Search Results for author:
Found 5 papers, 0 papers with code
Global $\mathcal{L}^2$ minimization at uniform exponential rate via geometrically adapted gradient descent in Deep Learning
In the overparametrized case, we prove that, provided that a rank condition holds, all orbits of the modified gradient descent drive the ${\mathcal L}^2$ cost to its global minimum at a uniform exponential convergence rate; one thereby obtains an a priori stopping time for any prescribed proximity to the global minimum.
Non-approximability of constructive global $\mathcal{L}^2$ minimizers by gradient descent in Deep Learning
We analyze geometric aspects of the gradient descent algorithm in Deep Learning (DL) networks.
Geometric structure of Deep Learning networks and construction of global ${\mathcal L}^2$ minimizers
In this paper, we explicitly determine local and global minimizers of the $\mathcal{L}^2$ cost function in underparametrized Deep Learning (DL) networks; our main goal is to shed light on their geometric structure and properties.
Geometric structure of shallow neural networks and constructive ${\mathcal L}^2$ cost minimization
In this paper, we approach the problem of cost (loss) minimization in underparametrized shallow neural networks through the explicit construction of upper bounds, without any use of gradient descent.
Hierarchical Summarization for Longform Spoken Dialog
These concerns all demonstrate the need for a distinctly speech tailored interactive system to help users understand and navigate the spoken language domain.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
