Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability

Adaptive Task Automata: A Framework for Verifying Adaptive Embedded Systems

Adaptive embedded systems are systems that must be capable to dynamically reconfigure in order to adapt to e.g., changes in available resources, useror application driven mode changes, or modified quality of service requirements. The possibility to adapt provides flexibility that extends the area of operation of embedded systems and potentially reduces resource consumption, but also poses chall...

MASH: A Framework for Adaptive Spatial Hypermedia

Spatial hypermedia provides opportunities for expression not possible in navigational hypertext. However, static spatial hypermedia, just like static traditional hypermedia, has a single presentation for all use contexts. We are exploring the use of multiple adaptation models to alter the presentation of spatial hypertext. Differences between navigational and spatial hypertext expression requir...

A Probabilisti Framework for Multi-Task Learning

Active Learning for Multi-Task Adaptive Filtering

In this paper, we propose an Active Learning (AL) framework for the Multi-Task Adaptive Filtering (MTAF) problem. Specifically, we explore AL approaches to rapidly improve an MTAF system, based on Dirichlet Process priors, with minimal user/task-level feedback. The proposed AL approaches select instances for delivery with a two-fold objective: 1) Improve future task-specific system performance ...

Adaptive Learning Environment Framework

In this paper the Adaptive Learning Environment Framework is presented. This Framework allows to re-use, combine, and improve existing learning systems or knowledge-databases and equip the resulting meta-learning environment with a new advanced user interface. Especially the last mentioned feature supports a completely new way to learn different materials from different sources without having t...