Learn how to implement the K-Nearest Neighbors (KNN) algorithm from scratch in Python! This tutorial covers the theory, coding process, and practical examples to help you understand how KNN works ...

Abstract: Approximate K Nearest Neighbor (AKNN) search in high-dimensional spaces is a critical yet challenging problem. In AKNN search, distance computation is the core task that dominates the ...

Embedding-based search outperforms traditional keyword-based methods across various domains by capturing semantic similarity using dense vector representations and approximate nearest neighbor (ANN) ...

KEYWORDS: Vehicle Routing Problem (VRP), Google Maps Distance Matrix API, Python, Pulp, Mixed Integer Linear Programming, Transportation, Optimisation Problem, Time Window, Mathematical Modelling ...

A sophisticated cyber-espionage attack used by notorious Russian advanced persistent threat (APT) Fancy Bear at the outset of the current Russia-Ukraine war demonstrates a novel attack vector that a ...

Graph-based methods have become increasingly important in data retrieval and machine learning, particularly in nearest neighbor (NN) search. NN search helps identify data points closest to a given ...

ABSTRACT: Using resting-state functional magnetic resonance imaging (fMRI) technology to assist in identifying brain diseases has great potential. In the identification of brain diseases, graph-based ...

PyTorch + HuggingFace code for RetoMaton: "Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval" (ICML 2022), including an implementation of kNN-LM and kNN-MT ...