Avanti Shrikumar

Molecular Biology · 2012–2021

A researcher in the Science corpus: 9 papers; trusts 137 researchers and is trusted by 181 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 343 citations into it; #14,407 of 17,773 by trust here, #3,963 by citations
• Artificial Intelligence — 221 citations into it; #1,560 of 35,345 by trust here, #2,033 by citations
• Computer Vision and Pattern Recognition — 106 citations into it; #2,892 of 21,575 by trust here, #2,768 by citations
• Safety, Robustness & Interpretability — 55 citations into it; #79 of 2,251 by trust here, #202 by citations
• Biophysics — 49 citations into it
• Genetics — 42 citations into it
• Materials Chemistry — 34 citations into it
• Computational Biology & Bioinformatics — 30 citations into it

Whom Avanti Shrikumar trusts overall

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

1. Diederik P. Kingma
2. Yann LeCun
3. Matthew D. Zeiler
4. Karen Simonyan
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Sepp Hochreiter
8. Alex Krizhevsky
9. Sergey Ioffe
10. Jimmy Ba

Who trusts Avanti Shrikumar overall

1. Marco Ancona
2. Scott M. Lundberg
3. Wojciech Samek
4. Mukund Sundararajan
5. Reza Shokri
6. Su-In Lee
7. Ankur Taly
8. Cengiz Oztireli
9. Grgoire Montavon
10. Enea Ceolini

Papers from Avanti Shrikumar's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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 Large Scale Visual Recognition Challenge
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning Important Features Through Propagating Activation Differences (2017) — 4,716 citations
• Opportunities and obstacles for deep learning in biology and medicine (2017) — 2,030 citations
• Not Just a Black Box: Learning Important Features Through Propagating Activation Differences (2016) — 887 citations
• Reverse-complement parameter sharing improves deep learning models for genomics (2017) — 70 citations
• Dynamic and Coordinated Epigenetic Regulation of Developmental Transitions in the Cardiac Lineage (2012) — 605 citations
• Base-resolution models of transcription factor binding reveal soft motif syntax (2021) — 562 citations
• Gkmexplain: Fast and Accurate Interpretation of Nonlinear Gapped k-mer SVMs Using Integrated Gradients (2018) — 9 citations
• Separable Fully Connected Layers Improve Deep Learning Models For Genomics (2017) — 12 citations
• Not Just A Black Box : Interpretable Deep Learning by Propagating Activation Differences (2016)