Jia Liu
Artificial Intelligence · Network and Distributed System Security Symposium · 2010–2023
A researcher in the Science corpus: 11 papers; trusts 86 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 132 citations into it; #20,465 of 35,345 by trust here, #19,018 by citations
- Computer Vision and Pattern Recognition — 50 citations into it; #20,003 of 21,575 by trust here, #13,542 by citations
- Optimization & Training — 44 citations into it
- Computer Networks and Communications — 40 citations into it; #9,673 of 9,886 by trust here, #5,961 by citations
- Electrical and Electronic Engineering — 34 citations into it; #11,471 of 16,539 by trust here, #14,827 by citations
- Federated & Distributed Learning — 28 citations into it
- Statistical and Nonlinear Physics — 25 citations into it
- Information Systems — 24 citations into it; #8,571 of 8,771 by trust here, #6,501 by citations
Whom Jia Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Yoshua Bengio
- Geoffrey E. Hinton
- John N. Tsitsiklis
- Yann LeCun
- Kaiming He
- L. Eon Bottou
- Yurii E. Nesterov
- Diederik P. Kingma
- Ilya Sutskever
Who trusts Jia Liu overall
Papers from Jia Liu's perspective
Ordered by how strongly the researchers Jia Liu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- K-isomorphism: privacy preserving network publication against structural attacks (2010) — 391 citations
- Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning (2021) — 319 citations
- The Sample Complexity of Best-k Items Selection from Pairwise Comparisons (2020) — 18 citations
- AI of Brain and Cognitive Sciences: From the Perspective of First Principles (2023) — 3 citations
- FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping (2020) — 1,101 citations
- Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach (2019) — 52 citations
- Compressed Distributed Gradient Descent: Communication-Efficient Consensus over Networks (2018) — 28 citations
- Poisoning Attacks to Graph-Based Recommender Systems (2018) — 233 citations
- Facile synthesis of hierarchical nanocomposites of aligned polyaniline nanorods on reduced graphene oxide nanosheets for microwave absorbing materials (2017) — 164 citations
- Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks (2016) — 593 citations
- Perception of Face Parts and Face Configurations: An fMRI Study (2010) — 329 citations