Christopher De

3.6k citations
49 papers · 1.1k · h-index 18

Impact in

Papers in

Christopher De

46 papers receiving 1.0k citations

Peers

Christopher De
Comparison fields: 5 of 104
  • Hardware and Architecture 129
  • Artificial Intelligence 555
  • Computer Vision and Pattern Recognition 217
  • Management Science and Operations Research 109
  • Health Informatics 11
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Citations per field
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Citations per year

Countries citing papers authored by Christopher De

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

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.

Border = papers with Christopher De Line = papers co-authored together Christopher De links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 49 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018170
2 2015162
3
Data Programming: Creating Large Training Sets, Quickly.
201656
4 201756
5
DeepDive: Declarative Knowledge Base Construction.
201653
6 201651
7 201648
8 201745
9 201636
10
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
201934
11 201934
12 201933
13 201630
14
Channel Gating Neural Networks
201927
15
Representation Tradeoffs for Hyperbolic Embeddings.
201822
16
A Kernel Theory of Modern Data Augmentation.
201919
17 201918
18 201718
19 201914
20
Moniqua: Modulo Quantized Communication in Decentralized SGD
202013

About Christopher De

Christopher De is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Hardware and Architecture and Information Systems, having authored 49 papers that have together received 1.1k 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), Data Quality and Management (4 papers), Privacy-Preserving Technologies in Data (4 papers) and Embedded Systems Design Techniques (4 papers). The work is most often cited by research in Hardware and Architecture (129 citations), Artificial Intelligence (555 citations), Computer Vision and Pattern Recognition (217 citations), Management Science and Operations Research (109 citations) and Health Informatics (11 citations). Christopher De has collaborated with scholars based in United States, China and Israel. Frequent co-authors include Christopher Ré, Sen Wu, Ilse M. Van Meerbeek, Robert F. Shepherd, Jaeho Shin, Kunle Olukotun, Ce Zhang, Feiran Wang, Alex Ratner and Zhiru Zhang. Their work appears in journals such as ACM SIGPLAN Notices, The VLDB Journal, Proceedings of the VLDB Endowment, ACM SIGMOD Record and Communications of the ACM.

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.

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