Gated linear networks

#papers

This post if from a series of notes written for personal usage while reading random ML/SWE/CS papers. The notes weren’t originally intended for the eyes of other people and therefore might be incomprehensible and/or flat out wrong.

Paper in question: 1910.01526

  • Series of linear filters (weights) on input with non-linearity at the end
    • Non-linearities are on each layer (neuron) but they cancel each other out
  • Set of weights per each neuron
    • Specific weight vector selected via context func. from input (side information)
    • Each neuron different set of weights, different context function
    • Same side information for all neurons in all layers
    • Weights adjusted during training, only the one weight vector for current input, online gradient descent
  • Context function:
    • Usually set of half-space functions (similarity with side inf)
    • Don’t change during training, need to be sampled correctly
    • Similar data will (through context func.) force same weights for neurons -> sim. outputs
    • Unsimilar data won’t use the same weights -> less forgetting
  • Each neuron is geometric mixture of outputs of previous layer (through weights)
    • Weights initialized randomly, updated via training
  • Essentially a multilevel mixture of KNN and linear transformation with point non-lin.
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