Zobrazeno 1 - 10
of 215
pro vyhledávání: '"Davis, Jared"'
Autor:
Davis, Jared Quincy, Hanin, Boris, Chen, Lingjiao, Bailis, Peter, Stoica, Ion, Zaharia, Matei
As practitioners seek to surpass the current reliability and quality frontier of monolithic models, Compound AI Systems consisting of many language model inference calls are increasingly employed. In this work, we construct systems, which we call Net
Externí odkaz:
http://arxiv.org/abs/2407.16831
Autor:
Chen, Lingjiao, Davis, Jared Quincy, Hanin, Boris, Bailis, Peter, Stoica, Ion, Zaharia, Matei, Zou, James
Many recent state-of-the-art results in language tasks were achieved using compound systems that perform multiple Language Model (LM) calls and aggregate their responses. However, there is little understanding of how the number of LM calls - e.g., wh
Externí odkaz:
http://arxiv.org/abs/2403.02419
Autor:
Luo, Jerry, Paduraru, Cosmin, Voicu, Octavian, Chervonyi, Yuri, Munns, Scott, Li, Jerry, Qian, Crystal, Dutta, Praneet, Davis, Jared Quincy, Wu, Ningjia, Yang, Xingwei, Chang, Chu-Ming, Li, Ted, Rose, Rob, Fan, Mingyan, Nakhost, Hootan, Liu, Tinglin, Kirkman, Brian, Altamura, Frank, Cline, Lee, Tonker, Patrick, Gouker, Joel, Uden, Dave, Bryan, Warren Buddy, Law, Jason, Fatiha, Deeni, Satra, Neil, Rothenberg, Juliet, Waraich, Mandeep, Carlin, Molly, Tallapaka, Satish, Witherspoon, Sims, Parish, David, Dolan, Peter, Zhao, Chenyu, Mankowitz, Daniel J.
This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems. Building on expertise that began with cooling Google's data centers more efficiently, we recently conducted
Externí odkaz:
http://arxiv.org/abs/2211.07357
Autor:
Chervonyi, Yuri, Dutta, Praneet, Trochim, Piotr, Voicu, Octavian, Paduraru, Cosmin, Qian, Crystal, Karagozler, Emre, Davis, Jared Quincy, Chippendale, Richard, Bajaj, Gautam, Witherspoon, Sims, Luo, Jerry
We present a hybrid industrial cooling system model that embeds analytical solutions within a multi-physics simulation. This model is designed for reinforcement learning (RL) applications and balances simplicity with simulation fidelity and interpret
Externí odkaz:
http://arxiv.org/abs/2207.13131
Autor:
Yuan, Binhang, He, Yongjun, Davis, Jared Quincy, Zhang, Tianyi, Dao, Tri, Chen, Beidi, Liang, Percy, Re, Christopher, Zhang, Ce
Training foundation models, such as GPT-3 and PaLM, can be extremely expensive, often involving tens of thousands of GPUs running continuously for months. These models are typically trained in specialized clusters featuring fast, homogeneous intercon
Externí odkaz:
http://arxiv.org/abs/2206.01288
Autor:
Bommasani, Rishi, Hudson, Drew A., Adeli, Ehsan, Altman, Russ, Arora, Simran, von Arx, Sydney, Bernstein, Michael S., Bohg, Jeannette, Bosselut, Antoine, Brunskill, Emma, Brynjolfsson, Erik, Buch, Shyamal, Card, Dallas, Castellon, Rodrigo, Chatterji, Niladri, Chen, Annie, Creel, Kathleen, Davis, Jared Quincy, Demszky, Dora, Donahue, Chris, Doumbouya, Moussa, Durmus, Esin, Ermon, Stefano, Etchemendy, John, Ethayarajh, Kawin, Fei-Fei, Li, Finn, Chelsea, Gale, Trevor, Gillespie, Lauren, Goel, Karan, Goodman, Noah, Grossman, Shelby, Guha, Neel, Hashimoto, Tatsunori, Henderson, Peter, Hewitt, John, Ho, Daniel E., Hong, Jenny, Hsu, Kyle, Huang, Jing, Icard, Thomas, Jain, Saahil, Jurafsky, Dan, Kalluri, Pratyusha, Karamcheti, Siddharth, Keeling, Geoff, Khani, Fereshte, Khattab, Omar, Koh, Pang Wei, Krass, Mark, Krishna, Ranjay, Kuditipudi, Rohith, Kumar, Ananya, Ladhak, Faisal, Lee, Mina, Lee, Tony, Leskovec, Jure, Levent, Isabelle, Li, Xiang Lisa, Li, Xuechen, Ma, Tengyu, Malik, Ali, Manning, Christopher D., Mirchandani, Suvir, Mitchell, Eric, Munyikwa, Zanele, Nair, Suraj, Narayan, Avanika, Narayanan, Deepak, Newman, Ben, Nie, Allen, Niebles, Juan Carlos, Nilforoshan, Hamed, Nyarko, Julian, Ogut, Giray, Orr, Laurel, Papadimitriou, Isabel, Park, Joon Sung, Piech, Chris, Portelance, Eva, Potts, Christopher, Raghunathan, Aditi, Reich, Rob, Ren, Hongyu, Rong, Frieda, Roohani, Yusuf, Ruiz, Camilo, Ryan, Jack, Ré, Christopher, Sadigh, Dorsa, Sagawa, Shiori, Santhanam, Keshav, Shih, Andy, Srinivasan, Krishnan, Tamkin, Alex, Taori, Rohan, Thomas, Armin W., Tramèr, Florian, Wang, Rose E., Wang, William, Wu, Bohan, Wu, Jiajun, Wu, Yuhuai, Xie, Sang Michael, Yasunaga, Michihiro, You, Jiaxuan, Zaharia, Matei, Zhang, Michael, Zhang, Tianyi, Zhang, Xikun, Zhang, Yuhui, Zheng, Lucia, Zhou, Kaitlyn, Liang, Percy
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically
Externí odkaz:
http://arxiv.org/abs/2108.07258
Approximate bi-level optimization (ABLO) consists of (outer-level) optimization problems, involving numerical (inner-level) optimization loops. While ABLO has many applications across deep learning, it suffers from time and memory complexity proporti
Externí odkaz:
http://arxiv.org/abs/2106.02487
The Transformer architecture has revolutionized deep learning on sequential data, becoming ubiquitous in state-of-the-art solutions for a wide variety of applications. Yet vanilla Transformers are notoriously resource-expensive, requiring $O(L^2)$ in
Externí odkaz:
http://arxiv.org/abs/2012.11346
Autor:
Davis, Jared A., Schlauch, Robert C.1 rschlauch@usf.edu
Publikováno v:
Journal of Social & Clinical Psychology. Apr2024, Vol. 43 Issue 2, p152-179. 28p.
Autor:
Choromanski, Krzysztof, Likhosherstov, Valerii, Dohan, David, Song, Xingyou, Gane, Andreea, Sarlos, Tamas, Hawkins, Peter, Davis, Jared, Mohiuddin, Afroz, Kaiser, Lukasz, Belanger, David, Colwell, Lucy, Weller, Adrian
We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors
Externí odkaz:
http://arxiv.org/abs/2009.14794