arXiv:2101.00982
공식 코드 · Papers with Code
Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification
원문 초록
Uncertainty and confidence have been shown to be useful metrics in a wide variety of techniques proposed for deep learning testing, including test data selection and system supervision.We present uncertainty-wizard, a tool that allows to quantify such uncertainty and confidence in artificial neural networks. It is built on top of the industry-leading tf.keras deep learning API and it provides a near-transparent and easy to understand interface. At the same time, it includes major performance optimizations that we benchmarked on two different machines and different configurations.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Statistical Machine Learning for Astronomy -- A Textbook
Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Estimating prevalence with precision and accuracy
UQLM: A Python Package for Uncertainty Quantification in Large Language Models
Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference
Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
토론
이 논문과 연결된 포럼 글이 아직 없습니다.