Konda Reddy Mopuri

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 555 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 108 citations into it; #7,211 of 35,345 by trust here, #9,170 by citations
• Computer Vision and Pattern Recognition — 84 citations into it; #14,070 of 21,575 by trust here, #17,258 by citations
• Safety, Robustness & Interpretability — 42 citations into it
• Optimization & Training — 22 citations into it
• Signal Processing — 18 citations into it; #2,414 of 7,572 by trust here, #3,673 by citations
• Model Architectures — 16 citations into it
• Efficiency & Compression — 14 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom Konda Reddy Mopuri trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Yann LeCun
6. Christian Szegedy
7. Kaiming He
8. Karen Simonyan
9. Jimmy Ba
10. Ilya Sutskever

Who trusts Konda Reddy Mopuri overall

1. Muzammal Naseer
2. Bo Zhao
3. Naveed Akhtar
4. Jamie Hayes
5. Salman Hameed Khan
6. Gaurav Kumar Nayak
7. Lingjuan Lyu
8. Hakan Bilen
9. Ajmal Saeed Mian
10. Fahad Shahbaz Khan

Papers from Konda Reddy Mopuri's perspective

Ordered by how strongly the researchers Konda Reddy Mopuri 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Dataset Condensation with Gradient Matching (2020) — 720 citations
• Zero-Shot Knowledge Distillation in Deep Networks (2019) — 267 citations
• iDLG: Improved Deep Leakage from Gradients (2020) — 844 citations
• Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation (2020) — 22 citations
• Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions (2018) — 94 citations
• Fast Feature Fool: A data independent approach to universal adversarial perturbations (2017) — 228 citations