I have noticed that this work is implemented by tensorflow where the graph of the gradient can be constructed. I wonder how to compute two-order partial derivative with non-graph based deep-learning ...

Two windows are designed considering the sidelobe energy or the highest sidelobe level as cost functions for minimizing the sidelobes of their derivatives. The SST using the proposed window provides a ...

🐛 Bug torch.autograd.grad seems to not create graph if the function being differentiated has a second derivative of 0. This causes unexpected issues when we try to take a higher-order gradient. The ...

In the paper, by virtue of convolution theorem for the Laplace transforms, Bernstein’s theorem for completely monotonic functions, some properties of a function involving exponential function, and ...

While pre-training has transformed many fields in deep learning tremendously, its application to three-dimensional crystal structures and materials science remains limited and under-explored. In ...

In many applications, such as brain network connectivity or shopping recommendations, the underlying graph explaining the different interactions between participating agents is unknown. Moreover, many ...