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Found 7 papers, 1 papers with code
Deep Neural Network Models Trained With A Fixed Random Classifier Transfer Better Across Domains
Inspired by NC properties, we explore in this paper the transferability of DNN models trained with their last layer weight fixed according to ETF.
Random matrices in service of ML footprint: ternary random features with no performance loss
As a result, for any kernel matrix ${\bf K}$ of the form above, we propose a novel random features technique, called Ternary Random Feature (TRF), that (i) asymptotically yields the same limiting kernel as the original ${\bf K}$ in a spectral sense and (ii) can be computed and stored much more efficiently, by wisely tuning (in a data-dependent manner) the function $\sigma$ and the random vector ${\bf w}$, both taking values in $\{-1, 0, 1\}$.
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach
This article provides theoretical insights into the inner workings of multi-task and transfer learning methods, by studying the tractable least-square support vector machine multi-task learning (LS-SVM MTL) method, in the limit of large ($p$) and numerous ($n$) data.
Latent heterogeneous multilayer community detection
We propose a method for simultaneously detecting shared and unshared communities in heterogeneous multilayer weighted and undirected networks.
Coulomb Autoencoders
Learning the true density in high-dimensional feature spaces is a well-known problem in machine learning.
Spectral community detection in heterogeneous large networks
The analysis of this equivalent spiked random matrix allows us to improve spectral methods for community detection and assess their performances in the regime under study.
The Asymptotic Performance of Linear Echo State Neural Networks
In this article, a study of the mean-square error (MSE) performance of linear echo-state neural networks is performed, both for training and testing tasks.
