Durga Sivasubramanian
Artificial Intelligence · 2020–2021
A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 27 citations into it
- Computer Vision and Pattern Recognition — 19 citations into it
- Optimization & Training — 17 citations into it
- Computational Mechanics — 8 citations into it
- Computational Theory and Mathematics — 6 citations into it
- Evaluation & Benchmarks — 5 citations into it
Whom Durga Sivasubramanian trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kenneth L. Clarkson
- Alex Krizhevsky
- Kaiming He
- Yann LeCun
- Sariel Har-Peled
- Trevor Campbell
- Baharan Mirzasoleiman
- Geoffrey E. Hinton
- X. Zhang
- Burr Settles
Who trusts Durga Sivasubramanian overall
Papers from Durga Sivasubramanian's perspective
Ordered by how strongly the researchers Durga Sivasubramanian trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Learning Multiple Layers of Features from Tiny Images
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training (2021) — 337 citations
- GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training (2021) — 81 citations
- GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning (2020) — 324 citations