Media Summary: For more than a century and a half it has been widely-believed that the physics of diffraction imposes certain fundamental limits on ... Do neural networks learn symmetries in data? What is the best way to make a neural network respect a symmetry in the data? From Analysis to Learning: Tensor-Based Assessment of Latent Similarity In data analysis, tensor decompositions such as the ...
1w Minds Ankur Moitra - Detailed Analysis & Overview
For more than a century and a half it has been widely-believed that the physics of diffraction imposes certain fundamental limits on ... Do neural networks learn symmetries in data? What is the best way to make a neural network respect a symmetry in the data? From Analysis to Learning: Tensor-Based Assessment of Latent Similarity In data analysis, tensor decompositions such as the ... In this panel conversation, guest host Jamie Bristow is joined by Katrin Kaufer, Martin Kalungu-Banda and Megan Seneque to ... Random Fully Connected Neural Networks as Perturbatively Solvable Models Fully connected networks are roughly described by ... Recent progress on sparse approximation techniques for parametric PDEs Sparse approximation has proven effective at ...
In deep learning it is common to overparameterize neural networks, that is, to use more parameters than training samples. Watch part 1/2 here: Tensor Methods and Emerging Applications to the Physical and Data ... In this talk, we study a new recovery procedure for non-harmonic signals, or more generally for extended exponential sums y(t), ...
