Aditya G. Parameswaran
Artificial Intelligence · 2012–2017
A researcher in the Science corpus: 13 papers; trusts 85 researchers and is trusted by 15 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 92 citations into it; #17,865 of 35,345 by trust here, #11,624 by citations
- Computer Science Applications — 66 citations into it; #658 of 1,855 by trust here, #465 by citations
- Management Science and Operations Research — 49 citations into it; #6,385 of 11,946 by trust here, #2,310 by citations
- Evaluation & Benchmarks — 26 citations into it; #2,048 of 3,147 by trust here, #1,990 by citations
- Information Systems — 26 citations into it; #5,865 of 8,771 by trust here, #2,821 by citations
- Computer Networks and Communications — 25 citations into it; #6,723 of 9,886 by trust here, #4,950 by citations
- Computer Vision and Pattern Recognition — 19 citations into it; #16,268 of 21,575 by trust here, #15,955 by citations
- Signal Processing — 19 citations into it; #5,147 of 7,572 by trust here, #3,309 by citations
Whom Aditya G. Parameswaran trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Adam Marcus
- Panagiotis G. Ipeirotis
- Luis von Ahn
- M. Franklin
- Muthu Dayalan
- Sumit Gulwani
- Larry A. Wasserman
- Tim Kraska
- Eugene Wu
- Aniket Kittur
Who trusts Aditya G. Parameswaran overall
- Adam Marcus
- Yuji Roh
- Hector Garcia-Molina
- Yael Amsterdamer
- Yongxin Tong
- Tova Milo
- Manas R. Joglekar
- Moria Bergman
- Yeounoh Chung
- Sanjay Krishnan
Papers from Aditya G. Parameswaran's perspective
Ordered by how strongly the researchers Aditya G. Parameswaran trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Et al
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Going deeper with convolutions
Their papers
- DataHub: Collaborative Data Science & Dataset Version Management at Scale (2014) — 191 citations
- CrowdScreen: algorithms for filtering data with humans (2012) — 250 citations
- Deco: A System for Declarative Crowdsourcing (2012) — 92 citations
- Navigating the Data Lake with DATAMARAN: Automatically Extracting Structure from Log Datasets (2017) — 56 citations
- Challenges in Data Crowdsourcing (2016) — 133 citations
- Effortless Data Exploration with zenvisage: An Expressive and Interactive Visual Analytics System (2016) — 164 citations
- SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics (2015) — 318 citations
- Collaborative Data Analytics with DataHub (2015) — 57 citations
- SLiMFast: Guaranteed Results for Data Fusion and Source Reliability (2015) — 63 citations
- Crowdsourced Data Management: Industry and Academic Perspectives (2015) — 87 citations
- Principles of Dataset Versioning: Exploring the Recreation/Storage Tradeoff (2015) — 86 citations
- Comprehensive and reliable crowd assessment algorithms (2014) — 53 citations
- Smart Drill-Down: A New Data Exploration Operator (2014) — 32 citations