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Publication search results
found 52 matches
- 2022
- Paul Barham, Aakanksha Chowdhery, Jeff Dean, Sanjay Ghemawat, Steven Hand, Dan Hurt, Michael Isard, Hyeontaek Lim, Ruoming Pang, Sudip Roy, Brennan Saeta, Parker Schuh, Ryan Sepassi, Laurent El Shafey, Chandramohan A. Thekkath, Yonghui Wu:
Pathways: Asynchronous Distributed Dataflow for ML. MLSys 2022 - Jie Zhao, Xiong Gao, Ruijie Xia, Zhaochuang Zhang, Deshi Chen, Lei Chen, Renwei Zhang, Zhen Geng, Bin Cheng, Xuefeng Jin:
Apollo: Automatic Partition-based Operator Fusion through Layer by Layer Optimization. MLSys 2022 - Runsheng Guo, Victor Guo, Antonio Kim, Josh Hildred, Khuzaima Daudjee:
Hydrozoa: Dynamic Hybrid-Parallel DNN Training on Serverless Containers. MLSys 2022 - Yi Ding, Avinash Rao, Hyebin Song, Rebecca Willett, Henry Hoffmann:
NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction. MLSys 2022 - Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris S. Papailiopoulos:
On the Utility of Gradient Compression in Distributed Training Systems. MLSys 2022 - Oznur Alkan, Dennis Wei, Massimiliano Mattetti, Rahul Nair, Elizabeth Daly, Diptikalyan Saha:
FROTE: Feedback Rule-Driven Oversampling for Editing Models. MLSys 2022 - Kartikeya Bhardwaj, Milos Milosavljevic, Liam O'Neil, Dibakar Gope, Ramon Matas Navarro, Alex Chalfin, Naveen Suda, Lingchuan Meng, Danny Loh:
Collapsible Linear Blocks for Super-Efficient Super Resolution. MLSys 2022 - John Chen, Cameron R. Wolfe, Tasos Kyrillidis:
REX: Revisiting Budgeted Training with an Improved Schedule. MLSys 2022 - Junguk Cho, Diman Zad Tootaghaj, Lianjie Cao, Puneet Sharma:
SLA-Driven ML Inference Framework for Clouds with Hetergeneous Accelerators. MLSys 2022 - Aditya Desai, Li Chou, Anshumali Shrivastava:
Random Offset Block Embedding (ROBE) for compressed embedding tables in deep learning recommendation systems. MLSys 2022 - Pradeep Dogga, Karthik Narasimhan, Anirudh Sivaraman, Shiv Kumar Saini, George Varghese, Ravi Netravali:
Revelio: ML-Generated Debugging Queries for Finding Root Causes in Distributed Systems. MLSys 2022 - Kuntai Du, Qizheng Zhang, Anton Arapin, Haodong Wang, Zhengxu Xia, Junchen Jiang:
AccMPEG: Optimizing Video Encoding for Accurate Video Analytics. MLSys 2022 - Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry:
The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding. MLSys 2022 - Wei Hao, Aahil Awatramani, Jiayang Hu, Chengzhi Mao, Pin-Chun Chen, Eyal Cidon, Asaf Cidon, Junfeng Yang:
A Tale of Two Models: Constructing Evasive Attacks on Edge Models. MLSys 2022 - Hanpeng Hu, Chenyu Jiang, Yuchen Zhong, Yanghua Peng, Chuan Wu, Yibo Zhu, Haibin Lin, Chuanxiong Guo:
dPRO: A Generic Performance Diagnosis and Optimization Toolkit for Expediting Distributed DNN Training. MLSys 2022 - Zhiming Hu, Angela Ning Ye, Iqbal Mohomed:
mmSampler: Efficient Frame Sampler for Multimodal Video Retrieval. MLSys 2022 - Yimin Huang, Yujun Li, Hanrong Ye, Zhenguo Li, Zhihua Zhang:
Improving Model Training with Multi-fidelity Hyperparameter Evaluation. MLSys 2022 - Dzmitry Huba, John Nguyen, Kshitiz Malik, Ruiyu Zhu, Mike Rabbat, Ashkan Yousefpour, Carole-Jean Wu, Hongyuan Zhan, Pavel Ustinov, Harish Srinivas, Kaikai Wang, Anthony Shoumikhin, Jesik Min, Mani Malek:
PAPAYA: Practical, Private, and Scalable Federated Learning. MLSys 2022 - Tim Kaler, Nickolas Stathas, Anne Ouyang, Alexandros-Stavros Iliopoulos, Tao B. Schardl, Charles E. Leiserson, Jie Chen:
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining. MLSys 2022 - Michael Kuchnik, Ana Klimovic, Jiri Simsa, Virginia Smith, George Amvrosiadis:
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines. MLSys 2022 - Shurui Li, Puneet Gupta:
Bit-serial Weight Pools: Compression and Arbitrary Precision Execution of Neural Networks on Resource Constrained Processors. MLSys 2022 - Zichang Liu, Zhaozhuo Xu, Alan Baonan Ji, Junyan Zhang, Jonathan Li, Beidi Chen, Anshumali Shrivastava:
HALOS: Hashing Large Output Space for Cheap Inference. MLSys 2022 - Liang Luo, Peter West, Pratyush Patel, Arvind Krishnamurthy, Luis Ceze:
SRIFTY: Swift and Thrifty Distributed Neural Network Training on the Cloud. MLSys 2022 - Ankur Mallick, Kevin Hsieh, Behnaz Arzani, Gauri Joshi:
Matchmaker: Data Drift Mitigation in Machine Learning for Large-Scale Systems. MLSys 2022 - Hesham Mostafa:
Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs. MLSys 2022 - Corey J. Nolet, Divye Gala, Edward Raff, Joe Eaton, Brad Rees, Tim Oates:
GPU Semiring Primitives for Sparse Neighborhood Methods. MLSys 2022 - Andrew Or, Haoyu Zhang, Michael None Freedman:
VirtualFlow: Decoupling Deep Learning Models from the Underlying Hardware. MLSys 2022 - Niketan Pansare, Jay Katukuri, Aditya Arora, Frank Cipollone, Riyaaz Shaik, Noyan Tokgozoglu, Chandru Venkataraman:
Learning Compressed Embeddings for On-Device Inference. MLSys 2022 - Seo Jin Park, Joshua Fried, Sunghyun Kim, Mohammad Alizadeh, Adam Belay:
Efficient Strong Scaling Through Burst Parallel Training. MLSys 2022 - Wasu Piriyakulkij, Cristina Menghini, Ross Briden, Nihal V. Nayak, Jeffrey Zhu, Elaheh Raisi, Stephen H. Bach:
TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data. MLSys 2022
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