Research
Publications
Lab42 strives to break new ground in Artificial Intelligence (AI), alongside, but not limited to, neural networks and Deep Learning. This work is closely related to researching intelligence and its implications for AI. On this page, we present the work of Lab42's researchers and co-founders.
November 2022 | Lukas Tuggener, Jürgen Schmidhuber, and Thilo Stadelmann
Is it enough to optimize CNN architectures on ImageNet?
Classification performance based on ImageNet is the de-facto standard metric for CNN development. In this work we challenge the notion that CNN architecture design solely based on ImageNet leads to generally effective convolutional neural network (CNN) architectures that perform well on a diverse set of datasets and application domains. To this end, we investigate and ultimately improve ImageNet as a basis for deriving such architectures.
September 2022 | Christoph von der Malsburg, Benjamin F. Grewe, and Thilo Stadelmann
Making Sense of the Natural Environment
Proceedings of the KogWis 2022 – Understanding Minds Biannual Conference of the German Cognitive Science Society, Freiburg, Germany
May 2022 | Rolf Pfister
Towards a theory of abduction based on conditionals
Abduction is considered the most powerful, but also the most controversially discussed type of inference. Based on an analysis of Peirce’s retroduction, Lipton’s Inference to the Best Explanation and other theories, a new theory of abduction is proposed. It considers abduction not as intrinsically explanatory but as intrinsically conditional: for a given fact, abduction allows one to infer a fact that implies it.
April 2022 | Christoph von der Malsburg, Thilo Stadelmann, and Benjamin F. Grewe
A Theory of Natural Intelligence
In contrast to current AI technology, natural intelligence — the kind of autonomous intelligence that is realized in the brains of animals and humans to attain in their natural environment goals defined by a repertoire of innate behavioral schemata — is far superior in terms of learning speed, generalization capabilities, autonomy and creativity. How are these strengths, by what means are ideas and imagination produced in natural neural networks?
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