Unlike the average shape, the variability around the psychometric curve is affected by the decision time (Suppl. Fig. 3). When normalized, the variability reduces strongly with decision time (Fig. 4F). Moreover, we also show that a “simpler” input has a lower variability than a “more ...
2020-05-07 Vid2Curve: Simultaneous Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video Abstract | PDF 2020-05-07 Distributional Robustness of K-class Estimators and the PULSE Abstract | PDF 2020-05-06 Functional Decision Theory in an Evolutionary Environment Abstract | ...
Fig. 16 shows the sharp peaks in the predicted ϑ versus depth curve at the anhydrate-salt interfaces. Fig. 17 reveals that some of these peaks are also associated with intervals where σH reaches maximum values for the wellbore as a whole. Download: Download high-res image (616KB) ...
We computed a learning curve for each squirrel using proportional logistic regression, in which the proportion of paw attempts (out of ten) was modelled as a function of the trial block. We then used the fitted model to predict performance (i.e. proportion of paw attempts) in block 25, on...
range of SNRs and types of CA concentration time curves (denoted “pathology experts”, “healthy experts”, “vessel experts”) to generate a reconstruction hypothesis from noisy input by using a classification DNN to select the most likely hypothesis and provide a “clean output” curve. Trainin...
It is challenging to quantify psychological factors (i.e., the potential and efficiency of learning) using objective measures (i.e. accuracy and RTs). However, through mathematical modeling we were able to investigate the change of SL over time using the estimated learning curve in the exponenti...
Table 1 Multigroup growth curve model results for school engagement, with initial levels of stereotype awareness (intercept) predicting initial levels of engagement (intercept) for RQ3, initial levels of stereotype awareness (intercept) predicting changes (slope) in engagement for RQ4, and changes (...
description={deep learning}, sort={deep learning}, } \newglossaryentry{knowledge_base} { name=知识库, description={knowledge base}, sort={knowledge base}, } \newglossaryentry{ML} { name=机器学习, description={machine learning}, sort={machine learning}, } \newglossaryentry{ML_model} { name...
Problems involving the prediction of curves such as in viscosity-pressure curve in PVT data require extensive exploration of algebraic equations in addition to the understanding of the physics of the problem. Challenges remain in AI tools, too, in overfitting, coincidence effect, overtraining, etc. ...
Therefore, in the sense of synaptic modulation precision, it is reasonable to use the continuous fitting curve to modulate the synaptic weights (Fig. 2 & Supplementary Fig. S1b) to support the simulation in this work38,58. Application for spatiotemporal pattern learning. Spatiotemporal ...