Question

Optimizing an autograd + list comprehension python operation

score 24 · Answer 1 · 2024-03-27T07:59:32.047000

24

Views

Optimizing an autograd + list comprehension python operation

Published on 27 March 2024 at 07:59

score 32 · Answer 2 · 2024-03-18T20:11:24.003000

32

Views

How to generate jacobian of a tensor-valued function using torch.autograd?

Published on 18 March 2024 at 20:11

score 70 · Answer 3 · 2024-03-01T12:29:17.477000

70

Views

How to implement Carleman Matrix in Haskell?

Published on 01 March 2024 at 12:29

score 79 · Answer 4 · 2024-02-25T16:53:34.607000

79

Views

Calculating two gradients in pytorch and reusing an intermediate gradient

Published on 25 February 2024 at 16:53

score 57 · Answer 5 · 2024-02-24T16:16:51.900000

57

Views

solving an ODE using neural networks

Published on 24 February 2024 at 16:16

score 24 · Answer 6 · 2024-02-16T08:34:59.717000

24

Views

How to preallocate using JacobianConfig for "Hessian of vector valued function" double Jacobian in Julia ForwardDiff package

Published on 16 February 2024 at 08:34

score 48 · Answer 7 · 2024-02-07T04:52:27.633000

48

Views

JAX `custom_vjp` for functions with multiple outputs

Published on 07 February 2024 at 04:52

score 40 · Answer 8 · 2024-02-03T04:56:18.983000

40

Views

JAX `vjp` fails for vmapped function with `custom_vjp`

Published on 03 February 2024 at 04:56

score 47 · Answer 9 · 2024-02-02T00:51:44.997000

47

Views

JAX `vjp` does not recognize cotangent argument with `custom_vjp`

Published on 02 February 2024 at 00:51

score 43 · Answer 10 · 2024-01-24T17:03:47.360000

43

Views

Failing to return gradients

Published on 24 January 2024 at 17:03

score 67 · Answer 11 · 2023-12-25T12:38:46.543000

67

Views

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

Published on 25 December 2023 at 12:38

score 96 · Answer 12 · 2023-11-22T11:33:37.133000

96

Views

Accurately finding the gradient of values from 2 vectors

Published on 22 November 2023 at 11:33

score 43 · Answer 13 · 2023-11-09T23:25:25.343000

43

Views

Back propagation in pytorch to calculate component wise derivatives of vector

Published on 09 November 2023 at 23:25

score 121 · Answer 14 · 2023-11-02T15:44:12.040000

121

Views

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

Published on 02 November 2023 at 15:44

score 109 · Answer 15 · 2023-10-14T09:25:54.673000

109

Views

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

Published on 14 October 2023 at 09:25

score 261 · Answer 16 · 2023-10-02T22:24:46.903000

261

Views

How can PyTorch-like automatic differentiation work in Rust, given that it does not allow multiple mutable references?

Published on 02 October 2023 at 22:24

score 59 · Answer 17 · 2023-09-27T20:13:37.290000

59

Views

Why is the Jacobian of a 4D matrix calculated incorrectly by PyTorch's torch.autograd.functional.jacobian?

Published on 27 September 2023 at 20:13

score 139 · Answer 18 · 2023-09-24T15:08:08.180000

139

Views

Jacobian of a neural network in pytorch

Published on 24 September 2023 at 15:08

score 92 · Answer 19 · 2023-08-27T13:23:02.937000

92

Views

automatic differentiation and getting the next representable floating point value

Published on 27 August 2023 at 13:23

score 47 · Answer 20 · 2023-08-18T22:36:46.160000

47

Views

Zygote cannot evaluate Hessian if the function argument is annotated

Published on 18 August 2023 at 22:36

List Question

Optimizing an autograd + list comprehension python operation

How to generate jacobian of a tensor-valued function using torch.autograd?

How to implement Carleman Matrix in Haskell?

Calculating two gradients in pytorch and reusing an intermediate gradient

solving an ODE using neural networks

How to preallocate using JacobianConfig for "Hessian of vector valued function" double Jacobian in Julia ForwardDiff package

JAX `custom_vjp` for functions with multiple outputs

JAX `vjp` fails for vmapped function with `custom_vjp`

JAX `vjp` does not recognize cotangent argument with `custom_vjp`

Failing to return gradients

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

Accurately finding the gradient of values from 2 vectors

Back propagation in pytorch to calculate component wise derivatives of vector

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

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

How can PyTorch-like automatic differentiation work in Rust, given that it does not allow multiple mutable references?

Why is the Jacobian of a 4D matrix calculated incorrectly by PyTorch's torch.autograd.functional.jacobian?

Jacobian of a neural network in pytorch

automatic differentiation and getting the next representable floating point value

Zygote cannot evaluate Hessian if the function argument is annotated

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