Atithi Acharya @atithi8 - Twitter Profile

almost 4 years ago

We also identified how higher non-trivial eigenvectors of the Laplacian can be used to obtain further insights into the correlations underlying the datasets. Reach out to any of the authors @atithi8 @QuantumManuel @JingChen105 Jacob Miller, @aperdomoortiz if you have questions.

atithi8's tweet photo. We also identified how higher non-trivial eigenvectors of the Laplacian can be used to obtain further insights into the correlations underlying the datasets. Reach out to any of the authors @atithi8 @QuantumManuel @JingChen105 Jacob Miller, @aperdomoortiz if you have questions. https://t.co/CaafjFkh0z

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Atithi Acharya @atithi8

almost 4 years ago

Have you ever wondered what's the optimal ordering of sites that ensures data-model alignment? We use tools from spectral graph theory to search for optimal permutations of sites such that your model can best learn from your data. https://t.co/FmuKqkS4Vn

atithi8's tweet photo. Have you ever wondered what's the optimal ordering of sites that ensures data-model alignment? We use tools from spectral graph theory to search for optimal permutations of sites such that your model can best learn from your data. https://t.co/FmuKqkS4Vn https://t.co/IV9AL2dz6i

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Atithi Acharya @atithi8

almost 4 years ago

We demonstrate the effectiveness of our scheme by finding improvements in the performance of generative models based on matrix product states (MPS) across a variety of datasets. The varying degrees of improvement can be attributed to the spectral gap.