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Sensitivity

Papers

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Showing 1776–1800 of 2016 papers

TitleStatusHype
Deep Video Quality Assessor: From Spatio-temporal Visual Sensitivity to A Convolutional Neural Aggregation Network—0
A Supervised Learning Approach For Heading Detection—0
A hybrid cellular automaton model of cartilage regeneration capturing the interactions between cellular dynamics and scaffold porosity—0
Snapshot multispectral imaging using a filter array—0
Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer—0
Finding morphology points of electrocardiographic signal waves using wavelet analysis—0
Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation—0
Quantifying the Influences on Probabilistic Wind Power Forecasts—0
Murmur Detection Using Parallel Recurrent & Convolutional Neural Networks—0
Robust Implicit Backpropagation—0
Active Learning based on Data Uncertainty and Model Sensitivity—0
A Study of Deep Feature Fusion based Methods for Classifying Multi-lead ECG—0
Sensitivity to Input Order: Evaluation of an Incremental and Memory-Limited Bayesian Cross-Situational Word Learning Model—0
IGLOSS: iterative gapless local similarity search—0
False Positive Reduction by Actively Mining Negative Samples for Pulmonary Nodule Detection in Chest Radiographs—0
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient MatchingCode0
Entrainment of Lymphatic Contraction to Oscillatory Flow—0
Sensitivity to initial conditions in an extended activator-inhibitor model for the formation of patterns—0
Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma—0
Synthetic Sampling for Multi-Class Malignancy Prediction—0
Minimizing Sensitivity to Model Misspecification—0
Gated Path Planning NetworksCode1
Assessing the Accuracy of a Wrist Motion Tracking Method for Counting Bites across Demographic and Food Variables—0
A Note about: Local Explanation Methods for Deep Neural Networks lack Sensitivity to Parameter Values—0
Correspondence of Deep Neural Networks and the Brain for Visual TexturesCode0
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