The method can identify more accurate positive instances from unlabeled data to enlarge the labeled data. First, unlabeled samples are categorized using the Pairwise model. Then, the shortest dependency path with additional information is generated. Furthermore, two input forms with a new ...
sample_data Array of strings List of sample data sample_dir String Path for storing a sample sample_id String Sample ID sample_name String Sample name sample_size Long Sample size or text length, in bytes sample_status String Sample status. Options: __ALL__: labeled __NONE__: unlabeled ...
Meanwhile, in semi-supervised learning, which combines labeled and unlabeled data, the sample complexity is often influenced by the ratio of labeled to unlabeled data. Examples of Real-World Applications of Sample Complexity Management Consider a company like Netflix, which uses machine learning to ...
本文从理论和实践两名面重新研究了监督Label的价值,提出了应用未标注数据(unlabeled data)进行半监督学习(semi-supervised)以及无需额外数据,进行自监督学习(self-supervised)预训练,其训练结果参数作为常规监督学习网络初始化参数,在large-scale imbalanced 弱监督的语义分割论文汇总(持续更新ing)...
Its main objective is to learn hidden structures from unlabeled data by attempting to reproduce the input given to it through its hidden layer representations [36, 37] (Additional file 1: Fig. S1). It consists of simulated layers of neurons, where each neu- ron is associated with a ...
In this case, measurements in the statistical learning theory fail to explain why deep neural networks generalize well from the training set to new data. For example, Rademacher complexity, which is a measure of the capacity to fit random noises, will be close to 1, resulting in loose and ...
Unlabeled and LMO-labeled cells have similar neighborhoods while CMO-labeled cells preferentially co-localize in gene expression space. n = 7,888 HEK cells. (e) Schematic overview of a proof-of-concept snRNA-seq experiment using MULTI-seq. Nuclei were isolated from 10 distinct cell samples (...
(Derivation of DEE) In the case when along with training data, unlabeled data are available (x without y), one can compute two covariance matrices: one from unlabeled data \({\widetilde{C}}\) and another from the training data \({\widehat{C}}\). There is a unique matrix P (Horn ...
4, a scatter plot was created using matplotlib, where each point represents a data point. Its location in the plot is determined by the result of t-SNE dimensionality reduction, and the color is determined by its corresponding label. From this scatter plot, we can observe the arrangement of...
Some methods do not suffer from this drawback but instead require additional splitting of the data (Cotter et al. 2018, 2019a), hence possibly penalizing utility, as the amount of data used to update the model is reduced. Finally, other approaches have limited applicability, as they are ...