Christian Borgs

Artificial Intelligence · 2003–2023

A researcher in the Science corpus: 15 papers; trusts 28 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 84 citations into it; #16,569 of 35,345 by trust here, #3,467 by citations
• Statistical and Nonlinear Physics — 49 citations into it; #4,675 of 10,127 by trust here, #581 by citations
• Computational Theory and Mathematics — 48 citations into it; #1,609 of 12,182 by trust here, #1,047 by citations
• Computer Vision and Pattern Recognition — 22 citations into it; #15,433 of 21,575 by trust here, #4,402 by citations
• Computer Networks and Communications — 20 citations into it; #6,621 of 9,886 by trust here, #8,191 by citations
• Discrete Mathematics and Combinatorics — 18 citations into it; #199 of 2,017 by trust here, #62 by citations
• Geometry and Topology — 16 citations into it; #1,121 of 7,422 by trust here, #531 by citations
• Statistics and Probability — 16 citations into it

Whom Christian Borgs trusts overall

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

1. L. Lov'asz
2. Béla Bollobás
3. Jennifer Tour Chayes
4. Itai Benjamini
5. Jure Leskovec
6. Oliver Riordan
7. JA. Sanchez
8. Balázs Szegedy
9. Oded Schramm
10. Yoshua Bengio

Who trusts Christian Borgs overall

1. Sourav Chatterjee
2. Béla Bollobás
3. Jennifer Tour Chayes
4. Roberto Imbuzeiro Oliveira
5. L. Lov'asz
6. Charles Bordenave
7. Persi Diaconis
8. Youze Tang
9. Roman Glebov
10. Oliver Riordan

Papers from Christian Borgs's perspective

Ordered by how strongly the researchers Christian Borgs 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Entropy-SGD: biasing gradient descent into wide valleys (2016) — 882 citations
• Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting (2019) — 587 citations
• Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes (2016) — 183 citations
• What’s in a Name? Reducing Bias in Bios without Access to Protected Attributes (2019) — 86 citations
• Convergent Sequences of Dense Graphs I: Subgraph Frequencies, Metric Properties and Testing (2007) — 699 citations
• Counting Graph Homomorphisms (2006) — 213 citations
• Moments of Two-Variable Functions and the Uniqueness of Graph Limits (2008) — 160 citations
• Convergent Sequences of Dense Graphs II. Multiway Cuts and Statistical Physics (2012) — 355 citations
• Random subgraphs of finite graphs : II. The lace expansion and the triangle condition (2004) — 107 citations
• Percolation on dense graph sequences. (2007) — 93 citations
• ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis (2023) — 429 citations
• An $L^{p}$ theory of sparse graph convergence II: LD convergence, quotients and right convergence (2014) — 98 citations
• Asymptotic behavior and distributional limits of preferential attachment graphs (2014) — 111 citations
• Maximizing Social Influence in Nearly Optimal Time (2012) — 939 citations
• Directed scale-free graphs (2003) — 440 citations