Luping Liu

Artificial Intelligence · 2022–2024

A researcher in the Science corpus: 3 papers; trusts 45 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it
• Signal Processing — 56 citations into it
• Computer Vision and Pattern Recognition — 52 citations into it; #18,478 of 21,575 by trust here, #6,954 by citations
• Generative Models — 50 citations into it; #1,675 of 1,956 by trust here, #776 by citations
• Statistical and Nonlinear Physics — 13 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Biophysics — 5 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Luping Liu trusts overall

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

1. Alex Krizhevsky
2. Yang Song
3. Jonathan Ho
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Alex Nichol
7. Yoshua Bengio
8. Stefano Ermon
9. Kaiming He
10. Yann LeCun

Who trusts Luping Liu overall

1. Tianwei Yin
2. Qinsheng Zhang
3. Wenliang Zhao
4. Tero Karras
5. Xinyin Ma
6. Dongxu Li
7. Ye Yuan
8. Hu Ye
9. Florinel-Alin Croitoru
10. Shanchuan Lin

Papers from Luping Liu's perspective

Ordered by how strongly the researchers Luping Liu 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Pseudo Numerical Methods for Diffusion Models on Manifolds (2022) — 905 citations
• Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models (2023) — 505 citations
• Make-A-Voice: Revisiting Voice Large Language Models as Scalable Multilingual and Multitask Learners (2024) — 13 citations