A curated (most recent) list of resources for Learning with Noisy Labels

Offical implementation of "Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series" (ICLR 2022)

A ready-to-use framework of the state-of-the-art models for structured (tabular) data learning with PyTorch. Applications include recommendation, CRT prediction, healthcare analytics, anomaly detec…

NeurIPS 2020, "A Topological Filter for Learning with Label Noise".

Collection of Summer 2023 tech internships!

About Code release for "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight), https://openreview.net/forum?id=LzQQ89U1qm_

Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos

Coder of the paper 'Latent Outlier Exposure for Anomaly Detectin with Contaminated Data' published in ICML 2022

Code for "Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection" @ SIGKDD2021

Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks

A log analysis toolkit for automated anomaly detection [ISSRE'16]

A curated list of resources for Learning with Noisy Labels

GAIA, with the full name Generic AIOps Atlas, is an overall dataset for analyzing operation problems such as anomaly detection, log analysis, fault localization, etc.

log anomaly detection via BERT

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Repository for Content-Aware Transformer

Pytorch Implementation of DeepLog.

Open Source ML Model Versioning, Metadata, and Experiment Management

Towards a Rigorous Evaluation of Time-series Anomaly Detection (AAAI'22)

A large collection of system log datasets for AI-powered log analytics

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

NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"

PyTorch Tutorial for Deep Learning Researchers

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

AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise