Roy H. Campbell

Computer Vision and Pattern Recognition · 2000–2019

A researcher in the Science corpus: 14 papers; trusts 125 researchers and is trusted by 89 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 84 citations into it; #3,459 of 21,575 by trust here, #3,982 by citations
• Artificial Intelligence — 82 citations into it
• Reinforcement Learning — 50 citations into it
• Computer Networks and Communications — 29 citations into it; #3,370 of 9,886 by trust here, #2,196 by citations
• Generative Models — 23 citations into it; #795 of 1,956 by trust here, #562 by citations
• Control and Systems Engineering — 16 citations into it
• Information Systems — 16 citations into it; #7,439 of 8,771 by trust here, #6,195 by citations
• Optimization & Training — 16 citations into it

Whom Roy H. Campbell trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Anind K. Dey
6. Kaiming He
7. Yann LeCun
8. Ilya Sutskever
9. J. Urgen Schmidhuber
10. Alex Graves

Who trusts Roy H. Campbell overall

1. Charith Perera
2. Danijar Hafner
3. Suraj Nair
4. Tao Gu
5. Chelsea Finn
6. Sergey Levine
7. Anand Ranganathan
8. Mohammad Babaeizadeh
9. Manoj Kumar
10. Chen Sun

Papers from Roy H. Campbell's perspective

Ordered by how strongly the researchers Roy H. Campbell trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Stochastic Variational Video Prediction (2017) — 585 citations
• Model-Based Reinforcement Learning for Atari (2019) — 995 citations
• Fast Generation for Convolutional Autoregressive Models (2017) — 67 citations
• Big Data: Astronomical or Genomical? (2015) — 1,313 citations
• Monitoring, Security, and Dynamic Configuration with the dynamicTAO Reflective ORB (2000) — 404 citations
• TicTac: Accelerating Distributed Deep Learning with Communication Scheduling (2018) — 214 citations
• Consistent and Durable Data Structures for Non-Volatile Byte-Addressable Memory (2011) — 411 citations
• ARIA: automatic resource inference and allocation for mapreduce environments (2011) — 504 citations
• Reasoning about Uncertain Contexts in Pervasive Computing Environments (2004) — 365 citations
• A Middleware for Context-Aware Agents in Ubiquitous Computing Environments (2003) — 433 citations
• An infrastructure for context-awareness based on first order logic (2003) — 386 citations
• Gaia: A Middleware Infrastructure to Enable Active Spaces1 (2002) — 555 citations
• The case for reflective middleware (2002) — 406 citations
• A Middleware Infrastructure for Active Spaces (2002) — 1,163 citations