bhanML

graph4nlp

Graph4nlp is the library for the easy use of Graph Neural Networks for NLP. Welcome to visit our DLG4NLP website (https://dlg4nlp.github.io/index.html) for various learning resources!

latex-templates

A collection of LaTeX templates used for research, courses, and miscellanea.

Papers-of-Robust-ML

Related papers for robust machine learning

TRADES

TRADES (TRadeoff-inspired Adversarial DEfense via Surrogate-loss minimization)

Co-teaching

NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

learning-to-reweight-examples

Code for paper "Learning to Reweight Examples for Robust Deep Learning"

ODE-DL

Paper List For Linking ODE and Deep Learning

Friendly-Adversarial-Training

Attacks Which Do Not Kill Training Make Adversarial Learning Stronger (ICML2020 Paper)

BCL

[ICML2022] Contrastive Learning with Boosted Memorization

T-Revision

NeurIPS'2019: Are Anchor Points Really Indispensable in Label-Noise Learning?

coteaching_plus

ICML'19 How does Disagreement Help Generalization against Label Corruption?

Masking

NeurIPS'18: Masking: A New Perspective of Noisy Supervision

Imitation-Learning-from-Imperfect-Demonstration

[ICML 2019] Implementation of "Imitation Learning from Imperfect Demonstration"

SFAT

[ICLR 2023] "Combating Exacerbated Heterogeneity for Robust Models in Federated Learning"

VHL-paddle

translation of VHL repo in paddle

SFAR-paddle

SAMMD

This is the source code for Maximum Mean Discrepancy Test is Aware of Adversarial Attacks (ICML2021).

FasTer

Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations. ICML-2019.

vild_code

Source code of "Variational Imitation Learning with Diverse-quality Demonstrations" in ICML 2020. This github repository includes python code and datasets used in the experiments.

CAVL

Exploiting Class Activation Value for Partial-Label Learning, ICLR 2022 (poster)

dual-t-reducing-estimation-error-for-transition-matrix-in-label-noise-learning

MAIL

source code for NeurIPS21 paper robabilistic Margins for Instance Reweighting in Adversarial Training

TOHAN

Source code for NeurIPS 2021 paper "TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation".

instance-dependent-label-noise

Safeguarded-Dynamic-Label-Regression-for-Noisy-Supervision

Robust-ResNet

Towards Robust ResNet: A Small Step but a Giant Leap (IJCAI 2019)

dsnet

Code for ICML'21 paper Learning Diverse-Structured Networks for Adversarial Robustness

Max-Matching

LCCN

Latent Class-Conditional Noise Model

Robust-ResNet

IJCAI'19: Towards Robust ResNet: A Small Step but a Giant Leap