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7篇 您的检索式：作者名="M.Singer"

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		题名	作者	年代	出处	被引量
	1	The Sleep Disorders Inventory: an instrument for studies of sleep disturbance in persons with Alzheimer’s disease显示文摘	Rochelle E.Tractenberg Clifford M.Singer Jeffrey L.Cummings Leon J.Thal	2003	Journal of Sleep Research2003，，4：	1
	2	Constructing inferences in expository text comprehension显示文摘	M.Singer D.Harkness S.T.Stewart		0，，：	1
	3	Robust inference processes in the comprehension of expository text显示文摘	M.Singer G.O'Connell		0，，：	1
	4	Constructing inferences during narrative text comprehension显示文摘	A.C.Graesser M.Singer T.Trabasso		0，，：	1
	5	Minimal or globe inference during reading显示文摘	M.Singer A.C.Graesser T.Trabasso		0，，：	1
	6	皂脚连续处理法显示文摘原料油脂用碱液脱酸时生成的皂脚中含有皂、中性油、不皂化物、色素、胶质、松香皂、甘油(由中性油皂化产生)、电解质和水。皂脚中总脂肪物含量(皂和中性油)随脱酸方法而异,通常在15~30%之间。所含中性油以乳液态存在。	M.Singer 胡征宇	1990	日用化学工业译丛1990，，2：	0
	7	New guidance for using t-SNE:Alternative defaults,hyperparameter selection automation,and comparative evaluation显示文摘We present new guidelines for choosing hyperparameters for t-SNE and an evaluation comparing these guidelines to current ones.These guidelines include a proposed empirically optimum guideline derived from a t-SNE hyperparameter grid search over a large collection of data sets.We also introduce a new method to featurize data sets using graph-based metrics called scagnostics;we use these features to train a neural network that predicts optimal t-SNE hyperparameters for the respective data set.This neural network has the potential to simplify the use of t-SNE by removing guesswork about which hyperparameters will produce the best embedding.We evaluate and compare our neural network-derived and empirically optimum hyperparameters to several other t-SNE hyperparameter guidelines from the literature on 68 data sets.The hyperparameters predicted by our neural network yield embeddings with similar accuracy as the best current t-SNE guidelines.Using our empirically optimum hyperparameters is simpler than following previously published guidelines but yields more accurate embeddings,in some cases by a statistically significant margin.We find that the useful ranges for t-SNE hyperparameters are narrower and include smaller values than previously reported in the literature.Importantly,we also quantify the potential for future improvements in this area:using data from a grid search of t-SNE hyperparameters we find that an optimal selection method could improve embedding accuracy by up to two percentage points over the methods examined in this paper.	Robert Gove Lucas Cadalzo Nicholas Leiby Jedediah M.Singer Alexander Zaitzeff	2022	Visual Informatics2022，6，2：	0

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