# Tag Info

Accepted

### Backprop Through Max-Pooling Layers?

There is no gradient with respect to non maximum values, since changing them slightly does not affect the output. Further the max is locally linear with slope 1, with respect to the input that ...
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Accepted

• 27.4k
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### What is the dimensionality of the bias term in neural networks?

As per the general case, the bias vector must have the same dimensions as the output vector. Please, have a look at this excellent presentation: In this example by M.Görner, there are 10 classes, so ...
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### Sliding window leads to overfitting in LSTM?

LSTMs do not require a sliding window of inputs. They can remember what they have seen in the past, and if you feed in training examples one at a time they will choose the right size window of inputs ...
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### Differences between gradient calculated by different reduction methods in PyTorch

Let's start by just recalling what each of these means. Reduction 'none' means compute batch_size gradient updates independently for the loss with respect to each ...
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### Backprop Through Max-Pooling Layers?

@Shinvu's answer is well written, I would like to point to a video that explains the gradient of Max() operation and this within a computational graph which is quick to grasp.! while implementing the ...
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Accepted

### Back-propagation through max pooling layers

If this is correct then every "neuron" of the pooling layer has the same gradient? No. It depends on the weights and activation function. And most typically the weights are different from the first ...
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