CVPR2022 (Oral) - Rethinking Semantic Segmentation: A Prototype View
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Updated
Jun 30, 2022 - Python
CVPR2022 (Oral) - Rethinking Semantic Segmentation: A Prototype View
[ICMLSC 2018] On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset
C++/Python implementation of Nearest Neighbor Descent for efficient approximate nearest neighbor search
Built prediction and retrieval models for document retrieval, image retrieval, house price prediction, song recommendation, and analyzed sentiments using machine learning algorithms in Python
Use Kmean Clustering and Collaborative filtering approach for the recommendation problem
An MPI based implementation of K-NN search algorithm, aimed for use on CPU clusters.
MATLAB code for bonferroni-means fuzzy k nearest neighbor classifier (BM-FKNN)
This is the model that I build to grade future essay based on the that is provided.
1-Bag of SIFT representation and nearest neighbor classifier
How can I trust you? An intuition and tutorial on trust score
An efficient Nearest Neighbor Classifier for the MINST dataset. It uses a VP Tree data structure for preprocessing, thus improving query time complexity
Programming Assignment on Data Mining: Movies Review Classification
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms [NeurIPS 2020]
This is a course project to select a subset of data to build an efficient nearest neighbor classifier. Choosing a representative subset of "prototypes" from the training set is crucial for accelerating nearest neighbor classifiers. This project proposes projecting the data into a latent space using a pretrained embedder.
These are computer assignments of the book "An Introduction to Machine Learning" by Miroslav Kubat
Feature wise normalization: An effective way of normalizing data
As part of the UCSanDiego online course "Machine Learning Fundamentals"
Code for the PR lab course DTU
AI to predict whether online shopping customers will complete a purchase
Here I am starting with Machine Learning notes after SQL notes. I have covered the following topics such as:
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