Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Ghorbani, Behrooz"'
Autor:
OpenAI, Hurst, Aaron, Lerer, Adam, Goucher, Adam P., Perelman, Adam, Ramesh, Aditya, Clark, Aidan, Ostrow, AJ, Welihinda, Akila, Hayes, Alan, Radford, Alec, Mądry, Aleksander, Baker-Whitcomb, Alex, Beutel, Alex, Borzunov, Alex, Carney, Alex, Chow, Alex, Kirillov, Alex, Nichol, Alex, Paino, Alex, Renzin, Alex, Passos, Alex Tachard, Kirillov, Alexander, Christakis, Alexi, Conneau, Alexis, Kamali, Ali, Jabri, Allan, Moyer, Allison, Tam, Allison, Crookes, Amadou, Tootoochian, Amin, Tootoonchian, Amin, Kumar, Ananya, Vallone, Andrea, Karpathy, Andrej, Braunstein, Andrew, Cann, Andrew, Codispoti, Andrew, Galu, Andrew, Kondrich, Andrew, Tulloch, Andrew, Mishchenko, Andrey, Baek, Angela, Jiang, Angela, Pelisse, Antoine, Woodford, Antonia, Gosalia, Anuj, Dhar, Arka, Pantuliano, Ashley, Nayak, Avi, Oliver, Avital, Zoph, Barret, Ghorbani, Behrooz, Leimberger, Ben, Rossen, Ben, Sokolowsky, Ben, Wang, Ben, Zweig, Benjamin, Hoover, Beth, Samic, Blake, McGrew, Bob, Spero, Bobby, Giertler, Bogo, 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Markov, Todor, Sherbakov, Toki, Rubin, Tom, Stasi, Tom, Kaftan, Tomer, Heywood, Tristan, Peterson, Troy, Walters, Tyce, Eloundou, Tyna, Qi, Valerie, Moeller, Veit, Monaco, Vinnie, Kuo, Vishal, Fomenko, Vlad, Chang, Wayne, Zheng, Weiyi, Zhou, Wenda, Manassra, Wesam, Sheu, Will, Zaremba, Wojciech, Patil, Yash, Qian, Yilei, Kim, Yongjik, Cheng, Youlong, Zhang, Yu, He, Yuchen, Zhang, Yuchen, Jin, Yujia, Dai, Yunxing, Malkov, Yury
GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs
Externí odkaz:
http://arxiv.org/abs/2410.21276
Autor:
Choi, Dami, Xin, Derrick, Dadkhahi, Hamid, Gilmer, Justin, Garg, Ankush, Firat, Orhan, Yeh, Chih-Kuan, Dai, Andrew M., Ghorbani, Behrooz
In this paper, we empirically study the optimization dynamics of multi-task learning, particularly focusing on those that govern a collection of tasks with significant data imbalance. We present a simple yet effective method of pre-training on high-r
Externí odkaz:
http://arxiv.org/abs/2312.06134
Recent advances in machine translation (MT) have shown that Minimum Bayes Risk (MBR) decoding can be a powerful alternative to beam search decoding, especially when combined with neural-based utility functions. However, the performance of MBR decodin
Externí odkaz:
http://arxiv.org/abs/2305.09860
In this work, we provide a large-scale empirical study of the scaling properties of multilingual neural machine translation models. We examine how increases in the model size affect the model performance and investigate the role of the training mixtu
Externí odkaz:
http://arxiv.org/abs/2302.09650
Autor:
Zhang, Yichi, Garg, Ankush, Cao, Yuan, Lew, Łukasz, Ghorbani, Behrooz, Zhang, Zhiru, Firat, Orhan
Publikováno v:
Published at NeurIPS 2023
The rapid scaling of language models is motivating research using low-bitwidth quantization. In this work, we propose a novel binarization technique for Transformers applied to machine translation (BMT), the first of its kind. We identify and address
Externí odkaz:
http://arxiv.org/abs/2302.04907
Recent research has proposed a series of specialized optimization algorithms for deep multi-task models. It is often claimed that these multi-task optimization (MTO) methods yield solutions that are superior to the ones found by simply optimizing a w
Externí odkaz:
http://arxiv.org/abs/2209.11379
Autor:
Cohen, Jeremy M., Ghorbani, Behrooz, Krishnan, Shankar, Agarwal, Naman, Medapati, Sourabh, Badura, Michal, Suo, Daniel, Cardoze, David, Nado, Zachary, Dahl, George E., Gilmer, Justin
Very little is known about the training dynamics of adaptive gradient methods like Adam in deep learning. In this paper, we shed light on the behavior of these algorithms in the full-batch and sufficiently large batch settings. Specifically, we empir
Externí odkaz:
http://arxiv.org/abs/2207.14484
Autor:
Bansal, Yamini, Ghorbani, Behrooz, Garg, Ankush, Zhang, Biao, Krikun, Maxim, Cherry, Colin, Neyshabur, Behnam, Firat, Orhan
In this work, we study the effect of varying the architecture and training data quality on the data scaling properties of Neural Machine Translation (NMT). First, we establish that the test loss of encoder-decoder transformer models scales as a power
Externí odkaz:
http://arxiv.org/abs/2202.01994
Autor:
Zhang, Biao, Ghorbani, Behrooz, Bapna, Ankur, Cheng, Yong, Garcia, Xavier, Shen, Jonathan, Firat, Orhan
Natural language understanding and generation models follow one of the two dominant architectural paradigms: language models (LMs) that process concatenated sequences in a single stack of layers, and encoder-decoder models (EncDec) that utilize separ
Externí odkaz:
http://arxiv.org/abs/2202.00528
Autor:
Gilmer, Justin, Ghorbani, Behrooz, Garg, Ankush, Kudugunta, Sneha, Neyshabur, Behnam, Cardoze, David, Dahl, George, Nado, Zachary, Firat, Orhan
In this work, we study the evolution of the loss Hessian across many classification tasks in order to understand the effect the curvature of the loss has on the training dynamics. Whereas prior work has focused on how different learning rates affect
Externí odkaz:
http://arxiv.org/abs/2110.04369