ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2005.05541
  4. Cited By
Modularizing Deep Learning via Pairwise Learning With Kernels
v1v2 (latest)

Modularizing Deep Learning via Pairwise Learning With Kernels

12 May 2020
Shiyu Duan
Shujian Yu
José C. Príncipe
    MoMe
ArXiv (abs)PDFHTML

Papers citing "Modularizing Deep Learning via Pairwise Learning With Kernels"

9 / 9 papers shown
Assembling Modular, Hierarchical Cognitive Map Learners with
  Hyperdimensional Computing
Assembling Modular, Hierarchical Cognitive Map Learners with Hyperdimensional Computing
Nathan McDonald
Anthony Dematteo
367
2
0
29 Apr 2024
MOLE: MOdular Learning FramEwork via Mutual Information Maximization
MOLE: MOdular Learning FramEwork via Mutual Information Maximization
Tianchao Li
Yulong Pei
270
0
0
15 Aug 2023
Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning:
  A Survey
Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Survey
Gabriele Lagani
Fabrizio Falchi
Claudio Gennaro
Giuseppe Amato
AAML
269
12
0
30 Jul 2023
Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A
  Survey
Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey
Gabriele Lagani
Fabrizio Falchi
Claudio Gennaro
Giuseppe Amato
239
20
0
30 Jul 2023
Modularizing and Assembling Cognitive Map Learners via Hyperdimensional
  Computing
Modularizing and Assembling Cognitive Map Learners via Hyperdimensional Computing
N. McDonald
368
8
0
10 Apr 2023
Dynamic Recognition of Speakers for Consent Management by Contrastive
  Embedding Replay
Dynamic Recognition of Speakers for Consent Management by Contrastive Embedding ReplayIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022
Arash Shahmansoori
U. Roedig
160
1
0
17 May 2022
Learning to Transfer with von Neumann Conditional Divergence
Learning to Transfer with von Neumann Conditional DivergenceAAAI Conference on Artificial Intelligence (AAAI), 2021
Ammar Shaker
Shujian Yu
Daniel Oñoro-Rubio
OODDRL
289
1
0
07 Aug 2021
Training Deep Architectures Without End-to-End Backpropagation: A Survey
  on the Provably Optimal Methods
Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal MethodsIEEE Computational Intelligence Magazine (IEEE CIM), 2021
Shiyu Duan
José C. Príncipe
MQ
509
8
0
09 Jan 2021
Kernelized information bottleneck leads to biologically plausible
  3-factor Hebbian learning in deep networks
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networksNeural Information Processing Systems (NeurIPS), 2020
Roman Pogodin
P. Latham
488
46
0
12 Jun 2020
1
Page 1 of 1