Murali Annavaram

Artificial Intelligence · 2006–2020

A researcher in the Science corpus: 12 papers; trusts 483 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 312 citations into it; #20,609 of 35,345 by trust here, #8,210 by citations
• Computer Vision and Pattern Recognition — 121 citations into it; #20,622 of 21,575 by trust here, #11,721 by citations
• Federated & Distributed Learning — 91 citations into it; #210 of 319 by trust here, #117 by citations
• Efficiency & Compression — 76 citations into it
• Optimization & Training — 75 citations into it
• Privacy & Security — 42 citations into it
• Electrical and Electronic Engineering — 36 citations into it; #15,061 of 16,539 by trust here, #7,892 by citations
• Computer Science Applications — 32 citations into it; #1,729 of 1,855 by trust here, #1,051 by citations

Whom Murali Annavaram trusts overall

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

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

Who trusts Murali Annavaram overall

1. Chaoyang He
2. Jian-Qiang Lu
3. Songze Li
4. Qiong Wu
5. Jianping Gou
6. Geoffrey F. Miller
7. Jinhyun So
8. Xinle Liang
9. Fatemehsadat Mirshghallah
10. Yue Tan

Papers from Murali Annavaram's perspective

Ordered by how strongly the researchers Murali Annavaram 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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

• A framework of energy efficient mobile sensing for automatic user state recognition (2009) — 523 citations
• Die Stacking (3D) Microarchitecture (2006) — 642 citations
• Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments (2019) — 58 citations
• FedNAS: Federated Deep Learning via Neural Architecture Search (2020) — 98 citations
• Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search. (2020) — 30 citations
• DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural Networks (2020) — 27 citations
• Group Knowledge Transfer: Collaborative Training of Large CNNs on the Edge (2020) — 24 citations
• FedML: A Research Library and Benchmark for Federated Machine Learning (2020) — 716 citations
• Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge (2020) — 580 citations
• GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training (2018) — 70 citations
• Virtual trip lines for distributed privacy-preserving traffic monitoring (2008) — 420 citations
• This paper is included in the Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation.