Search Results for: "neural network"

Google Introduces Families of Neural Networks To Train Faster, SOTA Performance

Google Introduces Families of Neural Networks To Train Faster, SOTA Performance

In the future, we plan to optimise these models further and apply them to new tasks, such as zero-shot learning and self-supervised learning.

Why Are Apple’s Researchers Interested In Neural Network Subspaces?

Neural network subspaces contain diverse solutions that can be ensembled, approaching the ensemble performance of independently trained networks without the training cost.

A Complete Understanding of Dense Layers in Neural Networks

dense layer is deeply connected layer from its preceding layer which works for changing the dimension of the output by performing matrix vector multiplication

What is the Plateau Problem in Neural Networks and How to Fix it?

This post explains most underlined
Plateau phenomenon and it’s remedies
which is related to optimization of ML model.

A Beginner’s Guide to Neural Network Pruning

Neural network pruning, which comprises methodically eliminating parameters from an existing network, is a popular approach for minimizing the resource requirements at test time.

Google Maps

How Can Graph Neural Networks Help Google Maps Make Better ETA Predictions

A paper titled “ETA Prediction with Graph Neural Networks in Google Maps” presents a graph neural network estimator for an estimated time of travel (ETA).

Should Neural Networks Compose Music By The Same Logic & Process As Humans Do?

Not Quite My Tempo: Why Should We Care About AI Generated Music?

What A Neural Network Really Looks Like

A deep neural network is created by sandwiching “hidden” layers between the input and the output.

LSTM Vs GRU in Recurrent Neural Network: A Comparative Study

Long Short Term Memory in short LSTM is a special kind of RNN capable of learning long term sequences. They were introduced by Schmidhuber and Hochreiter in 1997. It is explicitly designed to avoid long term dependency problems. Remembering the long sequences for a long period of time is its way of working. 

DDSP

Hands-On Guide To Differential Digital Signal Processing Using Neural Networks

Digital Signal Processors take waveforms in voice, audio, video, temperature, and then mathematically manipulate them. The key idea of a DSP is to create complex, realistic signals by precisely controlling and tuning their many parameters.

How To Confuse a Neural Network Using Fast Gradient Sign Method?

Many machine learning models, including neural networks, consistently misclassify the adversarial examples. Adversarial examples are nothing but specialised inputs created to confuse neural networks, ultimately resulting in misclassification of the result. These notorious inputs are almost the same as the original image to human eyes but cause a neural network to fail to identify the image’s content.

Applying Neural Network Model To The Problem Of Cell Size Control

Cell growth and division are two significant areas of research in the field of cell…