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15 search results found
Mabwiser
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160
[IJAIT 2021] MABWiser: Contextual Multi-Armed Bandits Library
Parametricumap_paper
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112
Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).
Py Glm
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86
Generalized Linear Models in Sklearn Style
Famos
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77
Adversarial Framework for (non-) Parametric Image Stylisation Mosaics
Machine Learning
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67
Programming Assignments and Lectures for Andrew Ng's "Machine Learning" Coursera course
Feather
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39
The reference implementation of FEATHER from the CIKM '20 paper "Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models".
Semiparametric
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37
[TPAMI 2020] Generating Novel Views of Vehicles via Semi-parametric Guidance. A semi-parametric approach for synthesizing novel views of a rigid object from a single monocular image.
Stk
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34
The STK is a (not so) Small Toolbox for Kriging. Its primary focus is on the interpolation/regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior.
Pycalib
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9
Non-Parametric Calibration for Classification (AISTATS 2020)
Tomouh Introduction To Machine Learning Ml101
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8
This Course is organized by Tomouh Voluntary Team at Damascus University.
Coursera_machine_learning
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7
About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this clas
Parameterisedmodule.jl
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7
Full featured parameterised ML-modules in Julia
Coursera Ml
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7
💡This repository contains all of the lecture exercises of Machine Learning course by Andrew Ng, Stanford University @ Coursera. All are implemented by myself and in MATLAB/Octave.
Bnpseg
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6
Bayesian Non-Parametric Image Segmentation using HDP-MRF
Oda
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5
ODA is a non-parametric machine-learning algorithm that maximizes classification accuracy. This package provides an R-based interface for the MegaODA software suite.
Awesome Machine Learning
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5
Learning tutorial for machine learning beginners
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