quarktim's Projects
Code for paper "Enhancing Stock Movement Prediction with Adversarial Training" IJCAI 2019
Aircraft design optimization made fast through modern automatic differentiation. Composable analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more.
Roadmap to becoming an Artificial Intelligence Expert in 2022
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)
A programming framework for agentic AI. Join our Discord: https://discord.gg/pAbnFJrkgZ
Papers about explainability of GNNs
A curated list of awesome Python frameworks, libraries, software and resources
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We will keep updating the paper list about machine learning + causal theory. We also internally discuss related papers between NExT++ (NUS) and LDS (USTC) by week.
Official code for CVPR2022 paper: Depth-Aware Generative Adversarial Network for Talking Head Video Generation
A library for scientific machine learning and physics-informed learning
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
FMZ backtest engine python package
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quantitative trading with Javascript, Python, C++, Blockly, MyLanguage(ιΊ¦θ―θ¨)
18 Lessons, Get Started Building with Generative AI π https://microsoft.github.io/generative-ai-for-beginners/
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Computational Fluid Dynamics in JAX
bt - flexible backtesting for Python
MACH: MDO of Aircraft Configurations with High fidelity
OpenMDAO repository.
Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
CS7641 Team project
A differentiable PDE solving framework for machine learning
Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.
Papers for AI + quantitative investment
Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, Shooting Star, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD