The "matrix dot product" introduced today is also called the Frobenius inner product, and the corresponding norm ("length" of the matrix viewed as a vector) is the Frobenius norm. When you "flatten" a ...

Their key insight is that it is easier to adaptively round the weights to a finite set of compressed values when the weight and proxy Hessian matrices are incoherent. Intuitively, this is because both ...

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I want to parallelize the kronecker on each matrix, not doing the kronecker product of the tensors. torch.kron(x,y) gives me a tensor of size (10000, 20,20), but I want a shape out of size (100, 20, ...

Keywords: broadband beamformer, constant-beamwidth beamformer, microphone array, Kronecker product, directivity factor, white noise gain, Kaiser window, nonuniform array. Citation: Frank A and Cohen I ...

Since the similarity matrix is a non-positive definite square matrix, a multiple similarity matrix should be merged into a large similarity matrix. Therefore, a more appropriate method named the ...