Zobrazeno 1 - 10
of 2 275
pro vyhledávání: '"Yu, Lili"'
Reconstructing transmission networks is essential for identifying key factors like superspreaders and high-risk locations, which are critical for developing effective pandemic prevention strategies. In this study, we developed a Bayesian framework th
Externí odkaz:
http://arxiv.org/abs/2409.05245
Autor:
Zhou, Chunting, Yu, Lili, Babu, Arun, Tirumala, Kushal, Yasunaga, Michihiro, Shamis, Leonid, Kahn, Jacob, Ma, Xuezhe, Zettlemoyer, Luke, Levy, Omer
We introduce Transfusion, a recipe for training a multi-modal model over discrete and continuous data. Transfusion combines the language modeling loss function (next token prediction) with diffusion to train a single transformer over mixed-modality s
Externí odkaz:
http://arxiv.org/abs/2408.11039
Autor:
Ma, Xuezhe, Yang, Xiaomeng, Xiong, Wenhan, Chen, Beidi, Yu, Lili, Zhang, Hao, May, Jonathan, Zettlemoyer, Luke, Levy, Omer, Zhou, Chunting
The quadratic complexity and weak length extrapolation of Transformers limits their ability to scale to long sequences, and while sub-quadratic solutions like linear attention and state space models exist, they empirically underperform Transformers i
Externí odkaz:
http://arxiv.org/abs/2404.08801
Autor:
Zheng, Caiyun, Chen, Xu, Weng, Lizhu, Guo, Ling, Xu, Haiting, Lin, Meimei, Xue, Yan, Lin, Xiuqin, Yang, Aiqin, Yu, Lili, Xue, Zenggui, Yang, Jing
Publikováno v:
JMIR mHealth and uHealth, Vol 8, Iss 1, p e17055 (2020)
BackgroundPain ratings reported by patients with cancer continue to increase, and numerous computer and phone apps for managing cancer-related pain have been developed recently; however, whether these apps effectively alleviate patients’ pain remai
Externí odkaz:
https://doaj.org/article/16b51f78c2bd448296b9d37e6a5a61ce
In recent years, advances in the large-scale pretraining of language and text-to-image models have revolutionized the field of machine learning. Yet, integrating these two modalities into a single, robust model capable of generating seamless multimod
Externí odkaz:
http://arxiv.org/abs/2309.15564
Autor:
Yu, Lili, Shi, Bowen, Pasunuru, Ramakanth, Muller, Benjamin, Golovneva, Olga, Wang, Tianlu, Babu, Arun, Tang, Binh, Karrer, Brian, Sheynin, Shelly, Ross, Candace, Polyak, Adam, Howes, Russell, Sharma, Vasu, Xu, Puxin, Tamoyan, Hovhannes, Ashual, Oron, Singer, Uriel, Li, Shang-Wen, Zhang, Susan, James, Richard, Ghosh, Gargi, Taigman, Yaniv, Fazel-Zarandi, Maryam, Celikyilmaz, Asli, Zettlemoyer, Luke, Aghajanyan, Armen
We present CM3Leon (pronounced "Chameleon"), a retrieval-augmented, token-based, decoder-only multi-modal language model capable of generating and infilling both text and images. CM3Leon uses the CM3 multi-modal architecture but additionally shows th
Externí odkaz:
http://arxiv.org/abs/2309.02591
Autor:
Zhou, Chunting, Liu, Pengfei, Xu, Puxin, Iyer, Srini, Sun, Jiao, Mao, Yuning, Ma, Xuezhe, Efrat, Avia, Yu, Ping, Yu, Lili, Zhang, Susan, Ghosh, Gargi, Lewis, Mike, Zettlemoyer, Luke, Levy, Omer
Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preference
Externí odkaz:
http://arxiv.org/abs/2305.11206
Autoregressive transformers are spectacular models for short sequences but scale poorly to long sequences such as high-resolution images, podcasts, code, or books. We proposed Megabyte, a multi-scale decoder architecture that enables end-to-end diffe
Externí odkaz:
http://arxiv.org/abs/2305.07185
We propose a new two-stage pre-training framework for video-to-text generation tasks such as video captioning and video question answering: A generative encoder-decoder model is first jointly pre-trained on massive image-text data to learn fundamenta
Externí odkaz:
http://arxiv.org/abs/2305.03204
Autor:
Aghajanyan, Armen, Yu, Lili, Conneau, Alexis, Hsu, Wei-Ning, Hambardzumyan, Karen, Zhang, Susan, Roller, Stephen, Goyal, Naman, Levy, Omer, Zettlemoyer, Luke
Generative language models define distributions over sequences of tokens that can represent essentially any combination of data modalities (e.g., any permutation of image tokens from VQ-VAEs, speech tokens from HuBERT, BPE tokens for language or code
Externí odkaz:
http://arxiv.org/abs/2301.03728