counts for
4 papers
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…
Model-assisted survey sampling with Bayesian optimization
Jonne Pohjankukka, Sakari Tuominen, Jukka Heikkonen
Survey sampling plays an important role in the efficient allocation and management of resources. The essence of survey sampling lies in acquiring a sample of data points from a pop…
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…
Estimating the Prediction Performance of Spatial Models via Spatial k-Fold Cross Validation
Jonne Pohjankukka, Tapio Pahikkala, Paavo Nevalainen +1
In machine learning one often assumes the data are independent when evaluating model performance. However, this rarely holds in practise. Geographic information data sets are an ex…