Convolution

The fundamental building block of CNNsContinue reading on Towards Data Science » The fundamental building block of CNNsContinue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all materials to their authors. If you are the owner of the content and do not want us to…

Improve Article Save Article Like Article Improve Article Save Article Convolution is a mathematical operation used in signal processing, image processing, and other fields to combine two functions in order to produce a third function. It is defined as the integral of the product of two functions, one of which is flipped and shifted over time. It is often represented using the symbol “*” and is useful for filtering, smoothing, and other operations on signals and images.Fast Convolution:Fast convolution for 64-bit…

Dirichlet convolution is a mathematical operation that combines two arithmetic functions to create a third function. It is named after the mathematician Peter Gustav Lejeune Dirichlet and is closely related to the concept of convolution in signal processing.In mathematics, an arithmetic function is a function that takes positive integers as input and returns a number as output. The most well-known example of an arithmetic function is the divisor function, which counts the number of positive divisors a given number has.The…

Understand MolKGNN, an interpretable GNN tailored for drug discoveryFigure 1. An analogy of (A) image convolution and (B) proposed molecular convolution. Image from the original paper.This blog introduces our latest model Molecular Kernel Graph Neural Network (MolKGNN) from the paperInterpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug DiscoveryElucidating molecular structures and their pharmacological activity has been a long-standing problem in the history…

A deep dive into the basic cell of many neural networks.Figure 0: Sparks from the flame, similar to the extracted features using convolution (Image by Author)In this era of deep learning, where we have advanced computer vision models like YOLO, Mask RCNN, or U-Net to name a few, the foundational cell behind all of them is the Convolutional Neural Network (CNN)or to be more precise convolution operation. These networks try to solve the problem of object detection, segmentation, and live inference which leads to many…