However, recent works reveal that it is non-trivial to find a proper initialization of the prompt tokens.

To train an LLM, one needs to alternatingly run'forward' computations and'backward' computations.

Neural Information Processing Systems http://nips.cc/

In the total least squares problem, one is given an m n matrix A, and an m d matrix B, and one seeks to "correct" both A and B, obtaining matrices  and B, so that there exists an X satisfying the equation ÂX = B. Typically the problem is overconstrained, meaning that m max(n, d).

Deep neural networks have achieved impressive performance in many areas. Designing a fast and provable method for training neural networks is a fundamental question in machine learning. The classical training method requires paying Ω(mnd) cost for both forward computation and backward computation, where m is the width of the neural network, and we are given n training points in d-dimensional space.

Despite being powerful and well-understood, the kernel ridge regression suffers from the costly computation when dealing with large datasets, since generally implementation of Eq. (1) requires O(n3) running time.