Siva Theja Maguluri

Computer Networks and Communications · 2012–2018

A researcher in the Science corpus: 3 papers; trusts 46 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Networks and Communications — 50 citations into it; #3,353 of 9,886 by trust here, #5,961 by citations
• Optimization & Training — 29 citations into it; #2,052 of 2,601 by trust here, #2,259 by citations
• Artificial Intelligence — 16 citations into it; #16,795 of 35,345 by trust here, #26,682 by citations
• Computational Mechanics — 15 citations into it
• Information Systems — 8 citations into it; #6,590 of 8,771 by trust here, #8,258 by citations
• Mathematical Optimization — 6 citations into it
• Algorithms & Complexity — 5 citations into it; #408 of 516 by trust here, #380 by citations
• Electrical and Electronic Engineering — 5 citations into it

Whom Siva Theja Maguluri trusts overall

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

1. Angelia Nedić
2. Yurii E. Nesterov
3. Shai Shalev-Shwartz
4. David Ruppert
5. Michael M. Mitzenmacher
6. Asuman E. Ozdaglar
7. Wei Shi
8. John C. Duchi
9. Ermin Wei
10. Sean P. Meyn

Who trusts Siva Theja Maguluri overall

1. Liang Tong
2. S. Chraibi
3. Amirhossein Reisizadeh
4. Yong Li
5. Ahmed Khaled

Papers from Siva Theja Maguluri's perspective

Ordered by how strongly the researchers Siva Theja Maguluri 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
9. Distributed Subgradient Methods for Multi-Agent Optimization
10. Distributed Asynchronous Deterministic and Stochastic Gradient Optimization Algorithms

Their papers

• Fast Convergence Rates of Distributed Subgradient Methods With Adaptive Quantization (2018) — 75 citations
• Accelerating the Convergence Rates of Distributed Subgradient Methods with Adaptive Quantization (2018) — 15 citations
• Stochastic models of load balancing and scheduling in cloud computing clusters (2012) — 379 citations