TL;DR ApET introduces an attention-free, approximation-error guided token compression framework for VLMs that maximally preserves visual information by pruning tokens ...
See more of our coverage in your search results.Encuentra más de nuestra cobertura en los resultados de búsqueda. Add The New York Times on GoogleAgrega The New York Times en Google A few weeks ago, ...
ABSTRACT: This paper presents two sets of considerations on the use of approximations to estimate freight trip generation (FTG) and freight generation (FG) rates, based on a single variable. Following ...
Large language models (LLMs) have gained significant attention in machine learning, shifting the focus from optimizing generalization on small datasets to reducing ...
Abstract: In this article, the disjunctive and conjunctive lattice piecewise affine (PWA) approximations of explicit linear model predictive control (MPC) are proposed. Training data consisting of ...
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