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An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. [1] It is part of the families of probabilistic graphical models and variational Bayesian methods .
Neural coding (or neural representation) is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the neuronal responses, and the relationship among the electrical activities of the neurons in the ensemble.
Shannon's diagram of a general communications system, showing the process by which a message sent becomes the message received (possibly corrupted by noise). seq2seq is an approach to machine translation (or more generally, sequence transduction) with roots in information theory, where communication is understood as an encode-transmit-decode process, and machine translation can be studied as a ...
Like the Masked Autoencoder, the DINO (self-distillation with no labels) method is a way to train a ViT by self-supervision. [26] DINO is a form of teacher-student self-distillation. In DINO, the student is the model itself, and the teacher is an exponential average of the student's past states.
In order to give the definition for something that is PAC-learnable, we first have to introduce some terminology. [2] For the following definitions, two examples will be used. The first is the problem of character recognition given an array of bits encoding a binary-valued image. The other example is the problem of finding an interval that will ...
Variational autoencoder GAN (VAEGAN): [30] Uses a variational autoencoder (VAE) for the generator. Transformer GAN (TransGAN): [ 31 ] Uses the pure transformer architecture for both the generator and discriminator, entirely devoid of convolution-deconvolution layers.
The model generates sounds through a neural network based synthesis, employing a WaveNet-style autoencoder to learn its own temporal embeddings from four different sounds. [2] [3] Google then released an open source hardware interface for the algorithm called NSynth Super, [4] used by notable musicians such as Grimes and YACHT to generate experimental music using artificial intelligence.