blacksnail789521/Agentic-RL-Training-Recipes
Training Recipes for Agentic Reinforcement Learning in LLMs: A Survey
Staff ML Research Scientist @ Pravāh | Time Series Analysis & Agentic RL
Training Recipes for Agentic Reinforcement Learning in LLMs: A Survey
A modular forecasting library for Irregular Multimodal Multivariate Time Series, accompanying the NeurIPS 2025 paper “Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series.”
A benchmark dataset for Irregular Multimodal Multivariate Time Series, introduced in the NeurIPS 2025 paper “Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series.”
Ching (Jason) Chang's Personal Homepage
[TMLR 2026] A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models
Official implementation of Perseus, accepted at IEEE ICDM 2026: stateful interactive time-series segmentation with sparse supervision.
🎧 Practice English shadowing using YouTube videos with subtitles, Whisper AI, and a simple VLC-powered desktop app. No API keys needed — 100% local and free!
Official repository for LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters, accepted at ACM TIST 2025.
Official codebase for PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation, accepted to CIKM 2025
Official repository for TimeDRL: Disentangled Representation Learning for Multivariate Time-Series, accepted at ICDE 2024.
A Library for Advanced Deep Time Series Models.
The official repository of paper LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting (NeurIPS 2023)
Official Code for DragGAN (SIGGRAPH 2023)
DiffusionFastForward: a free course and experimental framework for diffusion-based generative models
High-speed download of LLaMA, Facebook's 65B parameter GPT model
The simplest, fastest repository for training/finetuning medium-sized GPTs.
A python library for easy manipulation and forecasting of time series.
Datasets, Transforms and Models specific to Computer Vision