All ML algorithms depend on data representation – efficient and appropriate data representation can enable better understanding of parameters that affect the variations in the data. Representations can be tailored or learned and are dependent on the domain in which classification or prediction algorithms are deployed in.
Recent techniques on representation learning consider unsupervised learning, deep learning, including advances in probabilistic models, auto-encoders, manifold learning and deep networks. One important research question is the ability of the model to achieve abstraction and invariance. In terms of abstraction, we mean the ability of the model to generate abstract concepts from more simple ones. This is then closely related to the amount of data one needs to learn the model or the representation. One of the focus areas will be development of models that encode various levels of abstraction and that are also based on multimodal input data. We will also strive to test this through invariance ability given that models encoding various abstraction levels are invariant to variations in input data. This is closely related to the symbol grounding problem, still largely unsolved in AI, robotics and NLP areas. We will also work on combining representation learning with reasoning techniques and common sense logic. For this reason, statistical-relation learning will also be a methodology to investigate.
|Daniel Ahlsen||Valentin Goranko|
Industrial doctoral students
|Shuangshuang Chen||Volvo||Mårten Björkman|
|Jonathan Styrud||ABB||Christian Smith|
|David Molin||Tobii||Josephine Sullivan|
|Alexandra Karlsson||SAAB||Magnus Jansson|
|Johan Haslum||AstraZenica||Kevin Smith|