Joseph E. Gonzalez
Impact in
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- Graph Theory and Algorithms
- Advanced Neural Network Applications
- Hardware and Architecture top 0.5%
- Parallel Computing and Optimization Techniques
Papers in
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- Domain Adaptation and Few-Shot Learning 15
- Machine Learning and Data Classification 11
- Data Stream Mining Techniques 9
- Co-authors
- Carlos GuestrinYucheng LowDanny BicksonIon StoicaMichael J. FranklinJoseph M. HellersteinHaijie GuReynold Xin
- Journals
- Proceedings of the VLDB Endowment (5 papers)IEEE Robotics and Automation Letters (3 papers)Communications of the ACM (3 papers)JAMA Cardiology (1 paper)IEEE Transactions on Neural Networks and Learning Systems (1 paper)
- Partner nations
- United StatesUnited KingdomIsrael
In The Last Decade
Joseph E. Gonzalez
117 papers receiving 7.8k citations
Hit Papers
Peers
Comparison fields: 5 of 166
- Computer Vision and Pattern Recognition 3.6k
- Hardware and Architecture 816
- Computer Networks and Communications 2.7k
- Artificial Intelligence 3.7k
- Information Systems 2.5k
Countries citing papers authored by Joseph E. Gonzalez
This map shows the geographic impact of Joseph E. Gonzalez's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Joseph E. Gonzalez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joseph E. Gonzalez more than expected).
Fields of papers citing papers by Joseph E. Gonzalez
This network shows the impact of papers produced by Joseph E. Gonzalez. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Joseph E. Gonzalez. The network helps show where Joseph E. Gonzalez may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Joseph E. Gonzalez, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 2 | |
| 2 | Efficient Memory Management for Large Language Model Serving with PagedAttention Hit paper breakdown → | 2023 | 329 |
| 3 | 2023 | 2 | |
| 4 | TenSet: A Large-scale Program Performance Dataset for Learned Tensor Compilers | 2021 | 8 |
| 5 | NovelD: A Simple yet Effective Exploration Criterion | 2021 | 16 |
| 6 | Representing Long-Range Context for Graph Neural Networks with Global Attention | 2021 | 7 |
| 7 | ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training | 2021 | 3 |
| 8 | Deep Mixture of Experts via Shallow Embedding | 2020 | 16 |
| 9 | Serverless Boom or Bust? An Analysis of Economic Incentives. | 2020 | 4 |
| 10 | Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers | 2020 | 39 |
| 11 | Deep Reinforcement Learning in System Optimization. | 2019 | 2 |
| 12 | Extending Deep Model Predictive Control with Safety Augmented Value Estimation from Demonstrations. | 2019 | 1 |
| 13 | On-Policy Robot Imitation Learning from a Converging Supervisor | 2019 | 4 |
| 14 | InferLine: ML Inference Pipeline Composition Framework. | 2018 | 17 |
| 15 | ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Enterprise Scale. | 2018 | 5 |
| 16 | Random projection design for scalable implicit smoothing of randomly observed stochastic processes | 2017 | 1 |
| 17 | Opaque: an oblivious and encrypted distributed analytics platform | 2017 | 137 |
| 18 | Ray RLLib: A Composable and Scalable Reinforcement Learning Library | 2017 | 49 |
| 19 | Parallel Double Greedy Submodular Maximization | 2014 | 11 |
| 20 | PowerGraph: distributed graph-parallel computation on natural graphs Hit paper breakdown → | 2012 | 1000 |
About Joseph E. Gonzalez
Joseph E. Gonzalez is a scholar working on Computational Mathematics, Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture and Computer Networks and Communications, having authored 122 papers that have together received 8.1k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (15 papers), Cloud Computing and Resource Management (15 papers), Advanced Neural Network Applications (14 papers), Multimodal Machine Learning Applications (13 papers), Machine Learning and Data Classification (11 papers), Graph Theory and Algorithms (10 papers), Robot Manipulation and Learning (9 papers) and Data Stream Mining Techniques (9 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (3.6k citations), Hardware and Architecture (816 citations), Computer Networks and Communications (2.7k citations), Artificial Intelligence (3.7k citations) and Information Systems (2.5k citations). Joseph E. Gonzalez has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Carlos Guestrin, Yucheng Low, Danny Bickson, Ion Stoica, Michael J. Franklin, Joseph M. Hellerstein, Haijie Gu, Reynold Xin, Aapo Kyrola and Ankur Dave. Their work appears in journals such as Proceedings of the VLDB Endowment, IEEE Robotics and Automation Letters, Communications of the ACM, JAMA Cardiology and IEEE Transactions on Neural Networks and Learning Systems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.