Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Leviathan, Yaniv"'
Unneeded elements in the attention's context degrade performance. We introduce Selective Attention, a simple parameter-free change to the standard attention mechanism which reduces attention to unneeded elements. Selective attention improves language
Externí odkaz:
http://arxiv.org/abs/2410.02703
We present GameNGen, the first game engine powered entirely by a neural model that enables real-time interaction with a complex environment over long trajectories at high quality. GameNGen can interactively simulate the classic game DOOM at over 20 f
Externí odkaz:
http://arxiv.org/abs/2408.14837
Autor:
Sun, Ziteng, Mendlovic, Uri, Leviathan, Yaniv, Aharoni, Asaf, Beirami, Ahmad, Ro, Jae Hun, Suresh, Ananda Theertha
Speculative decoding is an effective method for lossless acceleration of large language models during inference. It uses a fast model to draft a block of tokens which are then verified in parallel by the target model, and provides a guarantee that th
Externí odkaz:
http://arxiv.org/abs/2403.10444
Text-to-image diffusion models achieved a remarkable leap in capabilities over the last few years, enabling high-quality and diverse synthesis of images from a textual prompt. However, even the most advanced models often struggle to precisely follow
Externí odkaz:
http://arxiv.org/abs/2310.16656
We present Face0, a novel way to instantaneously condition a text-to-image generation model on a face, in sample time, without any optimization procedures such as fine-tuning or inversions. We augment a dataset of annotated images with embeddings of
Externí odkaz:
http://arxiv.org/abs/2306.06638
Autor:
Molad, Eyal, Horwitz, Eliahu, Valevski, Dani, Acha, Alex Rav, Matias, Yossi, Pritch, Yael, Leviathan, Yaniv, Hoshen, Yedid
Text-driven image and video diffusion models have recently achieved unprecedented generation realism. While diffusion models have been successfully applied for image editing, very few works have done so for video editing. We present the first diffusi
Externí odkaz:
http://arxiv.org/abs/2302.01329
Inference from large autoregressive models like Transformers is slow - decoding K tokens takes K serial runs of the model. In this work we introduce speculative decoding - an algorithm to sample from autoregressive models faster without any changes t
Externí odkaz:
http://arxiv.org/abs/2211.17192
Text-driven image generation methods have shown impressive results recently, allowing casual users to generate high quality images by providing textual descriptions. However, similar capabilities for editing existing images are still out of reach. Te
Externí odkaz:
http://arxiv.org/abs/2210.09477
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