Alex S. Fukunaga
Artificial Intelligence · 2013–2017
A researcher in the Science corpus: 4 papers; trusts 78 researchers and is trusted by 31 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 40 citations into it; #5,616 of 35,345 by trust here, #13,760 by citations
- Computational Theory and Mathematics — 18 citations into it; #1,215 of 12,182 by trust here, #3,218 by citations
- Computer Vision and Pattern Recognition — 14 citations into it; #14,024 of 21,575 by trust here, #18,782 by citations
- Generative Models — 7 citations into it
- Optimization & Training — 6 citations into it
- Reinforcement Learning — 6 citations into it; #1,559 of 2,242 by trust here, #2,036 by citations
- Representation & Self-Supervised Learning — 6 citations into it
- Evaluation & Benchmarks — 5 citations into it
Whom Alex S. Fukunaga trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ponnuthurai Nagaratnam Suganthan
- Diederik P. Kingma
- Janez Brest
- Geoffrey E. Hinton
- Jingqiao Zhang
- Chris J. Maddison
- A. K. Qin
- Yong Wang
- Sergey Ioffe
- Alex Krizhevsky
Who trusts Alex S. Fukunaga overall
- Swagatam Das
- Noor H. Awad
- Ali Wagdy Mohamed
- Ponnuthurai Nagaratnam Suganthan
- Ryoji Tanabe
- Blai Bonet
- Guohua Wu
- Janez Brest
- Sankha Subhra Mullick
- Mostafa Z. Ali
Papers from Alex S. Fukunaga's perspective
Ordered by how strongly the researchers Alex S. Fukunaga 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
- Going deeper with convolutions
Their papers
- Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary (2017) — 191 citations
- Learning to Prune Dominated Action Sequences in Online Black-Box Planning (2016) — 18 citations
- Success-history based parameter adaptation for Differential Evolution (2013) — 1,310 citations
- Improving the search performance of SHADE using linear population size reduction (2014) — 1,554 citations