Christopher De
- Hardware and Architecture top 5%
- Parallel Computing and Optimization Techniques 9
- Embedded Systems Design Techniques 4
- Artificial Intelligence top 2%
- Stochastic Gradient Optimization Techniques 8
- Anomaly Detection Techniques and Applications 5
- Machine Learning and Algorithms 4
- Privacy-Preserving Technologies in Data 4
-
- Advanced Neural Network Applications 9
-
- Interconnection Networks and Systems 5
Christopher De
46 papers receiving 959 citations
Peers
Comparison fields: 5 of 107
- Hardware and Architecture 125
- Artificial Intelligence 507
- Computer Vision and Pattern Recognition 203
- Computational Mathematics 5
- Health Informatics 11
Countries citing papers authored by Christopher De
This map shows the geographic impact of Christopher De'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 Christopher De with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christopher De more than expected).
Fields of papers citing papers by Christopher De
This network shows the impact of papers produced by Christopher De. 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 Christopher De. The network helps show where Christopher De may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Christopher De, 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 | Optimal Complexity in Decentralized Training | 2021 | 3 |
| 2 | Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision | 2021 | 4 |
| 3 | Moniqua: Modulo Quantized Communication in Decentralized SGD | 2020 | 12 |
| 4 | Neural Manifold Ordinary Differential Equations | 2020 | 1 |
| 5 | 2020 | 1 | |
| 6 | 2019 | 9 | |
| 7 | Improving Neural Network Quantization without Retraining using Outlier Channel Splitting | 2019 | 32 |
| 8 | Dimension-Free Bounds for Low-Precision Training | 2019 | 3 |
| 9 | Channel Gating Neural Networks | 2019 | 25 |
| 10 | Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models | 2019 | 3 |
| 11 | Improving Neural Network Quantization using Outlier Channel Splitting | 2019 | 2 |
| 12 | 2019 | 4 | |
| 13 | 2019 | 16 | |
| 14 | 2018 | 167 | |
| 15 | Accelerated Stochastic Power Iteration | 2018 | 10 |
| 16 | 2017 | 54 | |
| 17 | 2017 | 11 | |
| 18 | Socratic Learning: Correcting Misspecified Generative Models using Discriminative Models | 2016 | 3 |
| 19 | 2016 | 33 | |
| 20 | Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling. | 2016 | 6 |
About Christopher De
Christopher De is a scholar working on Hardware and Architecture, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 46 papers that have together received 991 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (9 papers), Parallel Computing and Optimization Techniques (9 papers), Stochastic Gradient Optimization Techniques (8 papers), Anomaly Detection Techniques and Applications (5 papers), Interconnection Networks and Systems (5 papers), Machine Learning and Algorithms (4 papers), Embedded Systems Design Techniques (4 papers) and Privacy-Preserving Technologies in Data (4 papers). The work is most often cited by research in Hardware and Architecture (125 citations), Artificial Intelligence (507 citations) and Computer Vision and Pattern Recognition (203 citations). Christopher De has collaborated with scholars based in United States, Israel and Canada. Frequent co-authors include Christopher Ré, Sen Wu, Robert F. Shepherd, Ilse M. Van Meerbeek, Jaeho Shin, Kunle Olukotun, Ce Zhang, Feiran Wang, Alex Ratner and Cristina Re. Their work appears in journals such as Proceedings of the VLDB Endowment, Communications of the ACM, ACM SIGPLAN Notices, Science Robotics and ACM SIGMOD Record.
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.