habitat-sim
imodels
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5 | 7 | |
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8.7 | 8.5 | |
1 day ago | 5 days ago | |
C++ | Jupyter Notebook | |
MIT License | MIT License |
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habitat-sim
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Break into CV with background in biological vision and neuroscience
Spot on! I think you grasped the general idea. During some of my 3d studies, I collected data about hand movements, eye movements and navigation paths within scenes, which could potentially be used for training data in robots (e.g. to train robot arm-suction grip, visual input and navigation respectively). I see projects like this https://aihabitat.org/, where my research seems quite relevant.
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Virtual environment frameworks
I need an easy to set up simulation of a 3d environment and I was wondering what you guys are using. Something like https://aihabitat.org/ . It already comes with rich visuals, which is quite important in my case and it works out of the box so I don't need to waste time developing my own models and graphics. Unfortunately habitat ai doesn't work on windows. Are there some alternatives?
- [D] Have we stopped researching agents?
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[D] Looking for open source projects to contribute
There are plenty of them out there. I spend a lot of time contributing to open source projects like Habitat-Sim https://github.com/facebookresearch/habitat-sim and Habitat-Lab https://github.com/facebookresearch/habitat-lab which have a ton of open issues and code maintaince stuff that we would welcome contributions of.
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[R] Best drone simulator for ML purposes
With some hacks it is pretty easy to get drones working in Habitat-Sim: https://github.com/facebookresearch/habitat-sim
imodels
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[D] Have researchers given up on traditional machine learning methods?
- all domains requiring high interpretability absolutely ignore deep learning at all, and put all their research into traditional ML; see e.g. counterfactual examples, important interpretability methods in finance, or rule-based learning, important in medical or law applications
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What would be my best approach given the data I have?
Next, this variable will be your target and you can use various supervised learning models to answer your question. Since interpretation is key, you can use something from here: https://github.com/csinva/imodels or do some black box models and use shab to understand which features contributed most.
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Random Forest Estimation Question
Option 2) fit a model from https://github.com/csinva/imodels on the predicted values of the RF
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UC Berkeley Researchers Introduce โimodels: A Python Package For Fitting Interpretable Machine Learning Models
Despite recent breakthroughs in the formulation and fitting of interpretable models, implementations are frequently challenging to locate, utilize, and compare. imodels solves this void by offering a single interface and implementation for a wide range of state-of-the-art interpretable modeling techniques, especially rule-based methods. imodels is basically a Python tool for predictive modeling that is simple, transparent, and accurate. It gives users a straightforward way to fit and use state-of-the-art interpretable models, all of which are compatible with scikit-learn (Pedregosa et al., 2011). These models can frequently replace black-box models while boosting interpretability and computing efficiency without compromising forecast accuracy. Continue Reading
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[D] Looking for open source projects to contribute
Our package imodels is expanding our sklearn-compatible set of interpretable models and always looking for new contributors!
- imodels: a package extending sklearn with state-of-the-art models for interpretable data science (e.g. Bayesian Rule Lists, RuleFit)
- imodels: a package extending sklearn with state-of-the-art interpretable models (e.g. Bayesian Rule Lists, RuleFit) from BAIR [P]
What are some alternatives?
gazebo-classic - Gazebo classic. For the latest version, see https://github.com/gazebosim/gz-sim
pycaret - An open-source, low-code machine learning library in Python
carla - Open-source simulator for autonomous driving research.
interpret - Fit interpretable models. Explain blackbox machine learning.
bootcamp - Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.
shap - A game theoretic approach to explain the output of any machine learning model.
nn - ๐งโ๐ซ 60 Implementations/tutorials of deep learning papers with side-by-side notes ๐; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), ๐ฎ reinforcement learning (ppo, dqn), capsnet, distillation, ... ๐ง
linear-tree - A python library to build Model Trees with Linear Models at the leaves.
docarray - Represent, send, store and search multimodal data
habitat-lab - A modular high-level library to train embodied AI agents across a variety of tasks and environments.
Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera - Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes