Questions tagged [gradient]

For questions related to the gradient, a way of packing together all the partial derivative information of a function

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Why is it a problem if the outputs of an activation function are not zero-centered?

In this lecture, the professor says that one problem with the sigmoid function is that its outputs aren't zero-centered. Are the explanation provided by the professor regarding why this is bad is that ...
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2answers
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Why is tf.abs non-differentiable in Tensorflow?

I understand why tf.abs is non-differentiable in principle (discontinuity at 0) but the same applies to tf.nn.relu yet, in case of this function gradient is simply set to 0 at 0. Why the same logic is ...
2
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1answer
56 views

What is $ \nabla_{\theta_{k-1}} \theta_{k}$ in the context of MAML?

I am attempting to fully understand the explicit derivation and computation of the Hessian and how it is used in MAML. I came across this blog: https://lilianweng.github.io/lil-log/2018/11/30/meta-...
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0answers
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During Backpropagation in LSTM, why is the previous output $h_{t-1}$ considered constant w.r.t any $W$ while computing derivative?

I've just started learning LSTM, and some points in the process of calculating the gradients are getting me confused. Say, for example, we want to compute $\frac{\partial}{\partial W_i}L$, where $L$ ...
4
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1answer
1k views

How is the gradient of the loss function in DQN derived?

In the original DQN paper, page 1, the loss function of the DQN is $$ L_{i}(\theta_{i}) = \mathbb{E}_{(s,a,r,s') \sim U(D)} [(r+\gamma \max_{a'} Q(s',a',\theta_{i}^{-}) - Q(s,a;\theta_{i}))^2] $$ ...
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1answer
137 views

How can the sum of squared errors have negative gradient if it's defined as the squared of the error?

The formula for the sum of squared errors (SSE) is: $$ \frac{1}{2} \sum_{i=1}^n (t^i - o^i)^2 $$ I have a few related questions. If $t^i - o^i$ is negative, doesn't the power of 2 eliminate any ...