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Results tagged with gradient-descent
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user 42699
For questions surrounding gradient descent, a method for finding the optimum state of a parameterized function based on another function often called the loss or error function. It iteratively descends the loss surface to the minimum loss by adjusting parameters based on the product of the partial derivatives comprising the gradient and a learning rate.
1
vote
Is there anything that ensures that convolutional filters end up different from one another?
Yes, your thought experiment is correct, and the concept is known as breaking the symmetry. This is why biases can be initialized to $0$ (bias initialization doesn't matter), but weights should be ran …
- 1,690
4
votes
1
answer
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What is the gradient of an attention unit?
The paper Attention Is All You Need describes the Transformer architecture, which describes attention as a function of the queries $Q = x W^Q$, keys $K = x W^K$, and values $V = x W^V$:
$\text{Attenti …
- 1,690