Krishnateja Killamsetty

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 5 papers; trusts 81 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 79 citations into it
• Computer Vision and Pattern Recognition — 57 citations into it
• Optimization & Training — 33 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Computational Mechanics — 12 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Statistics and Probability — 7 citations into it

Whom Krishnateja Killamsetty trusts overall

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

1. Alex Krizhevsky
2. Baharan Mirzasoleiman
3. Yann LeCun
4. Kaiming He
5. Diederik P. Kingma
6. Geoffrey E. Hinton
7. Trevor Campbell
8. Yoshua Bengio
9. Kenneth L. Clarkson
10. Sariel Har-Peled

Who trusts Krishnateja Killamsetty overall

1. Sachin Mehta
2. Sang Michael Xie
3. Rishabh K. Iyer
4. Farzad Abdolhosseini
5. Xujiang Zhao
6. Rishabh Tiwari

Papers from Krishnateja Killamsetty's perspective

Ordered by how strongly the researchers Krishnateja Killamsetty 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. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. 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
• GCR: Gradient Coreset based Replay Buffer Selection for Continual Learning (2021) — 183 citations
• RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning (2021) — 112 citations
• GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning (2020) — 324 citations