We propose MUMBO, the first high-performing yet computationally efficient acquisition function for multi-task Bayesian optimization. Here, the challenge is to perform efficient optimization by evaluating low-cost functions somehow related to our true target function. This is a broad class of problems ...
【背景】: NIPS2017论文《What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?》中提到,当我们使用数据集,基于输入x和输出y训练模型的时候,面临两种不确定性(Uncertainties):认知不确定性(epistemic)和偶然不确定性(aleatoric)。 认知不确定性(epistemic):指的是由于缺少数据导致的认知偏差。
论文链接:Molecular Language Model as Multi-task Generator 论文代码:huggingface.co/zjunlp/M 论文Demo: huggingface.co/spaces/z 1. Introduction 在化学领域中,分子生成是一项重要的任务。以往的工作通常将其描述为序列生成问题,并提出了四种主要的方法Bayesian Optimization (BO)(Gomez-Bombarelli et al.,2018...
This design allows for simultaneous optimization of the entire network via gradient descent33, contrasting with conventional machine learning techniques that, while robust, may lack discriminative power34. Each single-task model has 3 different inputs: clinical data (mandatory), imaging data (task-...
77 2023 Federated many-task Bayesian optimization IEEE transactions on evolutionary … 13 76 2023 What makes evolutionary multi-task optimization better: A comprehensive survey Applied Soft Computing 17 75 2023 Block-level knowledge transfer for evolutionary multitask optimization IEEE Transactions on … ...
(one choice of model per task, hence the plural) are BSN for all tasks except PCSS, which uses CAPRA score (see Table1, sections A, B & C, boxed model’s scores). Selection is based on the model’s performance ontest setsand on the fact that Bayesian models offer the advantage of...
[3] Baxter, J. (1997) A Bayesian / Information Theoretic Model of Learning to Learn via Multiple Task Sampling. Machine Learning. 28, 7-39. [4] Duong, L., Cohn. et.al. 2015. Low Resource Dependency Parsing Cross-Lingual Parameter Sharing in a Neural Network Parser. ACL2015. ...
This is a significant benefit compared to other existing Bayesian MTL models that are non-conjugate and rely on approximate inference methods. In comparison with the vast majority of the MTL methods in the literature that are based on the optimization of a regularized cost function, BMSL provides...
the MTGP formulationsand their gradients.1 IntroductionGaussian process (GP) is one of the most important machine learning algorithms in practiceand often plays a key role in Bayesian optimization (BO) (Brochu et al., 2010; Shahriari et al.,2016; Garnett, 2022)1because GP shows good ...
In general, it is understood that feature selection can be achieved by making the model matrix W sparse, for example in the context of a (relaxed) convex optimization framework or a Bayesian framework. An advantage of the Bayesian approach is that it enables the degree of sparsity to be ...