Alexander Ororbia

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 13 papers; trusts 537 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 172 citations into it; #12,612 of 35,345 by trust here, #11,192 by citations
• Computer Vision and Pattern Recognition — 124 citations into it; #8,416 of 21,575 by trust here, #10,677 by citations
• Safety, Robustness & Interpretability — 73 citations into it; #1,191 of 2,251 by trust here, #1,312 by citations
• Representation & Self-Supervised Learning — 43 citations into it
• Model Architectures — 31 citations into it
• Optimization & Training — 30 citations into it; #1,668 of 2,601 by trust here, #1,942 by citations
• Signal Processing — 30 citations into it
• Natural Language Processing — 25 citations into it

Whom Alexander Ororbia trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Ruslan Salakhutdinov
5. Diederik P. Kingma
6. Ian J. Goodfellow
7. Radford M. Neal
8. Yann LeCun
9. Alex Graves
10. Ilya Sutskever

Who trusts Alexander Ororbia overall

1. Qinglong Wang
2. Andrés Camero
3. Wenbo Guo
4. Enrique Alba
5. Decebal Constantin Mocanu
6. Yao-Yuan Yang
7. Chris Alberti
8. Naveed Akhtar
9. Xinyu Xing
10. Chaopeng Shen

Papers from Alexander Ororbia's perspective

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

Their papers

• Learning a Hierarchical Latent-Variable Model of 3D Shapes (2017) — 66 citations
• Unifying Adversarial Training Algorithms with Data Gradient Regularization (2017) — 26 citations
• Adversary Resistant Deep Neural Networks with an Application to Malware Detection (2016) — 181 citations
• Like a Baby: Visually Situated Neural Language Acquisition (2018) — 19 citations
• Online Semi-Supervised Learning with Deep Hybrid Boltzmann Machines and Denoising Autoencoders (2015) — 30 citations
• Investigating recurrent neural network memory structures using neuro-evolution (2019) — 62 citations
• An Empirical Evaluation of Rule Extraction from Recurrent Neural Networks (2017) — 66 citations
• Using Non-invertible Data Transformations to Build Adversary-Resistant Deep Neural Networks (2016) — 10 citations
• Privacy Protection for Natural Language Records: Neural Generative Models for Releasing Synthetic Twitter Data (2016) — 6 citations
• Learning Adversary-Resistant Deep Neural Networks (2016) — 4 citations
• Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks (2016) — 10 citations
• Learning Adversary-Resistant Deep Neural Networks (2016) — 44 citations
• Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization (2016) — 24 citations