Alex Beutel

Artificial Intelligence · 2013–2020

A researcher in the Science corpus: 15 papers; trusts 131 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 321 citations into it; #10,913 of 35,345 by trust here, #3,246 by citations
• Safety, Robustness & Interpretability — 125 citations into it; #723 of 2,251 by trust here, #344 by citations
• Computer Vision and Pattern Recognition — 90 citations into it
• Information Systems — 82 citations into it; #2,672 of 8,771 by trust here, #937 by citations
• Natural Language Processing — 74 citations into it; #3,969 of 5,688 by trust here, #2,106 by citations
• Safety Research — 62 citations into it; #813 of 5,829 by trust here, #256 by citations
• Representation & Self-Supervised Learning — 49 citations into it; #1,653 of 3,145 by trust here, #1,266 by citations
• Management Science and Operations Research — 45 citations into it; #7,490 of 11,946 by trust here, #1,309 by citations

Whom Alex Beutel trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yehuda Koren
5. Alex Graves
6. Ilya Sutskever
7. Alex Krizhevsky
8. Ruslan Salakhutdinov
9. L. Eon Bottou
10. Yann LeCun

Who trusts Alex Beutel overall

1. Srijan Kumar
2. Flavien Prost
3. Dana Pessach
4. Luca Oneto
5. Emilio Ferrara
6. James R. Foulds
7. Yanai Elazar
8. Wang-Cheng Kang
9. Xiangnan He
10. Enyan Dai

Papers from Alex Beutel's perspective

Ordered by how strongly the researchers Alex Beutel 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Counterfactual Fairness in Text Classification through Robustness (2018) — 294 citations
• Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020) — 897 citations
• Top-K Off-Policy Correction for a REINFORCE Recommender System (2018) — 558 citations
• Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations (2017) — 473 citations
• Latent Cross: Making Use of Context in Recurrent Recommender Systems (2018) — 371 citations
• Fairness without Demographics through Adversarially Reweighted Learning (2020) — 399 citations
• The Case for Learned Index Structures (2017) — 1,261 citations
• Recurrent Recommender Networks (2017) — 758 citations
• Fairness in Recommendation Ranking through Pairwise Comparisons (2019) — 457 citations
• FRAUDAR: Bounding Graph Fraud in the Face of Camouflage (2016) — 393 citations
• Measuring and Reducing Gendered Correlations in Pre-trained Models (2020) — 313 citations
• Toward a better trade-off between performance and fairness with kernel-based distribution matching (2019) — 49 citations
• Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements (2019) — 176 citations
• FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop (2014) — 122 citations
• CopyCatch: stopping group attacks by spotting lockstep behavior in social networks (2013) — 386 citations