Search Results for author:
Found 4 papers, 2 papers with code
SING: A Plug-and-Play DNN Learning Technique
We propose SING (StabIlized and Normalized Gradient), a plug-and-play technique that improves the stability and generalization of the Adam(W) optimizer.
Investigating Neural Architectures by Synthetic Dataset Design
It is therefore impossible to know what cues a given neural structure is taking advantage of in such data.
Can neural networks extrapolate? Discussion of a theorem by Pedro Domingos
In particular, what are the interpolation capabilities of trained neural networks?
Improving Pixel-Level Contrastive Learning by Leveraging Exogenous Depth Information
In this paper we will focus on the depth information, which can be obtained by using a depth estimation network or measured from available data (stereovision, parallax motion, LiDAR, etc.).
Cannot find the paper you are looking for? You can
Submit a new open access paper.
