Search Results for author: Amit Blonder

Found 1 papers, 1 papers with code

Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps

2 code implementations ICLR 2020 Tri Dao, Nimit S. Sohoni, Albert Gu, Matthew Eichhorn, Amit Blonder, Megan Leszczynski, Atri Rudra, Christopher Ré

Modern neural network architectures use structured linear transformations, such as low-rank matrices, sparse matrices, permutations, and the Fourier transform, to improve inference speed and reduce memory usage compared to general linear maps.

Image Classification speech-recognition +1

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