Martín Arjovsky

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 9 papers; trusts 242 researchers and is trusted by 4,262 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 138 citations into it; #350 of 35,345 by trust here, #447 by citations
• Computer Vision and Pattern Recognition — 107 citations into it; #528 of 21,575 by trust here, #437 by citations
• Generative Models — 79 citations into it; #28 of 1,956 by trust here, #32 by citations
• Optimization & Training — 33 citations into it; #228 of 2,601 by trust here, #108 by citations
• Reinforcement Learning — 33 citations into it
• Probabilistic & Bayesian Methods — 26 citations into it; #590 of 1,525 by trust here, #110 by citations
• Statistics and Probability — 21 citations into it; #10,532 of 12,932 by trust here, #10,275 by citations
• Model Architectures — 17 citations into it; #460 of 2,278 by trust here, #776 by citations

Whom Martín Arjovsky trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yann LeCun
5. Alex Krizhevsky
6. Ilya Sutskever
7. Alex Graves
8. L. Eon Bottou
9. Sepp Hochreiter
10. Radford M. Neal

Who trusts Martín Arjovsky overall

1. Fei Ye
2. Alexia Jolicoeur-Martineau
3. Yoshua Bengio
4. Ngoc-Trung Tran
5. Garrett Wilson
6. Dacheng Tao
7. Sergey Levine
8. Chunyuan Li
9. Zhe Gan
10. Gauthier Gidel

Papers from Martín Arjovsky's perspective

Ordered by how strongly the researchers Martín Arjovsky 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Improved Training of Wasserstein GANs (2017) — 11,031 citations
• Adversarially Learned Inference (2016) — 1,346 citations
• Towards Principled Methods for Training Generative Adversarial Networks (2017) — 2,278 citations
• Invariant Risk Minimization (2019) — 2,885 citations
• Unitary Evolution Recurrent Neural Networks (2015) — 909 citations
• Never Give Up: Learning Directed Exploration Strategies (2020) — 360 citations
• Geometrical Insights for Implicit Generative Modeling (2017) — 52 citations
• Out of Distribution Generalization in Machine Learning (2021) — 116 citations
• Wasserstein Generative Adversarial Networks (2017) — 9,668 citations