David Alvarez-Melis

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 7 papers; trusts 302 researchers and is trusted by 92 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 86 citations into it; #2,669 of 35,345 by trust here, #4,627 by citations
• Natural Language Processing — 50 citations into it; #1,016 of 5,688 by trust here, #1,466 by citations
• Safety, Robustness & Interpretability — 38 citations into it; #175 of 2,251 by trust here, #427 by citations
• Computer Vision and Pattern Recognition — 32 citations into it; #10,349 of 21,575 by trust here, #15,529 by citations
• Representation & Self-Supervised Learning — 29 citations into it
• Model Architectures — 8 citations into it
• Biophysics — 7 citations into it
• Generative Models — 5 citations into it

Whom David Alvarez-Melis trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. Karen Simonyan
9. Tomas Mikolov
10. Danilo Jimenez Rezende

Who trusts David Alvarez-Melis overall

1. Giulia Vilone
2. Tommi S. Jaakkola
3. Meike Nauta
4. Matthew R. O'Shaughnessy
5. Gregory Plumb
6. Alicja Gosiewska
7. Longo Luca
8. Riccardo Guidotti
9. Shiyu Chang
10. Thomas McGrath

Papers from David Alvarez-Melis's perspective

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

Their papers

• A causal framework for explaining the predictions of black-box sequence-to-sequence models (2017) — 213 citations
• Towards Robust Interpretability with Self-Explaining Neural Networks (2018) — 1,140 citations
• On the Robustness of Interpretability Methods (2018) — 670 citations
• Word Embeddings as Metric Recovery in Semantic Spaces (2016) — 90 citations
• Gromov-Wasserstein Alignment of Word Embedding Spaces (2018) — 359 citations
• Probabilistic Bias Mitigation in Word Embeddings (2019) — 4 citations
• Tree-structured decoding with doubly-recurrent neural networks (2016) — 106 citations