Questions tagged [derivation]

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i am trying to get the the variance of derivative of order 2. i only have the data of an EEG signal sampled at 128 Hz. is the below code correct

I basically want to implement equation 7 present in the added image ...
Shreedatta Nasik's user avatar
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Gradient function in LogisitcLoss class

I am going through a code for XGBoost from scratch and I am referring to this repository here The log-loss function is given by On differentiating the above function with respect to y_pred (referring ...
Mehul Jain's user avatar
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Problem for a math formula in Weight Uncertainty in Neural Network

I am studying the paper https://arxiv.org/pdf/1505.05424.pdf and there is a formula I don't get page 4: I don't understand how they obtain this formula. Moreover, with chain rule, I get $\frac{\...
Jack21's user avatar
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How to find the derivative of the hidden state of recurrent neural networks?

Recently I am reading the following paper (link) ...
user153245's user avatar
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Are some weight gradients equal?

I want to create a 3 layers neural network from scratch to perform linear regression. The first and the second layer have 2 neurons, and the last layer has one neuron. Feature vector x is divided into ...
Iya Lee's user avatar
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calculating derivative of bias in backpropagation

Looking at the algorithm in wikipedia, we can implement backpropagation by calculating: $$\delta^{L}=\left(f^{L}\right)'\cdot\nabla_{a^{L}}C$$ (where I treat $\left(f^{L}\right)'$ as an $n\times n$ ...
Ariel Yael's user avatar
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How to compute backpropagation gradient according chain rule for using vector/matrix differential?

I have some problems for computing derivative for sum of squares error in backprop neural network. For example, we have a neural network as in picture. For drawing simplicity, i've dropped the sample ...
Grigogiy Reznichenko's user avatar
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Adding a group specific penalty to binary cross-entropy

I want to implement a custom Keras loss function that consists of plain binary cross-entropy plus a penalty that increases the loss for false negatives from one class (each observation can belong to ...
Tim's user avatar
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Loss function for points inside polygon

I am trying to optimize some parameters that used to transform 2d points from a place to another (you may think of that as rotation & translation parameter for simplicity) The parameters are ...
Humam Helfawi's user avatar
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1 answer
131 views

SVM - Making sense of distance derivation

I am studying the math behind SVM. The following question is about a small but important detail during the SVM derivation. The question Why the distance between the hyperplane $w*x+b=0$ and data ...
Alan Yue's user avatar
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Deriving vectorized form of linear regression

We first have the weights of a D dimensional vector $w$ and a D dimensional predictor vector $x$, which are all indexed by $j$. There are $N$ observations, all D dimensional. $t$ is our targets, i.e, ...
user2793618's user avatar
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Why is it valid to remove a constant factor from the derivative of an error function?

I was reading the book 'Make your own neural network' by Tariq Rashid. In his book, he said: (Note - He's talking about normal feed forward neural networks) The $t_k$ is the target value at node $k$, ...
Dhruv Agarwal's user avatar
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How is this score function estimator derived?

In this paper they have this equation, where they use the score function estimator, to estimate the gradient of an expectation. How did they derive this?
adam's user avatar
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Is it valid to use numpy.gradient to find slope of line as well as slope of curve at any point?

what is the difference between slope of the line and slope of the curve? Is it valid to use numpy.gradient to find the slope of the line and slope of the curve at ...
star's user avatar
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2 votes
1 answer
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1st order Taylor Series derivative calculation for autoregressive model

I wrote a blog post where I calculated the Taylor Series of an autoregressive function. It is not strictly the Taylor Series, but some variant (I guess). I'm mostly concerned about whether the ...
targetXING's user avatar
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Derivative of Loss wrt bias term

I read this and have an ambiguity. I try to understand well how to calculate the derivative of Loss w.r.t to bias. In this question, we have this definition: ...
Gonzalo Sanchez cano's user avatar
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1 answer
303 views

Maximum Entropy Policy Gradient Derivation

I am reading through the paper on Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review by Sergey Levine. I am having a difficulty in understanding this part of the ...
Ricky Sanjaya's user avatar
1 vote
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back propagation through time derivation issue

I read several posts about BPTT for RNN, but I am actually a bit confused about one step in the derivation. Given $$h_t=f(b+Wh_{t-1}+Ux_t)$$ when we compute $\frac{\partial h_t}{\partial W}$, does ...
username123's user avatar
2 votes
1 answer
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Doubt in Derivation of Backpropagation

I was going through the derivation of backpropagation algorithm provided in this document (adding just for reference). I have doubt at one specific point in this derivation. The derivation goes as ...
ATK's user avatar
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1 vote
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A Derivation in Combinatory Categorial Grammer

I am reading about CCG on page 23 of Speech and Language processing. There is a derivation as follows: (VP/PP)/NP , VP\((VP/PP)/NP) => VP? Can anyone example ...
chikitin's user avatar
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