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Training autonomous vehicles can be dramatically sped up: MOSAIC achieves state-of-the-art driving performance with 80% less data by intelligently selecting training examples based on scaling laws.
Swap out slow, one-token-at-a-time generation in VLMs for a 6x speed boost, without sacrificing quality, using a surprisingly simple direct conversion to block-diffusion decoding.
Achieve 49% and 19% better Chamfer distance than state-of-the-art dynamic surface reconstruction methods on Hi4D and CMU Panoptic datasets, respectively, by enforcing temporal consistency in Gaussian Splatting.
