Neural Network

What is a Markov Chains? Markov chains model systems that change condition in phases. Probabilities can define these systems' overall behaviour, but their details are...

What is Hierarchical composition? A concept in machine learning and cognitive science known as "hierarchical composition" divides complicated information or ideas into groups of smaller,...

What is sparse decomposition? A signal or data sample can be represented as a linear combination of a few basis elements (also known as atoms)...

What is Sparse Auto-Encoders? A sparse autoencoder is a kind of autoencoder that promotes sparsity in the hidden layer activations in order to learn effective...

What is Deconvolutional network? A deconvolutional network, also known as a transposed convolutional network, is a kind of neural network that upsamples feature maps and...

In directed probabilistic models, the Stochastic Gradient Variational Bayes (SGVB) algorithm is a technique for effective inference and learning, especially when working with continuous...

What are Deep Convolutional Inverse Graphics Network (DC-IGN)? A model called the Deep Convolutional Inverse Graphics Network (DC-IGN) is made to learn interpretable representations of...

What is Deep Generative Models? Neural networks known as "deep generative models" are able to create fresh, realistic samples by learning to mimic the underlying...

What is Deep Boltzmann Machines? Multiple layers of hidden units make up a generative stochastic neural network known as a Deep Boltzmann Machine (DBM). It...

What is Spatial Transformer Networks? One kind of neural network module called a Spatial Transformer Network (STN) enables a model to differentiate between distinct spatial...