Wedemonstratetheeffectiveness of our approach for SwAV and ALBERT pretraining in realisticconditions and achieve performance comparable to traditional setups at a fraction of the cost.

Givenaninput sentence, G2L2 first looks up the lexicon entries associated with each token.

The profile of a sample is the multiset of its symbol frequencies. We show that for samples of discrete distributions, profile entropy is a fundamental measure unifying the concepts of estimation, inference, and compression.

Clearly, in order for learning to be possible, we must impose some constraints on the size of the function class. One possibility is to bound the number of parameters (i.e., the dimensions of the matrix W), in which case learnability follows from standard VC-dimension or covering number arguments (see Anthony and Bartlett [1999]).

In order to build more useful AI systems, a natural inclination is to try to make them more agentic . But while agents built from language models are touted as the next big advance [Wang et al., 2024],