Daniel Graves

Artificial Intelligence · 2010–2014

A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 16 citations into it; #24,851 of 35,345 by trust here, #33,294 by citations
• Reinforcement Learning — 10 citations into it
• Computational Theory and Mathematics — 8 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it; #15,325 of 21,575 by trust here, #21,424 by citations

Whom Daniel Graves trusts overall

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

1. Francesco Camastra
2. Bernhard Schölkopf
3. Inderjit S. Dhillon
4. Alex Smola
5. Alessandro Verri
6. Ralf Herbrich
7. Yuqiang Guan
8. K. Müller
9. Richard S. Sutton
10. Brian Kulis

Who trusts Daniel Graves overall

1. Hsin-Chien Huang
2. Yung-Yu Chuang
3. Chu-Song Chen

Papers from Daniel Graves's perspective

Ordered by how strongly the researchers Daniel Graves 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. Reinforcement Learning: An Introduction
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. GENERATIVE ADVERSARIAL NETS

Their papers

• A Clustering-Based Graph Laplacian Framework for Value Function Approximation in Reinforcement Learning (2014) — 38 citations
• Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study (2010) — 368 citations