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A contrastive rule for meta-learning

A contrastive rule for meta-learning

4 April 2021
Nicolas Zucchet
Simon Schug
J. Oswald
Dominic Zhao
João Sacramento
    MLT
ArXivPDFHTML

Papers citing "A contrastive rule for meta-learning"

6 / 6 papers shown
Title
Improving equilibrium propagation without weight symmetry through
  Jacobian homeostasis
Improving equilibrium propagation without weight symmetry through Jacobian homeostasis
Axel Laborieux
Friedemann Zenke
17
7
0
05 Sep 2023
Dataset Distillation with Convexified Implicit Gradients
Dataset Distillation with Convexified Implicit Gradients
Noel Loo
Ramin Hasani
Mathias Lechner
Daniela Rus
DD
31
41
0
13 Feb 2023
Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned
  Linear Filters based on Long-Short Term Channel Decomposition
Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition
Sangwoo Park
Osvaldo Simeone
29
4
0
23 Mar 2022
Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear
  Filters and Equilibrium Propagation
Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium Propagation
Sangwoo Park
Osvaldo Simeone
34
8
0
01 Oct 2021
Long short-term memory and learning-to-learn in networks of spiking
  neurons
Long short-term memory and learning-to-learn in networks of spiking neurons
G. Bellec
Darjan Salaj
Anand Subramoney
Robert Legenstein
Wolfgang Maass
119
481
0
26 Mar 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
338
11,684
0
09 Mar 2017
1