7 skills found
THU-DA-6D-Pose-Group / GDR NetGDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation. (CVPR 2021)
mhuzaifadev / Machine Learning Zero To HeroWelcome to Machine Learning: Zero to Hero: From the fundamentals of machine learning to advanced techniques like regressions, classification, clustering, Neural Networks, OpenCV, Recommendation Engines and more, this Python-based repository provides a comprehensive guide for mastering ML.
kanchen-usc / QRC NetImplementation of Query-guided Regression Network with Context Policy for Phrase Grounding in Tensorflow
RojunLin / R 3CNNRegression Guided by Relative Ranking Using Convolutional Neural Network (R^3 CNN) for Facial Beauty Prediction
SaniyaKhullar / NetREmNetwork Regression Embeddings reveal cell-type Transcription Factor coordination for target gene (TG) regulation
Tandoan19 / SONNETSONNET: A self-guided ordinal regression neural network for segmentation and classification of nuclei in large-scale multi-tissue histology images
lordflavio / PEMF Time SeriesPredictive Estimation of Model Fidelity (PEMF) is a model-independent approach to measure the fidelity of surrogate models or metamodels, such as Kriging, Radial Basis Functions (RBF), Support Vector Regression (SVR), and Neural Networks. It can be perceived as a novel sequential and predictive implementation of K-fold cross-validation. PEMF takes as input a model trainer (e.g., RBF-multiquadric or Kriging-Linear), sample data on which to train the model, and hyper-parameter values (e.g., shape factor in RBF) to apply to the model. As output, it provides a predicted estimate of the median and/or the maximum error in the surrogate model. PEMF has been reported to be more accurate and robust than typical leave-one-out cross-validation, in providing surrogate model error measures (for various benchmark functions). The current version of PEMF has been implemented with RBF (included in this package), Kriging (DACE package), and SVR (Libsvm package), PEMF (has been and) can be readily used for the following purposes: 1. Surrogate model validation 2. Surrogate model uncertainty analysis 3. Surrogate model selection 4. Surrogate-based optimization (to guide sequential sampling) Other perceived broader applications of PEMF include testing of machine learning models and uncertainty analysis with data-driven models (and other areas where leave-one-out or k-fold cross-validation is typically used).
