SanderGielisse
PhD Candidate AI Computer Vision Lab @ Delft University of Technology
ScriptAndHandsThe Netherlands
Pinned Repositories
cvlab-tudelft.github.io
DO NOT edit this repository. This is the rendered repository, not the source repository. You're looking for the repository called TU-Delft-CV-Lab-website.
mongo-java-driver
The official MongoDB drivers for Java, Kotlin, and Scala
AutoTimestamp
TU Delft Computer Vision Seminar Project; Predicting Timestamp from Photo's
BannerBoard-free
checkstyle
Checkstyle is a development tool to help programmers write Java code that adheres to a coding standard. By default it supports the Google Java Style Guide and Sun Code Conventions, but is highly configurable. It can be invoked with an ANT task and a command line program.
ConversationalAgentTaskClarity
Crowd Computing conversational agent experiment in which task clarity is considered as the study.
Enderstone
Enderstone - Minecraft Server Software
fltk-testbed
MEDFE-CAE
Source code for Non-Lossy Ground Truth Comparison via CAE for MEDFE as part of the reproducibility project for the Deep Learning course at the TU Delft
Mythan
Mythan is a Java implementation of the NEAT algorithm as described by Kenneth O. Stanley and Risto Miikkulainen.
SanderGielisse's Repositories
SanderGielisse/Enderstone
Enderstone - Minecraft Server Software
SanderGielisse/Mythan
Mythan is a Java implementation of the NEAT algorithm as described by Kenneth O. Stanley and Risto Miikkulainen.
SanderGielisse/BannerBoard-free
SanderGielisse/AutoTimestamp
TU Delft Computer Vision Seminar Project; Predicting Timestamp from Photo's
SanderGielisse/checkstyle
Checkstyle is a development tool to help programmers write Java code that adheres to a coding standard. By default it supports the Google Java Style Guide and Sun Code Conventions, but is highly configurable. It can be invoked with an ANT task and a command line program.
SanderGielisse/ConversationalAgentTaskClarity
Crowd Computing conversational agent experiment in which task clarity is considered as the study.
SanderGielisse/fltk-testbed
SanderGielisse/MEDFE-CAE
Source code for Non-Lossy Ground Truth Comparison via CAE for MEDFE as part of the reproducibility project for the Deep Learning course at the TU Delft