Whether there is any need to modify the backward function in pytorch?...

Update step in PyTorch implementation of Newton's method...

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

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`...

How can PyTorch-like automatic differentiation work in Rust, given that it does not allow multiple m...

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

automatic differentiation and getting the next representable floating point value...

What is differentiable programming?...

Is there a fast way of calculating 1000 Jacobians of a neural network?...

how to apply gradients manually in pytorch...

What is wrong with this implementation of matmul for automatic differentiation?...

How can I implement a vmappable sum over a dynamic range in Jax?...

Confused about evaluating vector-Jacobian-product with non-identity vectors (JAX)...

computational complexity of higher order derivatives with AD in jax...

How to use and interpret JAX Vector-Jacobian Product (VJP) for this example?...

How to get the gradients of network parameters for a derivative-based loss?...

Can tf.gradienttape() calculate gradient of other library's function...

jax minimization with stochastically estimated gradients...

jax automatic differentiation...

Derivative of Scalar Expansion in PyTorch...

Why does jax.grad(lambda v: jnp.linalg.norm(v-v))(jnp.ones(2)) produce nans?...

Pytorch torch.linalg.qr is differentiable?...

Derivative in JAX and Sympy not coinciding...

Obtaining zeros in this derivative in Jax...

second derivative is None in tensorflow automatic differentiation...

Issues with non-hashable static arguments when forming...

Torch automatic differentiation for matrix defined with components of vector...

How to link a C++11 program to the Adept library...