David Hallac

Artificial Intelligence · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 20 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 18 citations into it; #31,516 of 35,345 by trust here, #22,463 by citations
• Graph Learning — 18 citations into it
• Statistical and Nonlinear Physics — 11 citations into it; #8,786 of 10,127 by trust here, #5,625 by citations
• Computational Mechanics — 9 citations into it; #5,990 of 10,001 by trust here, #8,566 by citations
• Computational Theory and Mathematics — 9 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Statistics and Probability — 9 citations into it; #8,056 of 12,932 by trust here, #10,275 by citations
• Molecular Biology — 8 citations into it

Whom David Hallac trusts overall

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

1. Neal Parikh
2. Stephen P. Boyd
3. Eamonn J. Keogh
4. Eric R. Ziegel
5. B. Muthén
6. Robert Tibshirani
7. Marco Cuturi
8. Peter J. Rousseeuw
9. U. Brandes
10. Jessica Lin

Who trusts David Hallac overall

1. Liang Wu
2. Xiao Huang
3. Huan Liu
4. Marinka Zitnik
5. Jundong Li
6. Jure Leskovec

Papers from David Hallac's perspective

Ordered by how strongly the researchers David Hallac trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Regression Shrinkage and Selection via the Lasso
7. Long Short-Term Memory
8. The Elements of Statistical Learning
9. Learning Multiple Layers of Features from Tiny Images
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Learning Structural Node Embeddings via Diffusion Wavelets (2017) — 444 citations
• Network Lasso: Clustering and Optimization in Large Graphs (2015) — 314 citations
• Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data (2017) — 309 citations