The first exam for code agents in the physical world: frozen robot tasks from many benchmark suites, each answered with a program and verified in a separate sandbox. Runs on Harbor.
- ai-agents
- benchmark
- coding-agents
- embodied-ai
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The first exam for code agents in the physical world: frozen robot tasks from many benchmark suites, each answered with a program and verified in a separate sandbox. Runs on Harbor.
scikit-learn wrappers for Python fastText.
Automated 3D cell detection in very large images
A repository of learning & R resources related to topic models
Learning Swift by working through example code in playgrounds
A fast xgboost feature selection algorithm
Deep and Machine Learning for Microscopy
Stacked Generalization (Ensemble Learning)
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
Companion repository for “In-Context Learning for Robots: Methods and Applications” — methods, datasets, and benchmarks organized by taxonomy and release date.
Style Art library process images using COREML with a set of pre trained machine learning models and convert them to Art style.
Deep Reinforcement Learning for Financial Trading using Price Trailing @ ICASSP 2019
Learn how to start using Markdown
Teaching materials for a future Foundation of Embodied AI course, with Chinese and English tutorials on robot dynamics, Isaac Sim, Isaac Lab, MuJoCo, robot learning, ROS 2, camera calibration, PPO, SAC, Flow Matching, GR00T, and SONIC.
The Deep Learning training framework on Spark
learn stuff with pen + paper + AI
Reading irctc captchas with 98% accuracy using deep learning
Build JSON API-compliant APIs on Rails with no (or less) learning curve.
Protect your SIP Servers from bad actors at https://sentrypeer.org
This is an aspiring project aimed at accumulating knowledge from the world of cybersecurity and presenting it in a cogent way, so it is accessible to as large an audience as possible and so that everyone has a good resource to learn hacking from.
✅ Complete beginners tutorial building a todo list from scratch in Phoenix 1.7 (latest)
Usiigaci: stain-free cell tracking in phase contrast microscopy enabled by supervised machine learning
BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.
Meta Self-learning for Multi-Source Domain Adaptation: A Benchmark