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Colloquium: Tensor Network Machine Learning Models

date
18.06.2018 
time
04:30 PM - 05:30 PM 
speaker
Dr. Edwin Miles Stoudenmire 
affiliation
Flatiron Institute, Center for Computational Quantum Physics, New York, USA 
part of series
MPI-PKS Colloquium 
language
en 
main topic
Physics: Theoretical Physics
abstract

Tensor networks are an efficient representation of interesting many-body wavefunctions and underpin powerful algorithms for strongly correlated systems. But tensor networks could be applied much more broadly than just for representing wavefunctions. Large tensors similar to wavefunctions appear naturally in certain families of models studied extensively in machine learning. Decomposing the model parameters as a tensor network leads to interesting algorithms for training models on real-world data which scale better than existing approaches. In addition to training models directly for recognizing labeled data, tensor network real-space renormalization approaches can be used to extract statistically significant "features" for subsequent learning tasks. I will also highlight other benefits of the tensor network approach such as the flexibility to blend different approaches and to interpret trained models.

 

Last update: 18.06.2018 00:07.

venue 

Max-Planck-Institut für Physik komplexer Systeme (Seminarroom 1+2+3) 
Nöthnitzer Straße 38
01187 Dresden
telefon
+ 49 (0)351 871 0 
e-mail
Max-Planck-Institut für Physik komplexer Systeme 
homepage
http://www.mpipks-dresden.mpg.de 

organizer 

Max-Planck-Institut für Physik komplexer Systeme (MPI-PKS)
Nöthnitzer Straße 38
01187 Dresden
telefon
+ 49 (0)351 871 0 
e-mail
Max-Planck-Institut für Physik komplexer Systeme (MPI-PKS) 
homepage
http://www.mpipks-dresden.mpg.de 
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