arXiv:2111.15258
공식 코드 · Papers with Code
DeepAL: Deep Active Learning in Python
원문 초록
We present DeepAL, a Python library that implements several common strategies for active learning, with a particular emphasis on deep active learning. DeepAL provides a simple and unified framework based on PyTorch that allows users to easily load custom datasets, build custom data handlers, and design custom strategies without much modification of codes. DeepAL is open-source on Github and welcome any contribution.
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Near-Polynomially Competitive Active Logistic Regression
CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization
Active Learning for Manifold Gaussian Process Regression
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Info-Coevolution: An Efficient Framework for Data Model Coevolution
ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
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