Chao Huang

Artificial Intelligence · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · 2022–2024

A researcher in the Science corpus: 7 papers; trusts 78 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 162 citations into it
• Information Systems — 111 citations into it
• Computer Vision and Pattern Recognition — 54 citations into it
• Representation & Self-Supervised Learning — 26 citations into it
• Recommender Systems — 20 citations into it
• Management Science and Operations Research — 15 citations into it
• Graph Learning — 12 citations into it
• Statistical and Nonlinear Physics — 7 citations into it

Whom Chao Huang trusts overall

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

1. Xiangnan He
2. Xiang Wang
3. Yehuda Koren
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Ruslan Salakhutdinov
7. Diederik P. Kingma
8. Alex Krizhevsky
9. Kaiming He
10. Ilya Sutskever

Who trusts Chao Huang overall

1. Junliang Yu
2. Xubin Ren

Papers from Chao Huang's perspective

Ordered by how strongly the researchers Chao Huang 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• LightRAG: Simple and Fast Retrieval-Augmented Generation (2024) — 375 citations
• LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation (2023) — 395 citations
• GraphGPT: Graph Instruction Tuning for Large Language Models (2023) — 364 citations
• Representation Learning with Large Language Models for Recommendation (2023) — 427 citations
• LLMRec: Large Language Models with Graph Augmentation for Recommendation (2023) — 451 citations
• Hypergraph Contrastive Collaborative Filtering (2022) — 479 citations
• Knowledge Graph Contrastive Learning for Recommendation (2022) — 506 citations