Balasubramaniam Srinivasan

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 99 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #12,359 of 35,345 by trust here, #14,438 by citations
• Graph Learning — 66 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it
• Model Architectures — 23 citations into it
• Molecular Biology — 22 citations into it; #14,501 of 17,773 by trust here, #12,465 by citations
• Statistical and Nonlinear Physics — 20 citations into it
• Computational Theory and Mathematics — 18 citations into it

Whom Balasubramaniam Srinivasan trusts overall

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

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

Who trusts Balasubramaniam Srinivasan overall

1. Vijay Prakash Dwivedi
2. William L. Hamilton
3. Jiaxuan You
4. Zhengdao Chen
5. Anh Tuan Luu
6. Thomas Laurent
7. Pan Li
8. Ines Chami
9. Yanbang Wang
10. Jure Leskovec

Papers from Balasubramaniam Srinivasan's perspective

Ordered by how strongly the researchers Balasubramaniam Srinivasan 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Relational Pooling for Graph Representations (2019) — 285 citations
• Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs (2018) — 214 citations
• On the Equivalence between Positional Node Embeddings and Structural Graph Representations (2019) — 143 citations