Jian Tang

Artificial Intelligence · IEEE Conference on Computer Communications · 2005–2021

A researcher in the Science corpus: 14 papers; trusts 219 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 138 citations into it; #28,571 of 35,345 by trust here, #21,316 by citations
• Computer Vision and Pattern Recognition — 92 citations into it; #17,576 of 21,575 by trust here, #17,757 by citations
• Reinforcement Learning — 56 citations into it
• Computer Networks and Communications — 48 citations into it; #5,431 of 9,886 by trust here, #2,300 by citations
• Electrical and Electronic Engineering — 37 citations into it; #10,461 of 16,539 by trust here, #8,322 by citations
• Optimization & Training — 25 citations into it; #2,262 of 2,601 by trust here, #2,313 by citations
• Representation & Self-Supervised Learning — 24 citations into it
• Model Architectures — 23 citations into it

Whom Jian Tang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Graves
5. Richard S. Sutton
6. J. Urgen Schmidhuber
7. Yann LeCun
8. Ashish Raniwala
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Jian Tang overall

1. Chaoyun Zhang
2. Guowang Miao
3. Anand Prabhu Subramanian
4. Paul Patras
5. Qian Mao
6. Mahesh K. Marina
7. Mohamed F. Younis
8. Micah Goldblum
9. Samir R Das
10. Nguyen Cong Luong

Papers from Jian Tang's perspective

Ordered by how strongly the researchers Jian Tang trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Adversarial Meta-Learning (2018) — 46 citations
• A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs (2017) — 243 citations
• Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach (2018) — 455 citations
• Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data (2017) — 117 citations
• Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing (2012) — 1,031 citations
• Relay node placement in large scale wireless sensor networks (2006) — 364 citations
• Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones (2016) — 404 citations
• Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control (2020) — 56 citations
• Hierarchical Graph Attention Network for Few-shot Visual-Semantic Learning (2021) — 13 citations
• A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers (2018) — 471 citations
• Experience-driven Networking: A Deep Reinforcement Learning based Approach (2018) — 414 citations
• Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach (2017) — 353 citations
• A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning (2017) — 279 citations
• Interference-aware topology control and QoS routing in multi-channel wireless mesh networks (2005) — 585 citations