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GRS's Projects

smilecnn icon smilecnn

Smile detection with a deep convolutional neural net, with Keras.

smir icon smir

Semi-supervised classifier with squared-loss mutual information regularization

smoke-plot icon smoke-plot

Function for visualizing posterior uncertainty in R

smop icon smop

Small Matlab to Python compiler (Please open an issue if you are using this program)

smos-box icon smos-box

A SNAP Toolbox dedicated to the SMOS sensor

smote icon smote

Synthetic Minority Over-sampling Technique

smoteboost icon smoteboost

Explored synthetic minority oversampling procedure (SMOTE) and AdaBoost for image classification.

smotefamily icon smotefamily

:exclamation: This is a read-only mirror of the CRAN R package repository. smotefamily — A Collection of Oversampling Techniques for Class Imbalance Problem Based on SMOTE

snake icon snake

old snake library - to be transferred to personal account

snap-examples icon snap-examples

SNAP example projects showing how to extend SNAP or build new applications on SNAP

snapshotensemble icon snapshotensemble

Snapshot Ensembles in Torch (Snapshot Ensembles: Train 1, Get M for Free)

snark icon snark

generic c++ libraries and utilities for robotics

snlp_group14 icon snlp_group14

Semisupervised label propagation approach for identifying derivational nouns in english

soar icon soar

Soar, a general cognitive architecture for systems that exhibit intelligent behavior.

soft-computing-lab icon soft-computing-lab

Lab experiments on Nueral Networks for Undergraduate ECE Students , NIT Rourkela

soft-decision-tree icon soft-decision-tree

pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"

soft_computing_2014_lsea_ea icon soft_computing_2014_lsea_ea

Codebase for Soft Computing UKCI special issue paper "Using an adaptive collection of local evolutionary algorithms for multi-modal problems" (provisionally accepted) containing LSEA_EA algorithm

softx-d-17-00002 icon softx-d-17-00002

Feature extraction, selection and classification code for power line scene recognition. To cite this software publication: https://www.sciencedirect.com/science/article/pii/S2352711017300560

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