Nino Antulov-Fantulin

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 83 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #28,113 of 35,345 by trust here, #29,412 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Model Architectures — 13 citations into it
• Signal Processing — 12 citations into it; #5,981 of 7,572 by trust here, #6,083 by citations
• Safety, Robustness & Interpretability — 10 citations into it
• Management Science and Operations Research — 8 citations into it; #9,324 of 11,946 by trust here, #8,906 by citations
• Natural Language Processing — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Nino Antulov-Fantulin trusts overall

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

1. Chris H. Q. Ding
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Michael I. Jordan
5. Yoshua Bengio
6. Daniel D. Lee
7. Ilya Sutskever
8. Andrew Y. Ng
9. Oriol Vinyals
10. Jerome H. Friedman

Who trusts Nino Antulov-Fantulin overall

1. K. J. Joseph

Papers from Nino Antulov-Fantulin's perspective

Ordered by how strongly the researchers Nino Antulov-Fantulin trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
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. Learning Multiple Layers of Features from Tiny Images
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Exploring Interpretable LSTM Neural Networks over Multi-Variable Data (2019) — 184 citations
• A Nonlinear Orthogonal Non-Negative Matrix Factorization Approach to Subspace Clustering (2017) — 68 citations