Optimized CNN and CRNN for Advanced Classification and Regression This repository contains the implementation for my third major project, "Optimized CNN and CRNN for Advanced Classification and ...

The extracted features are mapped to psychoacoustic loudness growth estimates using a multi-target regression model based on a convolutional neural network. We conduct an ablation study to analyze the ...

Imaging mass spectrometry (IMS) is a technique for simultaneously acquiring the expression and distribution of molecules on the surface of a sample, and it plays a crucial role in spatial omics ...

This paper proposes a method for predicting adolescent health risks by combining multi-sequence, two-dimensional convolutional autoencoder (2DCNN-AE) and multi-scale asynchronous correlation ...

Creating and Training the LightGBM Autoencoder Model The LightGBM system does not have a built-in autoencoder class so one must be created using multiple regression modules. The goal of the ...

A new research by Google DeepMind shows how sparse autoencoders (SAEs) with special JumpReLU activation can help interpret LLMs.

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...

Autoencoder for Product Matching This was an experiment for a possible PhD topic. The main idea was to use different Autoencoder for entity resolution / product matching. The core idea was to pretrain ...