Spectral Transformers
We'll discuss a new technique for sequence modeling for prediction tasks with long range dependencies and fast inference/generation. At the heart of the method is a new formulation for state space model
We'll discuss a new technique for sequence modeling for prediction tasks with long range dependencies and fast inference/generation. At the heart of the method is a new formulation for state space model
Let p be an odd prime and f(x) a polynomial of degree at least 5 with complex coefficients and without repeated roots. Suppose that all the coefficients of f(x) lie in a subfield K suc
I will discuss some well-known and less-known papers of Turing, exemplify the scope of deep, prescient ideas he put forth, and mention follow-up work on these by the Theoretical CS community.
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Sparse principal component analysis (PCA) is a powerful method for low-rank and sparse signal recovery, applicable to covariance estimation, dimension reduction, and feature selection. In this work, w
A common theme in mathematics is that limits of finite objects are well behaved. This allows one to prove many theorems about finitely approximable objects, while leaving the general case op