Jia-Xiang Cheng's repositories
PyTorch-PDQN-for-Digital-Twin-ACS
PyTorch implementation of RIC for conveyor systems with Deep Q-Networks (DQN) and Profit-Sharing (PS). Wang, T., Cheng, J., Yang, Y., Esposito, C., Snoussi, H., & Tao, F. (2020). Adaptive Optimization Method in Digital Twin Conveyor Systems via Range-Inspection Control. IEEE Transactions on Automation Science and Engineering.
auton-survival
Deep Survival Machines - Fully Parametric Survival Regression
pdm-dataset
Datasets for Predictive Maintenance
PyTorch-Tutorials
Simple implementation for basic tasks.
Auto-GPT
An experimental open-source attempt to make GPT-4 fully autonomous.
definer
By DeFiNER, Decentralized Finance Navigates Every Route. A Solution Framework for Modeling and Hedging Impermanent Loss and Dynamic Liquidity Provision Using Deep Reinforcement Learning in Uniswap V3 with Concentrated Liquidity. Fintech-As-A-Service: Hackathon of NUS Fintech Summit 2024.
llm-course
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
PyTorch-SurvNAM
PyTorch implementation of SurvNAM (under development actively)
AI-Expert-Roadmap
Roadmap to becoming an Artificial Intelligence Expert in 2022
Deep-Recurrent-Survival-Analysis
Deep Recurrent Survival Analysis, an auto-regressive deep model for time-to-event data analysis with censorship handling. An implementation of our AAAI 2019 paper and a benchmark for several (Python) implemented survival analysis methods.
FinBERT-QA
Financial Domain Question Answering with pre-trained BERT Language Model
gpt-engineer
Specify what you want it to build, the AI asks for clarification, and then builds it.
llama
Inference code for LLaMA models
openai-cookbook
Examples and guides for using the OpenAI API
photography
A free online portfolio website to showcase your photos.
pykan
Kolmogorov Arnold Networks
sdhasidhas
v4 hook to automatically hedge impermanent loss with options
squeeth-monorepo
Squeeth is a new financial primitive in DeFi that gives traders exposure to ETH²
SurvLIMEpy
Local interpretability for survival models
survml-deepweisurv
PyTorch implementation of DeepWeiSurv, by Bennis, A., Mouysset, S., & Serrurier, M. (2020, May). Estimation of conditional mixture Weibull distribution with right censored data using neural network for time-to-event analysis. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 687-698). Springer, Cham.
survml-nsc
Implementation for the paper Neural Survival Clustering: Non parametric mixture of neural networks for survival clustering
survml-pycox
Survival analysis with PyTorch
survshap
SurvSHAP(t): Time-dependent explanations of machine learning survival models
YOLOv5
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite