📒
PaperNotes
  • PAPER NOTES
  • Meta-Learning with Implicit Gradient
  • DARTS: Differentiable Architecture Search
  • Meta-Learning of Neural Architectures for Few-Shot Learning
  • Towards Fast Adaptation of Neural Architectures with Meta Learning
  • Editable Neural Networks
  • ANIL (Almost No Inner Loop)
  • Meta-Learning Representation for Continual Learning
  • Learning to learn by gradient descent by gradient descent
  • Modular Meta-Learning with Shrinkage
  • NADS: Neural Architecture Distribution Search for Uncertainty Awareness
  • Modular Meta Learning
  • Sep
    • Incremental Few Shot Learning with Attention Attractor Network
    • Learning Steady-States of Iterative Algorithms over Graphs
      • Experiments
    • Learning combinatorial optimization algorithms over graphs
    • Meta-Learning with Shared Amortized Variational Inference
    • Concept Learners for Generalizable Few-Shot Learning
    • Progressive Graph Learning for Open-Set Domain Adaptation
    • Probabilistic Neural Architecture Search
    • Large-Scale Long-Tailed Recognition in an Open World
    • Learning to stop while learning to predict
    • Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift
    • Learning to Generalize: Meta-Learning for Domain Generalization
  • Oct
    • Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
    • Network Architecture Search for Domain Adaptation
    • Continuous Meta Learning without tasks
    • Learning Causal Models Online
    • Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
    • Conditional Neural Progress (CNPs)
    • Reviving and Improving Recurrent Back-Propagation
    • Meta-Q-Learning
    • Learning Self-train for semi-supervised few shot classification
    • Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
  • Nov
    • Neural Process
    • Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
    • Learning to Adapt to Evolving Domains
  • Tutorials
    • Relax constraints to continuous
    • MAML, FO-MAML, Reptile
    • Gradient Descent
      • Steepest Gradient Descent
      • Conjugate Gradient Descent
  • KL, Entropy, MLE, ELBO
  • Coding tricks
    • Python
    • Pytorch
  • ml
    • kmeans
Powered by GitBook
On this page

Was this helpful?

  1. Tutorials

Gradient Descent

PreviousMAML, FO-MAML, ReptileNextSteepest Gradient Descent

Last updated 4 years ago

Was this helpful?

https://blog.csdn.net/weixin_40170902/article/details/80092628
https://cloud.tencent.com/developer/article/1118673
https://zhuanlan.zhihu.com/p/97873519
https://zhuanlan.zhihu.com/p/147569674?utm_source=wechat_session&utm_medium=social&utm_oi=45678843658240
https://towardsdatascience.com/a-visual-explanation-of-gradient-descent-methods-momentum-adagrad-rmsprop-adam-f898b102325c
https://zhuanlan.zhihu.com/p/32230623
https://blog.csdn.net/aws3217150/article/details/70214422
https://zhuanlan.zhihu.com/p/114248570
https://www.zhihu.com/question/354819140/answer/895186613
http://gitlinux.net/2019-02-26-optimizers/#43-%E6%A2%AF%E5%BA%A6%E6%88%AA%E6%96%AD