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Sep 25, 2023
Data Imputation using Reverse ML

Abstract Imputation fixes broken data. Methods are from making it constant mean to clustering, regression and generative networks. What if...

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Sep 8, 2023
Researching Visual Question Answering: Bridging the Gap between Humans and AI

Introduction The goal of this project is to offer regular users and developers the chance to engage in practical visual...

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Jul 16, 2019
Differentiable Programming – Inverse Graphics AutoEncoder

Intro to Differentiable Programming DeepLearning classifier, LSTM, YOLO detector, Variational AutoEncoder, GAN – are these guys truly architectures in sense...

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Jun 14, 2019
Point Cloud Data: Simple Approach

Introduction to Point Cloud Data  In recent years, there was great progress in the development of LIDAR detectors that resulted...

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Nov 12, 2018
Active Learning on MNIST – Saving on Labeling

Active Learning is a semi-supervised technique that allows labeling less data by selecting the most important samples from the learning process (loss) standpoint. It can have a huge impact on the project cost in the case when the amount of data is large and the labeling rate is high. For example, object detection and NLP-NER problems.The article is based on the following code: Active Learning on MNIST (more…)

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May 16, 2018
Vectorization of Raster to Polygons

Vectorization of Raster  We would never start writing any vectorization code if there was any free library. However, recently Andy...

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Oct 16, 2017
The Natural Ear for Digital Sound Processing – as an alternative to the Fourier Transform

Fast Fourier Transform (FFT) in Digital Sound Processing This is a primitive prototype of the natural ear. Why I came...

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