Zobrazeno 1 - 10
of 342
pro vyhledávání: '"Liu, Jerry"'
Autor:
Fifty, Christopher, Junkins, Ronald G., Duan, Dennis, Iger, Aniketh, Liu, Jerry W., Amid, Ehsan, Thrun, Sebastian, Ré, Christopher
Vector Quantized Variational AutoEncoders (VQ-VAEs) are designed to compress a continuous input to a discrete latent space and reconstruct it with minimal distortion. They operate by maintaining a set of vectors -- often referred to as the codebook -
Externí odkaz:
http://arxiv.org/abs/2410.06424
Autor:
Guo, Michael Y., Liu, Jerry, Balmes, Patricia, Yanta, Christine, Motamedi, Ali, Phang, P Terry
Publikováno v:
In The American Journal of Surgery September 2024 235
Autor:
Anderson, Michael, Chen, Benny, Chen, Stephen, Deng, Summer, Fix, Jordan, Gschwind, Michael, Kalaiah, Aravind, Kim, Changkyu, Lee, Jaewon, Liang, Jason, Liu, Haixin, Lu, Yinghai, Montgomery, Jack, Moorthy, Arun, Nadathur, Satish, Naghshineh, Sam, Nayak, Avinash, Park, Jongsoo, Petersen, Chris, Schatz, Martin, Sundaram, Narayanan, Tang, Bangsheng, Tang, Peter, Yang, Amy, Yu, Jiecao, Yuen, Hector, Zhang, Ying, Anbudurai, Aravind, Balan, Vandana, Bojja, Harsha, Boyd, Joe, Breitbach, Matthew, Caldato, Claudio, Calvo, Anna, Catron, Garret, Chandwani, Sneh, Christeas, Panos, Cottel, Brad, Coutinho, Brian, Dalli, Arun, Dhanotia, Abhishek, Duncan, Oniel, Dzhabarov, Roman, Elmir, Simon, Fu, Chunli, Fu, Wenyin, Fulthorp, Michael, Gangidi, Adi, Gibson, Nick, Gordon, Sean, Hernandez, Beatriz Padilla, Ho, Daniel, Huang, Yu-Cheng, Johansson, Olof, Juluri, Shishir, Kanaujia, Shobhit, Kesarkar, Manali, Killinger, Jonathan, Kim, Ben, Kulkarni, Rohan, Lele, Meghan, Li, Huayu, Li, Huamin, Li, Yueming, Liu, Cynthia, Liu, Jerry, Maher, Bert, Mallipedi, Chandra, Mangla, Seema, Matam, Kiran Kumar, Mehta, Jubin, Mehta, Shobhit, Mitchell, Christopher, Muthiah, Bharath, Nagarkatte, Nitin, Narasimha, Ashwin, Nguyen, Bernard, Ortiz, Thiara, Padmanabha, Soumya, Pan, Deng, Poojary, Ashwin, Ye, Qi, Raginel, Olivier, Rajagopal, Dwarak, Rice, Tristan, Ross, Craig, Rotem, Nadav, Russ, Scott, Shah, Kushal, Shan, Baohua, Shen, Hao, Shetty, Pavan, Skandakumaran, Krish, Srinivasan, Kutta, Sumbaly, Roshan, Tauberg, Michael, Tzur, Mor, Verma, Sidharth, Wang, Hao, Wang, Man, Wei, Ben, Xia, Alex, Xu, Chenyu, Yang, Martin, Zhang, Kai, Zhang, Ruoxi, Zhao, Ming, Zhao, Whitney, Zhu, Rui, Mathews, Ajit, Qiao, Lin, Smelyanskiy, Misha, Jia, Bill, Rao, Vijay
In this paper, we provide a deep dive into the deployment of inference accelerators at Facebook. Many of our ML workloads have unique characteristics, such as sparse memory accesses, large model sizes, as well as high compute, memory and network band
Externí odkaz:
http://arxiv.org/abs/2107.04140
Autor:
Hsieh, Kristin, Hotca, Alexandra Elena, Dickstein, Daniel R., Lehrer, Eric J., Hsieh, Celina, Gupta, Vishal, Sindhu, Kunal K., Liu, Jerry T., Reed, Samuel H., Chhabra, Arpit, Misiukiewicz, Krzysztof, Roof, Scott, Kahn, Mohemmed Nazir, Kirke, Diana, Urken, Mark, Posner, Marshall, Genden, Eric, Bakst, Richard L.
Publikováno v:
In Advances in Radiation Oncology April 2024 9(4)
An intelligent agent operating in the real-world must balance achieving its goal with maintaining the safety and comfort of not only itself, but also other participants within the surrounding scene. This requires jointly reasoning about the behavior
Externí odkaz:
http://arxiv.org/abs/2101.06832
We present a novel compression algorithm for reducing the storage of LiDAR sensor data streams. Our model exploits spatio-temporal relationships across multiple LiDAR sweeps to reduce the bitrate of both geometry and intensity values. Towards this go
Externí odkaz:
http://arxiv.org/abs/2011.07590
Autor:
Liu, Jerry, Wang, Shenlong, Ma, Wei-Chiu, Shah, Meet, Hu, Rui, Dhawan, Pranaab, Urtasun, Raquel
We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames. Unlike prior learning-based approaches, we reduce complexity by not performing any form of explicit transformatio
Externí odkaz:
http://arxiv.org/abs/2008.09180
In this paper, we study two challenging and less-touched problems in single image dehazing, namely, how to make deep learning achieve image dehazing without training on the ground-truth clean image (unsupervised) and a image collection (untrained). A
Externí odkaz:
http://arxiv.org/abs/2006.16829
We present a novel deep compression algorithm to reduce the memory footprint of LiDAR point clouds. Our method exploits the sparsity and structural redundancy between points to reduce the bitrate. Towards this goal, we first encode the LiDAR points i
Externí odkaz:
http://arxiv.org/abs/2005.07178