48 citations · 138 across the 6 of their papers we have counts for
6 papers
Network of Evolvable Neural Units: Evolving to Learn at a Synaptic Level
Paul Bertens, Seong-Whan Lee
Although Deep Neural Networks have seen great success in recent years through various changes in overall architectures and optimization strategies, their fundamental underlying des…
A Machine-Learning Item Recommendation System for Video Games
Paul Bertens, Anna Guitart, Pei Pei Chen +1
Video-game players generate huge amounts of data, as everything they do within a game is recorded. In particular, among all the stored actions and behaviors, there is information o…
Game Data Mining Competition on Churn Prediction and Survival Analysis using Commercial Game Log Data
EunJo Lee, Yoonjae Jang, DuMim Yoon +14
Game companies avoid sharing their game data with external researchers. Only a few research groups have been granted limited access to game data so far. The reluctance of these com…
Games and Big Data: A Scalable Multi-Dimensional Churn Prediction Model
Paul Bertens, Anna Guitart, África Periáñez
The emergence of mobile games has caused a paradigm shift in the video-game industry. Game developers now have at their disposal a plethora of information on their players, and thu…
Forecasting Player Behavioral Data and Simulating in-Game Events
Anna Guitart, Pei Pei Chen, Paul Bertens +1
Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to en…
Rank Ordered Autoencoders
Paul Bertens
A new method for the unsupervised learning of sparse representations using autoencoders is proposed and implemented by ordering the output of the hidden units by their activation v…