Question

How to get more performance out of automatic differentiation?

score 641 · Answer 1 · 2025-01-01 18:47:53

641

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How to get more performance out of automatic differentiation?

Published on 01 January 2025 at 18:47

score 197 · Answer 2 · 2025-01-02 03:01:59

197

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minimal Numeric.AD example won't compile

Published on 02 January 2025 at 03:01

score 94 · Answer 3 · 2025-01-07 16:11:26

94

Views

If i can compute the gradient and Hessian, will Newtons method significantly outperform BFGS/L-BFGS?

Published on 07 January 2025 at 16:11

score 3.8k · Answer 4 · 2025-01-02 03:05:44

3.8k

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Automatic differentiation library in Scheme / Common Lisp / Clojure

Published on 02 January 2025 at 03:05

score 462 · Answer 5 · 2025-01-07 13:46:52

462

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how to use promote rule in julia?

Published on 07 January 2025 at 13:46

score 209 · Answer 6 · 2025-01-02 03:04:34

209

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Is sympy solve compatible with tensorflow GradientTape?

Published on 02 January 2025 at 03:04

score 149 · Answer 7 · 2025-01-02 03:05:43

149

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How does TensorFlow compute the gradient of vgg19.preprocess_input?

Published on 02 January 2025 at 03:05

score 941 · Answer 8 · 2025-01-02 03:07:19

941

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What is the correct way to use a PyTorch Module inside a PyTorch Function?

Published on 02 January 2025 at 03:07

score 74 · Answer 9 · 2025-01-02 03:09:33

74

Views

How to compute in tensorflow the partial derivative of each observation from the output w.r.t each input observation

Published on 02 January 2025 at 03:09

score 84 · Answer 10 · 2025-01-02 03:09:13

84

Views

What is the correct way of defining a differentiable function with scalar arguments that returns a vector/array?

Published on 02 January 2025 at 03:09

score 17 · Answer 11 · 2025-01-02 03:07:31

17

Views

Back propagation in pytorch to calculate component wise derivatives of vector

Published on 02 January 2025 at 03:07

score 62 · Answer 12 · 2025-01-02 03:05:02

62

Views

Accurately finding the gradient of values from 2 vectors

Published on 02 January 2025 at 03:05

score 41 · Answer 13 · 2025-01-02 03:08:58

41

Views

How can I get exhaust list of functions available to grad_fn for backward() function in Pytorch?

Published on 02 January 2025 at 03:08

score 88 · Answer 14 · 2025-01-02 03:06:50

88

Views

How can I fix this problem of automated differentiation with Tensorflow?

Published on 02 January 2025 at 03:06

score 218 · Answer 15 · 2025-01-02 03:08:19

218

Views

Tensorflow loses track of variables/gradients after multiplication with constant tensor

Published on 02 January 2025 at 03:08

score 499 · Answer 16 · 2025-01-02 03:08:57

499

Views

Is there a way to define the gradient of a 3D FFT using tensorflow's custom_gradient decorator

Published on 02 January 2025 at 03:08

score 907 · Answer 17 · 2025-01-02 03:03:34

907

Views

Gradient of neural network with respect to inputs

Published on 02 January 2025 at 03:03

score 1.6k · Answer 18 · 2025-01-02 03:07:47

1.6k

Views

How does tensorflow handle non differentiable nodes during gradient calculation?

Published on 02 January 2025 at 03:07

score 1k · Answer 19 · 2025-01-02 03:08:11

1k

Views

Why Pytorch autograd need another vector to backward instead of computing Jacobian?

Published on 02 January 2025 at 03:08

score 195 · Answer 20 · 2025-01-02 03:09:12

195

Views

No gradients provided for any variable - Custom loss function with random weights depending on the Softmax output

Published on 02 January 2025 at 03:09

List Question

How to get more performance out of automatic differentiation?

minimal Numeric.AD example won't compile

If i can compute the gradient and Hessian, will Newtons method significantly outperform BFGS/L-BFGS?

Automatic differentiation library in Scheme / Common Lisp / Clojure

how to use promote rule in julia?

Is sympy solve compatible with tensorflow GradientTape?

How does TensorFlow compute the gradient of vgg19.preprocess_input?

What is the correct way to use a PyTorch Module inside a PyTorch Function?

How to compute in tensorflow the partial derivative of each observation from the output w.r.t each input observation

What is the correct way of defining a differentiable function with scalar arguments that returns a vector/array?

Back propagation in pytorch to calculate component wise derivatives of vector

Accurately finding the gradient of values from 2 vectors

How can I get exhaust list of functions available to grad_fn for backward() function in Pytorch?

How can I fix this problem of automated differentiation with Tensorflow?

Tensorflow loses track of variables/gradients after multiplication with constant tensor

Is there a way to define the gradient of a 3D FFT using tensorflow's custom_gradient decorator

Gradient of neural network with respect to inputs

How does tensorflow handle non differentiable nodes during gradient calculation?

Why Pytorch autograd need another vector to backward instead of computing Jacobian?

No gradients provided for any variable - Custom loss function with random weights depending on the Softmax output

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