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meta-weight-net. NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).

Meta-weight-net_class-imbalance. NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for class imbalance).

CMW-Net. Pytorch implementation of TPAMI2023: CMW-NetCMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning

Auto-6ML. Auto^6ML is a jittor library allowing users to achieve machine learning automation.

Meta-SPL. Pytorch implementation for Meta-SPL (self-paced learning).

MLR-SNet. This is an official PyTorch implementation of MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks

Multitask-Learning. Multitask Learning Resources

SLeM-Theory. The implementation of meta-regularization proposed in SLeM theory paper "Learning an Explicit Hyper-parameter Prediction Function Conditioned on Tasks".

Probabilistic-MW-Net. TNNLS2021: A Probabilistic Formulation for Meta-Weight-Net (Pytorch implementation for noisy labels)

Awesome-NAS. A curated list of neural architecture search (NAS) resources.

awesome-AutoML. A curated list of AutoML papers/tutorials/slides etc.

COPZoo. Neural Network for solving challenging Combinatorial Optimization Problems

continual-learning. PyTorch implementation of various methods for continual learning (XdG, EWC, online EWC, SI, LwF, DGR, DGR+distill, RtF, iCaRL).

meta-transfer-learning-tensorflow. TensorFlow implementation for "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)

In-Context-Learning_PaperList. Paper List for In-context Learning 🌷