Statistical Physics of Learning and InferenceBiehl, M., Caticha, N., Opper, M. & Villmann, T., Apr-2019, Proc. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning : ESANN 2019 . Verleysen, M. (ed.). Ciaco - i6doc.com, 9 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic
and computer science has been very fruitful and is currently gaining momentum as a consequence of the revived interest in neural networks, machine learning
and inference in general.
Statistical physics methods complement other approaches to the theoretical understanding of machine learning processes and inference in stochastic modeling. They facilitate, for instance, the study of dynamical and equilibrium properties of randomized training processes in model situations.
At the same time, the approach inspires novel and efficient algorithms and facilitates interdisciplinary applications in a variety of scientific and technical disciplines.
|Title of host publication||Proc. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning|
|Subtitle of host publication||ESANN 2019|
|Publisher||Ciaco - i6doc.com|
|Number of pages||9|
|Publication status||Published - Apr-2019|
|Event||Special Session at ESANN 2019: Statistical Physics of Learning and Inference - Brugge, Belgium|
Duration: 24-Apr-2019 → 26-Apr-2019
|Conference||Special Session at ESANN 2019|
|Period||24/04/2019 → 26/04/2019|
Special Session at ESANN 2019: Statistical Physics of Learning and Inference
24/04/2019 → 26/04/2019Brugge, Belgium
- 24-Apr-2019 → 26-Apr-2019
Activity: Participating in or organising an event › Participation in conference › Academic