D. Adjeroh

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 672 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 138 citations into it; #7,929 of 35,345 by trust here, #7,010 by citations
• Computer Vision and Pattern Recognition — 135 citations into it; #4,674 of 21,575 by trust here, #4,926 by citations
• Generative Models — 44 citations into it; #618 of 1,956 by trust here, #1,200 by citations
• Representation & Self-Supervised Learning — 44 citations into it; #1,364 of 3,145 by trust here, #1,246 by citations
• Signal Processing — 41 citations into it; #6,665 of 7,572 by trust here, #5,570 by citations
• Computer Networks and Communications — 26 citations into it; #8,475 of 9,886 by trust here, #7,140 by citations
• Evaluation & Benchmarks — 20 citations into it
• Information Systems — 17 citations into it

Whom D. Adjeroh trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yann LeCun
6. Kaiming He
7. Karen Simonyan
8. Ilya Sutskever
9. Andrew Zisserman
10. Ian J. Goodfellow

Who trusts D. Adjeroh overall

1. Yuval Alaluf
2. Kaiyang Zhou
3. Saeid Motiian
4. Yotam Nitzan
5. Zongze Wu
6. Or Patashnik
7. Yang Zhao
8. Zhisheng Xiao
9. Haohan Wang
10. Daniel Cohen-Or

Papers from D. Adjeroh's perspective

Ordered by how strongly the researchers D. Adjeroh 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Unified Deep Supervised Domain Adaptation and Generalization (2017) — 889 citations
• Adversarial Latent Autoencoders (2020) — 289 citations
• Generative Probabilistic Novelty Detection with Adversarial Autoencoders (2018) — 349 citations
• Information Bottleneck Learning Using Privileged Information for Visual Recognition (2016) — 54 citations
• A Survey on Malware Detection Using Data Mining Techniques (2017) — 612 citations