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A reading list on LLM based Synthetic Data Generation 🔥
Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step
[ASE'24] Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?
[NeurIPS 2024] Official implementation of MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection.
A list of awesome academic researches and industrial materials about Large Language Model (LLM) and Artificial Intelligence for IT Operations (AIOps).
Code for our paper "VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters".
cluster data collected from production clusters in Alibaba for cluster management research
A Fair and Scalable Time Series Forecasting Benchmark and Toolkit.
High accuracy RAG for answering questions from scientific documents with citations
A one-of-a-kind resume builder that keeps your privacy in mind. Completely secure, customizable, portable, open-source and free forever. Try it out today!
📺 Discover the latest machine learning / AI courses on YouTube.
Time series distances: Dynamic Time Warping (fast DTW implementation in C)
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…
Chat first code editor. To download the packaged app:
[ACL 2024 Best Paper] Deciphering Oracle Bone Language with Diffusion Models
TODS: An Automated Time-series Outlier Detection System
An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection
FITS: Frequency Interpolation Time Series Analysis Baseline
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
A python library for user-friendly forecasting and anomaly detection on time series.
[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"
Scalable and user friendly neural 🧠 forecasting algorithms.
MemAE for anomaly detection. -- Gong, Dong, et al. "Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection". ICCV 2019.
Code for Retrieval-Based Reconstruction For Time-series Contrastive Learning (ICLR 2024)
Run PyTorch LLMs locally on servers, desktop and mobile