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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
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  • Motivation
  • Overview
  • Reference

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  1. Sep

Learning to stop while learning to predict

ICML2020 9-25-2020

PreviousLarge-Scale Long-Tailed Recognition in an Open WorldNextAdaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift

Last updated 4 years ago

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Motivation

  • Task-imbalanced meta learning: different tasks need different numbers of gradient steps for adaptation

  • Deep learning based algorithms usually have a fixed number of iterations in the architecture.

could we learn to stop automatically?

Overview

Reference

predictive model Fθ\mathcal{F}_\thetaFθ​ : transforms the input x to generate a path of states x1,x2,...,xTx_1, x_2, ...,x_Tx1​,x2​,...,xT​

stop policy πϕ\pi_\phiπϕ​ : sequentially observes the states xtx_txt​ and determines the probability of stop at layer ttt

variational stop time distribution qϕq_\phiqϕ​ : stop time distribution induced by stopping policy πϕ\pi_\phiπϕ​

https://icml.cc/Conferences/2020/ScheduleMultitrack?event=6279
https://icml.cc/media/Slides/icml/2020/virtual(no-parent)-14-17-00UTC-6279-learning_to_sto.pdf
https://www.youtube.com/watch?v=3VqzhP44Eic