Yuval Kluger

Molecular Biology · 2003–2020

A researcher in the Science corpus: 18 papers; trusts 113 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 105 citations into it; #7,813 of 17,773 by trust here, #3,659 by citations
• Artificial Intelligence — 71 citations into it; #29,305 of 35,345 by trust here, #13,962 by citations
• Computer Vision and Pattern Recognition — 44 citations into it
• Representation & Self-Supervised Learning — 36 citations into it; #1,973 of 3,145 by trust here, #2,012 by citations
• Optimization & Training — 25 citations into it
• Statistics and Probability — 25 citations into it
• Computational Mechanics — 17 citations into it
• Computational Theory and Mathematics — 17 citations into it

Whom Yuval Kluger trusts overall

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

1. L. V. D. van der Maaten
2. Geoffrey E. Hinton
3. Inderjit S. Dhillon
4. Jianbo Shi
5. Yoshua Bengio
6. Trevor J. Hastie
7. M. Kerr
8. Yurii E. Nesterov
9. Alex Krizhevsky
10. Jitendra Malik

Who trusts Yuval Kluger overall

1. Yevgeny Seldin
2. David Alvarez-Melis
3. Kai Zhang
4. Maurizio Filippone
5. Hyuk Cho
6. Andrey A. Shabalin
7. George C. Linderman
8. Randal S. Olson
9. Jacob Steinhardt
10. Tommi S. Jaakkola

Papers from Yuval Kluger's perspective

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

Their papers

• SpectralNet: Spectral Clustering using Deep Neural Networks (2018) — 330 citations
• Ranking and combining multiple predictors without labeled data (2013) — 150 citations
• A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data (2003) — 1,283 citations
• Spectral biclustering of microarray data: coclustering genes and conditions. (2003) — 789 citations
• Removal of batch effects using distribution‐matching residual networks (2016) — 177 citations
• PCGF Homologs, CBX Proteins, and RYBP Define Functionally Distinct PRC1 Family Complexes (2012) — 857 citations
• Fast Interpolation-based t-SNE for Improved Visualization of Single-Cell RNA-Seq Data (2017) — 507 citations
• DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network (2016) — 1,801 citations
• Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations (2019) — 46 citations
• Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science (2017) — 17 citations
• Data-Driven Tree Transforms and Metrics (2017) — 23 citations
• Correction to: Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations (2020)
• Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding (2017) — 102 citations
• Algorithm 971: An Implementation of a Randomized Algorithm for Principal Component Analysis (2017) — 69 citations
• m6A mRNA methylation controls T cell homeostasis by targeting IL-7/STAT5/SOCS pathway (2017) — 765 citations
• Deep Survival : The Deep Cox Proportional Hazards Network (2016)
• Estimating the accuracies of multiple classifiers without labeled data (2014) — 74 citations
• XBP1 controls diverse cell type- and condition-specific transcriptional regulatory networks. (2007) — 842 citations