📈 Adaptive: parallel active learning of mathematical functions
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Updated
Mar 2, 2026 - Python
📈 Adaptive: parallel active learning of mathematical functions
A flexible, adaptive classification system for dynamic text classification
🧠 AI-powered Personalized Exam System: Integrating OpenPangu LLM, Knowledge Graph RAG, and BKT algorithm for adaptive question generation and recommendation. 基于LLM与知识图谱的智能个性化出题系统。
Codes for "Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control", ICASSP 2022
The Tornado 🌪️ framework, designed and implemented for adaptive online learning and data stream mining in Python.
An adaptive model for prediction of one day ahead foreign currency exchange rates using machine learning algorithms
Code for "End-to-End Adaptive Sampling and Representation for Event-based Detection with Recurrent Spiking Neural Networks", ECCV 2024
This work investigates automatic hyperparameter tuning for planners such as DWA and TEB, and our navigation framework LE-Nav can be used to adjust hyperparameters of any optimization-based planner.
Run many functions (adaptively) on many cores (>10k-100k) using mpi4py.futures, ipyparallel, loky, or dask-mpi. 🎉
AI chaos reasoning persona
Framework for building AI agents with real-time adaptive learning capabilities.
The simulator for education
Conditional Associative Logic Memory
Repository for Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction (CVPR2019)
Personalized Training for the Sequence Learning task with the NAO robot and the MUSE EEG sensor
[MGE Advances 2025] Offical implement of BgoFace
AutodidactAI is an AI-driven platform that empowers continuous learning and self-improvement. Inspired by the concept of ikigai, AutodidactAI seeks to find purpose in knowledge acquisition and enhance its performance through iterative feedback and adaptation, embodying the principles of self-directed learning and continuous improvement
The RESSPECT project is a result from an inter-collaboration agreement established between the Cosmostatistics Initiative (COIN) and the LSST Dark Energy Science Collaboration (DESC) with the goal of developing a recommendation system for telescope resource allocation able to optimize photometric supernova cosmology anaylsis.
Adaptive STEM learning system using Q-learning to personalize educational pathways. Integrated with Moodle via LTI 1.3. Achieves 22.5% score improvement and 51% reduction in weak skills through AI-driven recommendations and explainable decisions (SHAP).
AdaptFNO: Adaptive Fourier Neural Operator with Dynamic Spectral Modes and Multiscale Learning
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