Sijia Huang

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 2 papers; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it
• Molecular Biology — 20 citations into it
• Graph Learning — 18 citations into it
• Optimization & Training — 7 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Sijia Huang trusts overall

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

1. Romain Thibaux
2. George Eastman House
3. Thomas Hofmann
4. Daniel D. Lee
5. Ronglai Shen
6. Michael I. Jordan
7. Bo Zhao
8. Kim-Anh Lê Cao
9. Marylyn DeRiggi Ritchie
10. Robert Tibshirani

Who trusts Sijia Huang overall

1. Milan Picard
2. Marie-Pier Scott-Boyer

Papers from Sijia Huang's perspective

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

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

Their papers

• Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods (2021) — 492 citations
• More Is Better: Recent Progress in Multi-Omics Data Integration Methods (2017) — 616 citations