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Random matrices in service of ML footprint: ternary random features with
  no performance loss

Random matrices in service of ML footprint: ternary random features with no performance loss

5 October 2021
Hafiz Tiomoko Ali
Zhenyu Liao
Romain Couillet
ArXivPDFHTML

Papers citing "Random matrices in service of ML footprint: ternary random features with no performance loss"

2 / 2 papers shown
Title
Random Matrix Analysis to Balance between Supervised and Unsupervised
  Learning under the Low Density Separation Assumption
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption
Vasilii Feofanov
Malik Tiomoko
Aladin Virmaux
16
5
0
20 Oct 2023
On the Equivalence between Implicit and Explicit Neural Networks: A
  High-dimensional Viewpoint
On the Equivalence between Implicit and Explicit Neural Networks: A High-dimensional Viewpoint
Zenan Ling
Zhenyu Liao
Robert C. Qiu
30
0
0
31 Aug 2023
1