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How to get more performance out of automatic differentiation?

score 649 · Answer 1 · 2025-01-01 18:47:53

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How to get more performance out of automatic differentiation?

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score 209 · Answer 2 · 2025-01-02 03:01:59

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score 105 · Answer 3 · 2025-01-07 16:11:26

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

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score 3814 · Answer 4 · 2025-01-02 03:05:44

Automatic differentiation library in Scheme / Common Lisp / Clojure

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Is sympy solve compatible with tensorflow GradientTape?

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How does TensorFlow compute the gradient of vgg19.preprocess_input?

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

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score 80 · Answer 9 · 2025-01-02 03:09:33

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

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score 92 · Answer 10 · 2025-01-02 03:09:13

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

92 views Asked by loonatick At 02 January 2025 at 03:09

TechQA.

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?

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