Pinned Repositories
auton-survival
Deep Survival Machines - Fully Parametric Survival Regression
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.
NTU-EE6225.2
MIMO controller design using ETF and BLT.
PyTorch-CNN-for-RUL-Prediction
PyTorch implementation of CNN for remaining useful life prediction. Inspired by Babu, G. S., Zhao, P., & Li, X. L. (2016, April). Deep convolutional neural network-based regression approach for estimation of remaining useful life. In International conference on database systems for advanced applications (pp. 214-228). Springer, Cham.
PyTorch-LSTM-for-RUL-Prediction
PyTorch implementation of remaining useful life prediction with long-short term memories (LSTM), performing on NASA C-MAPSS data sets. Partially inspired by Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017, June). Long short-term memory network for remaining useful life estimation.
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.
PyTorch-Transformer-for-RUL-Prediction
Transformer implementation with PyTorch for remaining useful life prediction on turbofan engine with NASA CMAPSS data set. Inspired by Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing, 1-10.
Random-Weighted-Bootstrap-with-Weibull
Reproduction of the work by Hong, Y., Meeker, W. Q., & McCalley, J. D. (2009). Prediction of remaining life of power transformers based on left truncated and right censored lifetime data. Annals of Applied Statistics, 3(2), 857-879.
RUL-prediction-using-attention-based-deep-learning-approach
jiaxiang-cheng's Repositories
jiaxiang-cheng/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.
jiaxiang-cheng/RUL-prediction-using-attention-based-deep-learning-approach
jiaxiang-cheng/auton-survival
Deep Survival Machines - Fully Parametric Survival Regression
jiaxiang-cheng/Build-Website-with-ChatGPT
jiaxiang-cheng/gigaGPT
a small code base for training large models
jiaxiang-cheng/jiaxiang-cheng.github.io
jiaxiang-cheng/pdm-dataset
Datasets for Predictive Maintenance
jiaxiang-cheng/PyTorch-Tutorials
Simple implementation for basic tasks.
jiaxiang-cheng/Auto-GPT
An experimental open-source attempt to make GPT-4 fully autonomous.
jiaxiang-cheng/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.
jiaxiang-cheng/llm-course
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
jiaxiang-cheng/PyTorch-SurvNAM
PyTorch implementation of SurvNAM (under development actively)
jiaxiang-cheng/AI-Expert-Roadmap
Roadmap to becoming an Artificial Intelligence Expert in 2022
jiaxiang-cheng/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.
jiaxiang-cheng/FinBERT-QA
Financial Domain Question Answering with pre-trained BERT Language Model
jiaxiang-cheng/gpt-engineer
Specify what you want it to build, the AI asks for clarification, and then builds it.
jiaxiang-cheng/jiaxiang-cheng
jiaxiang-cheng/llama
Inference code for LLaMA models
jiaxiang-cheng/openai-cookbook
Examples and guides for using the OpenAI API
jiaxiang-cheng/photography
A free online portfolio website to showcase your photos.
jiaxiang-cheng/pykan
Kolmogorov Arnold Networks
jiaxiang-cheng/sdhasidhas
v4 hook to automatically hedge impermanent loss with options
jiaxiang-cheng/squeeth-monorepo
Squeeth is a new financial primitive in DeFi that gives traders exposure to ETH²
jiaxiang-cheng/SurvLIMEpy
Local interpretability for survival models
jiaxiang-cheng/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.
jiaxiang-cheng/survml-nsc
Implementation for the paper Neural Survival Clustering: Non parametric mixture of neural networks for survival clustering
jiaxiang-cheng/survml-pycox
Survival analysis with PyTorch
jiaxiang-cheng/survshap
SurvSHAP(t): Time-dependent explanations of machine learning survival models
jiaxiang-cheng/Tensorflow-SurvNAM
jiaxiang-cheng/YOLOv5
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite