adversarial-robustness-toolbox
FinRL
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| adversarial-robustness-toolbox | FinRL | |
|---|---|---|
| 8 | 44 | |
| 4,460 | 9,093 | |
| 2.9% | 3.3% | |
| 9.7 | 8.8 | |
| 6 days ago | 4 days ago | |
| Python | Jupyter Notebook | |
| MIT License | MIT License |
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adversarial-robustness-toolbox
- [D] Couldn't devs of major GPTs have added an invisible but detectable watermark in the models?
- [D] ML Researchers/Engineers in Industry: Why don't companies use open source models more often?
- [D]: How safe is it to just use a strangers Model?
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[D] Does anyone care about adversarial attacks anymore?
Check out this project https://github.com/Trusted-AI/adversarial-robustness-toolbox
- adversarial-robustness-toolbox: Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
- Library for Machine Learning Security Evasion, Poisoning, Extraction, Inference
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Introduction to Adversarial Machine Learning
Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference.
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[D] Testing a model's robustness to adversarial attacks
Depending on what attacks you want I've found both https://github.com/cleverhans-lab/cleverhans and https://github.com/Trusted-AI/adversarial-robustness-toolbox to be useful.
FinRL
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Batendo BOVA11 - Approach usando Reinforcement Learning
FinRL ---> https://github.com/AI4Finance-Foundation/FinRL
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Need help to get started
There is a roadmap here with tutorials from AI4fince. It helped me get started. https://github.com/AI4Finance-Foundation/FinRL/tree/master/examples
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[D] ML Researchers/Engineers in Industry: Why don't companies use open source models more often?
Definitely! An example could be the use of https://github.com/AI4Finance-Foundation/FinRL in quant-firms and fintech.
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Reinforcement learning for algorithmic trading?
I recently came across this GitHub repo on using reinforcement learning (RL) for algorithmic trading - https://github.com/AI4Finance-Foundation/FinRL. Would using RL be a realistic and profitable algotrading strategy ?
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Is anyone interested in joining a DeepRL-based algotrading project?
there is quite popular DRL project in finance: https://github.com/AI4Finance-Foundation/FinRL
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Does anyone have experience with Reinforcement Learning (RL)?
You can have a look at this repo,may help you to start your journey: https://github.com/AI4Finance-Foundation/FinRL
- Anyone worked with FinRL?
- reinforcement learning in finance and trading
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Feedback Collection for FinRL: Financial Reinforcement Learning
As a creator of the open-source FinRL project, I would like to welcome all kinds of feedback regarding financial reinforcement learning, especially about how to improve the open-source project FinRL.
- What should I change in my hyperparameter to improve this model? Should I lower or increase the learning rate?
What are some alternatives?
DeepRobust - A pytorch adversarial library for attack and defense methods on images and graphs
tensortrade - An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.
auto-attack - Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
gym-anytrading - The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)
TextAttack - TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/
FinRL-Meta - FinRL-Meta: Dynamic datasets and market environments for FinRL.
alpha-zero-boosted - A "build to learn" Alpha Zero implementation using Gradient Boosted Decision Trees (LightGBM)
FinRL-Library - Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance. NeurIPS 2020 & ICAIF 2021. 🔥 [Moved to: https://github.com/AI4Finance-Foundation/FinRL]
m2cgen - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
Deep-Hedging
waf-bypass - Check your WAF before an attacker does
Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020 - Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. ICAIF 2020. Please star. [Moved to: https://github.com/AI4Finance-Foundation/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020]