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Later, GLaM [36] demonstrated a language model with 1.2 trillion parameters, each MoE layer using top-2 out of 64 experts. Switch Transformers [21] use top-1 in all MoE layers. The NLLB-200 by Meta AI is a machine translation model for 200 languages. [37] Each MoE layer uses a hierarchical MoE with two levels.
Simpler modules like Linear, Tanh and Max make up the basic component modules. This modular interface provides first-order automatic gradient differentiation. What follows is an example use-case for building a multilayer perceptron using Modules: >
Y is the 1-hot maximizer of the linear Decoder layer D; that is, it takes the argmax of D's linear layer output. x 300-long word embedding vector. The vectors are usually pre-calculated from other projects such as GloVe or Word2Vec. h 500-long encoder hidden vector. At each point in time, this vector summarizes all the preceding words before it.
On the bottom is the same architecture but with the last "projection" layer replaced by another one that projects to fewer outputs. If one freezes the rest of the model and only finetune the last layer, one can obtain another vision model at cost much less than training one from scratch. AlexNet block diagram
The un-embedding layer is a linear-softmax layer: = (+) The matrix has shape (,). The embedding matrix M {\displaystyle M} and the un-embedding matrix W {\displaystyle W} are sometimes required to be transposes of each other, a practice called weight tying.
The simplest kind of feedforward neural network (FNN) is a linear network, which consists of a single layer of output nodes with linear activation functions; the inputs are fed directly to the outputs via a series of weights. The sum of the products of the weights and the inputs is calculated at each node.
Operating on byte-sized tokens, transformers scale poorly as every token must "attend" to every other token leading to O(n 2) scaling laws, as a result, Transformers opt to use subword tokenization to reduce the number of tokens in text, however, this leads to very large vocabulary tables and word embeddings.
An echo state network (ESN) [1] [2] is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer (with typically 1% connectivity). The connectivity and weights of hidden neurons are fixed and randomly assigned. The weights of output neurons can be learned so that the network can produce or ...