J. V. Rijn

发表

Comparing Strategic Agents for Dominance

A. Kannangara, J. V. Rijn, 2019 .

ENDGAME ANALYSIS OF DOU

J. V. Rijn, J. Vis, 2015 .

Complexity and Retrograde Analysis of the Game

J. V. Rijn, J. Vis, Dou Shou Qi, 2013 .

Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification

F. Hutter, I. Guyon, Fábio Ferreira, 2022, NeurIPS.

Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification

F. Hutter, I. Guyon, Fábio Ferreira, 2022, NeurIPS.

Opleiding Informatica BlockStep, an A* algorithm toward Minecraft optimal world traversal for speedrunning

J. V. Rijn, Isaac Braam, 2022 .

Advances in MetaDL: AAAI 2021 challenge and workshop

J. Vanschoren, J. V. Rijn, Zhengying Liu, 2022, MetaDL@AAAI.

Endgame Analysis of DOU SHOU QI pdf

Jan N. van Rijn, Jonathan K. Vis, J. V. Rijn, 2016, J. Int. Comput. Games Assoc..

Opleiding Informatica Strategic Gambling in DiminishingBridge

J. V. Rijn, Thijs Snelleman, Opleiding Informatica, 2020 .

Computing and Predicting Winning Hands in the Trick-Taking Game of Klaverjas

Jan N. van Rijn, Jonathan K. Vis, Frank W. Takes, 2018, BNCAI.

Informatica en Economie How to create an agent for the game of Klaverjas using Random Forests

J. V. Rijn, P. Koning, J. K. Vis, 2022 .