M. Sabatelli

发表

University of Groningen Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms Sabatelli,

Gilles Louppe, Matthia Loupe, Gilles Geurts, 2019 .

Correlation of significant places with self-reported state of bipolar disorder patients

V. Osmani, O. Mayora, P. Lukowicz, 2014 .

University of Groningen Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead Sabatelli,

M. Wiering, V. Codreanu, M. Sabatelli, 2017 .

Advances in Digital Music Iconography: Benchmarking the detection of musical instruments in unrestricted, non-photorealistic images from the artistic domain

Walter Daelemans, Pierre Geurts, Mike Kestemont, 2021, Digit. Humanit. Q..

Machine Learning Students Overfit to Overfitting

Matias Valdenegro-Toro, M. Sabatelli, 2022, Teaching ML.

How Well Do Vision Transformers (VTs) Transfer To The Non-Natural Image Domain? An Empirical Study Involving Art Classification

M. Sabatelli, Vincent Tonkes, 2022, ECCV Workshops.

A Deep Convolutional Neural Network for Location Recognition and Geometry based Information

Lambert Schomaker, M. Wiering, M. Sabatelli, 2018 .

A Deep Convolutional Neural Network for Location Recognition and Geometry based Information

Lambert Schomaker, M. Wiering, M. Sabatelli, 2018 .

Factors of Influence of the Overestimation Bias of Q-Learning

M. Sabatelli, Julius Wagenbach, 2022, ArXiv.

QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning

Gilles Louppe, D. Ernst, P. Geurts, 2020, ArXiv.

Fractional Transfer Learning for Deep Model-Based Reinforcement Learning pdf

Matthia Sabatelli, Marco A. Wiering, Remo Sasso, 2021, ArXiv.

On the Transferability of Winning Tickets in Non-Natural Image Datasets

Pierre Geurts, Mike Kestemont, Matthia Sabatelli, 2020, VISIGRAPP.

Deep Transfer Learning for Art Classification Problems

Walter Daelemans, Pierre Geurts, Mike Kestemont, 2018, ECCV Workshops.

Transfer Learning with Style Transfer between the Photorealistic and Artistic Domain

Walter Daelemans, Pierre Geurts, Mike Kestemont, 2021, Electronic Imaging.

Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead

Marco Wiering, Matthia Sabatelli, Valeriu Codreanu, 2018, ICPRAM.

Forest Fire Control with Learning from Demonstration and Reinforcement Learning

Matthia Sabatelli, Marco A. Wiering, Travis Hammond, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning

M. Wiering, M. Sabatelli, Remo Sasso, 2022, Trans. Mach. Learn. Res..

Correlation of significant places with self-reported state of bipolar disorder patients

Oscar Mayora-Ibarra, Agnes Grünerbl, Paul Lukowicz, 2014, 2014 4th International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH).

Learning Perceptive Bipedal Locomotion over Irregular Terrain

M. Sabatelli, H. Kasaei, B. V. Marum, 2023, arXiv.org.

Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning

M. Sabatelli, A. Muller, 2023, arXiv.org.

Safe and Psychologically Pleasant Traffic Signal Control with Reinforcement Learning using Action Masking

M. Sabatelli, A. Muller, 2022, 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC).

On The Transferability of Deep-Q Networks pdf

Pierre Geurts, Matthia Sabatelli, P. Geurts, 2021, ArXiv.

Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning

M. Sabatelli, Remo Sasso, Marco Wiering, 2022, ArXiv.

Contextual Online Imitation Learning (COIL): Using Guide Policies in Reinforcement Learning

M. Sabatelli, Marc Groefsema, Alexander Hill, 2024, ICAART.

Contextual Online Imitation Learning

M. Sabatelli, Marc Groefsema, Alexander Hill, 2022 .