Pinned Repositories
Auto-6ML
Auto^6ML is a jittor library allowing users to achieve machine learning automation.
awesome-AutoML
A curated list of AutoML papers/tutorials/slides etc.
CMW-Net
Pytorch implementation of TPAMI2023: CMW-NetCMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning
Meta-SPL
Pytorch implementation for Meta-SPL (self-paced learning).
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).
MLR-SNet
This is an official PyTorch implementation of MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks
Multitask-Learning
Multitask Learning Resources
Probabilistic-MW-Net
TNNLS2021: A Probabilistic Formulation for Meta-Weight-Net (Pytorch implementation for noisy labels)
SLeM-Theory
The implementation of meta-regularization proposed in SLeM theory paper "Learning an Explicit Hyper-parameter Prediction Function Conditioned on Tasks".
xjtushujun's Repositories
xjtushujun/meta-weight-net
NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).
xjtushujun/Meta-weight-net_class-imbalance
NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for class imbalance).
xjtushujun/CMW-Net
Pytorch implementation of TPAMI2023: CMW-NetCMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning
xjtushujun/Auto-6ML
Auto^6ML is a jittor library allowing users to achieve machine learning automation.
xjtushujun/Meta-SPL
Pytorch implementation for Meta-SPL (self-paced learning).
xjtushujun/MLR-SNet
This is an official PyTorch implementation of MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks
xjtushujun/Multitask-Learning
Multitask Learning Resources
xjtushujun/SLeM-Theory
The implementation of meta-regularization proposed in SLeM theory paper "Learning an Explicit Hyper-parameter Prediction Function Conditioned on Tasks".
xjtushujun/Probabilistic-MW-Net
TNNLS2021: A Probabilistic Formulation for Meta-Weight-Net (Pytorch implementation for noisy labels)
xjtushujun/Awesome-NAS
A curated list of neural architecture search (NAS) resources.
xjtushujun/Advances-in-Label-Noise-Learning
A curated (most recent) list of resources for Learning with Noisy Labels
xjtushujun/Awesome-Knowledge-Distillation
Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。
xjtushujun/Best-Incremental-Learning
An Incremental Learning, Continual Learning, and Life-Long Learning Repository
xjtushujun/Class-Imbalance
Cost-Sensitive Learning / Resampling / SMOTE etc.
xjtushujun/deep-value-networks-pytorch
Structured Prediction with Deep Value Networks (PyTorch implementation)
xjtushujun/dirichlet_python
xjtushujun/DivideMix
Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning
xjtushujun/fixmatch
A simple method to perform semi-supervised learning with limited data.
xjtushujun/higher
higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.
xjtushujun/In-Context-Learning_PaperList
Paper List for In-context Learning 🌷
xjtushujun/junshu.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
xjtushujun/label_smoothing_pytorch
pytorch implement of Label Smoothing
xjtushujun/LDAM-DRW
[NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
xjtushujun/machine-learning-notes
My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (1500+ slides) 我不间断更新的机器学习,概率模型和深度学习的讲义(1500+页)和视频链接
xjtushujun/MetaLearningPapers
A classified list of meta learning papers based on realm.
xjtushujun/mixupfamily
The implementation of mixup and mainfold mixup method with standard models(PreActRes, WideRes, Dense) in Cifar10, Cifar100 and SVHN dataset on supervised(sl) and semi-supervised(ssl) tasks.
xjtushujun/NARL-Adjuster
This is an official PyTorch implementation of Improve Noise Tolerance of Robust Loss via Noise-Awareness
xjtushujun/pytorch-maml
An Implementation of Model-Agnostic Meta-Learning in PyTorch with Torchmeta
xjtushujun/quip_paad_cancer_detection
xjtushujun/TANS
This is an official PyTorch implementation of Task-Adaptive Neural Network Search with Meta-Contrastive Learning (NeurIPS 2021, Spotlight).