Abstract: Disentanglement learning aims to separate explanatory factors of variation so that different attributes of the data can be well characterized and isolated, which promotes efficient inference ...
We analyze necessary and sufficient conditions on a nonsingular matrix A such that, for any initial vector r₀, an orthogonal basis of the Krylov subspaces ${\cal K}_{n}(A,r_{0})$ is generated by a ...
We propose adding a new parameter-efficient fine-tuning method based on adaptive singular value decomposition (SVD) for continual learning in LLMs. The core idea is to decompose weight matrices into ...
Neural populations can change the computation they perform on very short timescales. Although such flexibility is common, the underlying computational strategies at the population level remain unknown ...
The Annals of Statistics, Vol. 40, No. 4 (August 2012), pp. 2195-2238 (44 pages) This paper considers the problem of clustering a collection of unlabeled data points assumed to lie near a union of ...
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