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Drug synergy prediction remains a challenging task in the treatment of complex diseases such as cancer. Here, the authors develop BAITSAO, a unified model based on Large Language Models for drug synergy prediction and drug discovery tasks in cell line and tumor datasets. ...
以一个图像分类模型为例,我们希望模型具有增量学习新的图像和新的类别的能力,但前者更多地与迁移学习有关,因此任务增量学习(Task-incremental Learning)和难度更高一点的类增量学习(Class-incremental Learning)是深度学习社区当前主要考虑的增量学习范式。本文主要讨论近几年关注度最高的基于微调(Fine-tuning)的类增量...
Students who reflect on their own thinking are more likely to engage in planning how to proceed with a learning task, monitoring their own performance on an ongoing basis, finding solutions to problems encountered, and evaluating themselves upon task completion. These activities may be difficult for...
在过去二十年 CS、AI 领域的研究中,涌现出了众多 distance metric learning 的经典方法,例如 NCA、LMNN、ITML 等,还有后续的一系列 online learning、linear similarity learning、nonlinear metric learning、multi-task metric learning、deep distance metric learning ...
AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics: A tutorial to help machine learning researchers to automatically obtain optimized machine learning models with the optimal learning performance on any specific task. SKBEL: A Python library for Bayesian Evidential Learning (BEL) in order to ...
("glue","rte"),s1="sentence1",s2="sentence2",y="label")#s2 is optional for classification, used to represent text pairs# See AutoTask for shorter codeclasshparams:model_name='tasksource/ModernBERT-base-nli'# better performance for most taskslearning_rate=3e-5# see hf.co/docs/...
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此外,每个模型都会有与之对应的瓶颈性能(peak performance),研究者通过增加训练数据以及调参使之不断逼近瓶颈性能。主动学习核心解决的问题正是如何使用尽可能少的标注数据达到模型的瓶颈性能,从而减少不必要的标注成本。如下图(b)的蓝色虚线所示,主动学习根据合适的策略筛选出最具有价值的样本优先标注并给模型训练,从而...
Notably, we found that the unsupervised group identification accuracy decreased without multi-task learning (Fig. 2b and “Methods”). Similarly, we evaluated the cross-modal prediction performance of UnitedNet through an ablation analysis. The results show that the ablation of either the multi-task...