# PaperNotes ## PaperNotes - [PAPER NOTES](https://lichangbin.gitbook.io/paper_notes/master.md): This folder is used to archive paper notes. - [Meta-Learning with Implicit Gradient](https://lichangbin.gitbook.io/paper_notes/meta-learning-with-implicit-gradient.md): NIPS 2019 - [DARTS: Differentiable Architecture Search](https://lichangbin.gitbook.io/paper_notes/darts-differentiable-architecture-search.md): ICLR 2019 8/19/2020 - [Meta-Learning of Neural Architectures for Few-Shot Learning](https://lichangbin.gitbook.io/paper_notes/meta-learning-of-neural-architectures-for-few-shot-learning.md): CVPR 2020 8-22-2020 - [Towards Fast Adaptation of Neural Architectures with Meta Learning](https://lichangbin.gitbook.io/paper_notes/towards-fast-adaptation-of-neural-architectures-with-meta-learning.md) - [Editable Neural Networks](https://lichangbin.gitbook.io/paper_notes/editable-neural-networks.md): ICLR 2020 8-22-2020 - [ANIL (Almost No Inner Loop)](https://lichangbin.gitbook.io/paper_notes/anil.md): ICLR 2020 8-23-2020 - [Meta-Learning Representation for Continual Learning](https://lichangbin.gitbook.io/paper_notes/meta-learning-representation-for-continual-learning.md): NeurIPS 2019 8-24-2020 - [Learning to learn by gradient descent by gradient descent](https://lichangbin.gitbook.io/paper_notes/learning-to-learn-by-gradient-descent-by-gradient-descent.md): NIPS 2016 8-24-2020 - [Modular Meta-Learning with Shrinkage](https://lichangbin.gitbook.io/paper_notes/modular-meta-learning-with-shrinkage.md): 8-24-2020 - [NADS: Neural Architecture Distribution Search for Uncertainty Awareness](https://lichangbin.gitbook.io/paper_notes/nads-neural-architecture-distribution-search-for-uncertainty-awarenessrandy.md): ICML 2020 08-28-2020 - [Modular Meta Learning](https://lichangbin.gitbook.io/paper_notes/modular-meta-learning.md): CoRL 2018 - [Incremental Few Shot Learning with Attention Attractor Network](https://lichangbin.gitbook.io/paper_notes/sep/incremental-few-shot-learning-with-attention-attractor-network.md): NeurIPS 2019 9/7/2020 - [Learning Steady-States of Iterative Algorithms over Graphs](https://lichangbin.gitbook.io/paper_notes/sep/learning-steady-states-of-iterative-algorithms-over-graphs.md): ICML 2018 9/15/2020 - [Experiments](https://lichangbin.gitbook.io/paper_notes/sep/learning-steady-states-of-iterative-algorithms-over-graphs/experiments.md) - [Learning combinatorial optimization algorithms over graphs](https://lichangbin.gitbook.io/paper_notes/sep/learning-combinatorial-optimization-algorithms-over-graphs.md): NIPS 2017 9-22-2020 - [Meta-Learning with Shared Amortized Variational Inference](https://lichangbin.gitbook.io/paper_notes/sep/meta-learning-with-shared-amortized-variational-inference.md): (ICML 2020) - [Concept Learners for Generalizable Few-Shot Learning](https://lichangbin.gitbook.io/paper_notes/sep/concept-learners-for-generalizable-few-shotlearning.md) - [Progressive Graph Learning for Open-Set Domain Adaptation](https://lichangbin.gitbook.io/paper_notes/sep/progressive-graph-learning-for-open-set-domain-adaptation.md) - [Probabilistic Neural Architecture Search](https://lichangbin.gitbook.io/paper_notes/sep/probabilistic-neural-architecture-search.md) - [Large-Scale Long-Tailed Recognition in an Open World](https://lichangbin.gitbook.io/paper_notes/sep/large-scale-long-tailed-recognition-in-an-open-world.md): 9-25-2020 - [Learning to stop while learning to predict](https://lichangbin.gitbook.io/paper_notes/sep/learning-to-stop-while-learning-to-predict.md): ICML2020 9-25-2020 - [Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift](https://lichangbin.gitbook.io/paper_notes/sep/adaptive-risk-minimization-a-meta-learning-approach-for-tackling-group-shift.md): 9-28-2020 - [Learning to Generalize: Meta-Learning for Domain Generalization](https://lichangbin.gitbook.io/paper_notes/sep/learning-to-generalize-meta-learning-for-domain-generalization.md): AAAI-18 9-28-2020 - [Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization](https://lichangbin.gitbook.io/paper_notes/oct/meta-learning-acquisition-functions-for-transfer-learning-in-bayesian-optimization.md): ICLR 20 10-2-2020 - [Network Architecture Search for Domain Adaptation](https://lichangbin.gitbook.io/paper_notes/oct/network-architecture-search-for-domain-adaptation.md): arxiv 10-2-2020 - [Continuous Meta Learning without tasks](https://lichangbin.gitbook.io/paper_notes/oct/continuous-meta-learning-without-tasks.md): NeurIPS 20 10-9-2020 - [Learning Causal Models Online](https://lichangbin.gitbook.io/paper_notes/oct/learning-causal-models-online.md): arxiv 10-9-2020 - [Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples](https://lichangbin.gitbook.io/paper_notes/oct/meta-dataset-a-dataset-of-datasets-for-learning-to-learn-from-few-examples.md): ICLR 20 10-23-2020 - [Conditional Neural Progress (CNPs)](https://lichangbin.gitbook.io/paper_notes/oct/conditional-neural-progress.md): ICML 18 10-23-2020 - [Reviving and Improving Recurrent Back-Propagation](https://lichangbin.gitbook.io/paper_notes/oct/reviving-and-improving-recurrent-back-propagation.md): ICML 18 10-26-2020 - [Meta-Q-Learning](https://lichangbin.gitbook.io/paper_notes/oct/meta-q-learning.md): ICLR 20 (Talk) 10-26-2020 - [Learning Self-train for semi-supervised few shot classification](https://lichangbin.gitbook.io/paper_notes/oct/learning-self-train-for-semi-supervised-few-shot-classification.md): NeurIPS 19 11/3/2020 - [Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards](https://lichangbin.gitbook.io/paper_notes/oct/watch-try-learn-meta-learning-from-demonstrations-and-rewards.md): ICLR 20, 11/3/2020 - [Neural Process](https://lichangbin.gitbook.io/paper_notes/nov/neural-process.md): arxiv 18 11/9/2020 - [Adversarially Robust Few-Shot Learning: A Meta-Learning Approach](https://lichangbin.gitbook.io/paper_notes/nov/adversarially-robust-few-shot-learning-a-meta-learning-approach.md): NeurIPS 20 11/9/2020 - [Learning to Adapt to Evolving Domains](https://lichangbin.gitbook.io/paper_notes/nov/learning-to-adapt-to-evolving-domains.md): NeurIPS 2020 11-16-2020 - [Relax constraints to continuous](https://lichangbin.gitbook.io/paper_notes/tutorials/relax-constraints-to-continuous.md) - [MAML, FO-MAML, Reptile](https://lichangbin.gitbook.io/paper_notes/tutorials/mathematical-details-in-maml-fo-maml-reptile.md): gradient optimization based meta-learning algorithms - [Gradient Descent](https://lichangbin.gitbook.io/paper_notes/tutorials/gradient-descent.md) - [Steepest Gradient Descent](https://lichangbin.gitbook.io/paper_notes/tutorials/gradient-descent/steepest-gradient-descent.md) - [Conjugate Gradient Descent](https://lichangbin.gitbook.io/paper_notes/tutorials/gradient-descent/conjugate-gradient-descent.md) - [KL, Entropy, MLE, ELBO](https://lichangbin.gitbook.io/paper_notes/kl-entropy-mle.md) - [Python](https://lichangbin.gitbook.io/paper_notes/coding-tricks/python.md) - [Pytorch](https://lichangbin.gitbook.io/paper_notes/coding-tricks/pytorch.md) - [kmeans](https://lichangbin.gitbook.io/paper_notes/ml/kmeans.md)