Sirvi Autor "Riis, Karl" järgi

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Bayesi isotoonilise kalibreerimise algoritm ja selle optimeerimine
(2019) Riis, Karl; Meelis Kull
Töö käigus kirjeldati detailselt Mari-Liis Allikivi ja Meelis Kulli loodud Bayesi isotoonilise kalibreerimise algoritm ning üritati seda optimeerida, kuna see oli suurtel andmehulkadel aeglane. Lisaks kirjeldati algoritmiga seotud matemaatilisi mõisteid ja võtteid ning lahendati nende seletamiseks näiteülesandeid. Algoritmi edasiarenduseks kasutati erinevaid tehnikaid, mis tegid algoritmi töö stabiilsemaks ja kiiremaks. Lõpuks analüüsiti optimeeritud Bayesi isotoonilist kalibreerimist ning võrreldi seda isotoonilise kalibreerimisega ja logistilise regressiooniga tehislikel andmetel.
Forecasting Human Trajectories with Uncertainty Estimation
(Tartu Ülikool, 2022) Riis, Karl; Kull, Meelis, juhendaja; Shahroudi, Novin, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Human trajectory forecasting is a task which has been getting increasingly more attention in recent years. It is often used in robotics research as autonomous robots have to be well aware of the movement patterns of surrounding pedestrians to ensure safe and collision-free navigation. Many recent trajectory prediction works have been focused on neural network based solutions which need to be trained on large amounts of data. We propose a new generative trajectory forecasting method which does not need to be previously trained and is algorithmically simple and intuitive. Our method produces a multi-modal output to convey the uncertainty in human motion and is configurable with a set of parameters to adapt it to various environments. We show that our method performs nearly as good and in some cases better than state-of-the-art forecasting models when considering the task of predicting trajectories in an unseen environment. The results indicate that when deploying a forecasting model in an environment for which there is not a lot of data available, a neural network can be rivaled by a simpler approach.