In this thesis / project, you will have the opportunity to further develop an Artificial Intelligenceagent capable of performing complex purposeful actions in MinecraftorDoomenvironments.
If you would like to know more about this thesis / project opportunity, please watch this video and contact:
In this project / thesis, you will explore recent advances in deep learning for manipulating objects with robotic hands in simulated environments [1][2]. You will try out solutions that cleverly combine deep reinforcement learning, supervised learning, and engineering.
This thesis / project will focus on practical applications of state of the art in computer vision[1][2] to detect objects and segment images [3][4][5][6].
This challenge tackles a key problem in the transportation world: How to efficiently manage dense traffic on complex railway networks? This is a real-world problem faced by many transportation and logistics companies around the world such as the Swiss Federal Railways and Deutsche Bahn. We are developing an AI agent capable of solving such problem.
If you would like to know more about this thesis/project opportunity, please contact:
Dr. Andrew Melnik <andrew.melnik(at)uni-bielefeld.de>
This thesis / project will focus on practical applications of state of the art style generative adversarial network models (StyleGAN) [see References here].
If you would like to know more about this thesis / project opportunity, please contact: Dr. Andrew Melnik <andrew.melnik(at)uni-bielefeld.de>
We are developing an AI agent capable of solving such physical reasoning tasks. If you would like to know more about this thesis/project opportunity, please contact:
Dr. Andrew Melnik <andrew.melnik(at)uni-bielefeld.de>
Motivation is the key to successful learning, in particular practicing to play a music hit has a potential to increase motivation and by doing so to accelerate learning.
The goal of this project is to implement a NN music hit generator and integrate it into existing intelligent tutoring software.
Finding an optimal learning strategy not only for machines but also for humans is central to a large research effort dedicated to development of ITS. Creating a computational environment that enables human learners to gain full control of their learning efficiency is
Virtual try on of clothes with deep learning and image segmentation using just a 2d image. To create a shopping environment close to reality, virtual try-on technology has attracted a lot of interests recently by delivering product information similar to that obtained from direct product examination. It allows users to experience themselves wearing different clothes without efforts of changing them physically.
The goal of this project is to develop a modelling approach to enable a human to learn a complex motor skill quickly. Learning of motor skills such as piano playing involves mastering of multiple subskills, such as reading music score, simultaneously controlling fingers and hands.
We are developing an AI agent for drone control capable of solving SLAM-related problems [1] in environments with challenging features such as changing light conditions, low illumination, adverse weather, and dynamic objects. If you would like to know more about this thesis/project opportunity, please contact: Dr. Andrew Melnik <andrew.melnik(at)uni-bielefeld.de>
Learning of a new skill is a process that is commonly accompanied by an expert. Inspired by the MAML [1] the goal of this thesis is to implement a meta-learner to accelerate human learning. We will develop a tool that teaches a novice to play piano, guided by the dexmo exoskeleton.
Vision-Based robot control typically requires a large amount of manual tuning and feature selection (edge, color, SIFT features, etc.). Modern (GPU-driven) computing power and more robust numerical methods, nowadays allow for training of deep neural networks that extract suitable features for a given task automatically and that are able to learn control tasks in an end-to-end fashion, i.e.
Im Rahmen eines EU-Projektes ist ein Smartphone-basiertes System zur Unterstützung der Diagnose und Therapie von Patienten mit bipolaren Störungen (manisch-depressive) entwickelt worden.
Finding an optimal learning strategy not only for machines but also
The goal of this project is to develop a modelling approach to enable a human to learn a complex motor skill quickly. Learning of motor skills such as piano playing involves mastering of multiple subskills, such as reading music score, simultaneously controlling fingers and hands.
Learning of a new skill is a process that is commonly accompanied by an expert. Inspired by the MAML [1] the goal of this thesis is to implement a meta-learner to accelerate human learning. We will develop a tool that teaches a novice to play piano, guided by the dexmo exoskeleton.