Amrit Singh Bedi

Computer Networks and Communications · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 81 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Networks and Communications — 91 citations into it; #3,591 of 9,886 by trust here, #4,757 by citations
• Artificial Intelligence — 90 citations into it; #28,169 of 35,345 by trust here, #27,551 by citations
• Optimization & Training — 73 citations into it; #2,069 of 2,601 by trust here, #2,162 by citations
• Computational Mechanics — 61 citations into it; #3,765 of 10,001 by trust here, #4,937 by citations
• Electrical and Electronic Engineering — 31 citations into it; #3,647 of 16,539 by trust here, #9,402 by citations
• Management Science and Operations Research — 27 citations into it; #9,389 of 11,946 by trust here, #10,392 by citations
• ML Systems & Hardware — 21 citations into it; #374 of 575 by trust here, #493 by citations
• Mathematical Optimization — 14 citations into it

Whom Amrit Singh Bedi trusts overall

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

1. Angelia Nedić
2. Asuman E. Ozdaglar
3. Alejandro Ribeiro
4. Francis R. Bach
5. Dimitri P. Bertsekas
6. L. Eon Bottou
7. Alec Koppel
8. Martin A. Zinkevich
9. Mark W. Schmidt
10. Ermin Wei

Who trusts Amrit Singh Bedi overall

1. Anis Elgabli
2. Jihong Park
3. Zeeshan Akhtar
4. Liangzu Peng
5. Medhi Bennis
6. Yawei Zhao
7. Ketan Rajawat
8. Paris V. Giampouras
9. Vaneet Aggarwal

Papers from Amrit Singh Bedi's perspective

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

Their papers

• GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning (2019) — 91 citations
• Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning (2019) — 74 citations
• Asynchronous Saddle Point Algorithm for Stochastic Optimization in Heterogeneous Networks (2019) — 31 citations
• Asynchronous Incremental Stochastic Dual Descent Algorithm for Network Resource Allocation (2017) — 32 citations
• Tracking Moving Agents via Inexact Online Gradient Descent Algorithm (2017) — 45 citations
• Conservative Stochastic Optimization With Expectation Constraints (2020) — 25 citations