Jianyu Huang

Hardware and Architecture · 2019–2021

A researcher in the Science corpus: 6 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Hardware and Architecture — 21 citations into it
• Computer Networks and Communications — 13 citations into it

Whom Jianyu Huang trusts overall

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

1. Alex Krizhevsky
2. Kazushige Goto
3. R. V. D. Geijn
4. Kaiming He
5. Maxim Naumov
6. Geoffrey E. Hinton
7. Karen Simonyan
8. Yann LeCun
9. Diederik P. Kingma
10. Yoshua Bengio

Who trusts Jianyu Huang overall

1. Tim Dettmers

Papers from Jianyu Huang's perspective

Ordered by how strongly the researchers Jianyu Huang 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. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet: A large-scale hierarchical image database
5. Going deeper with convolutions
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. ImageNet Large Scale Visual Recognition Challenge
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Long Short-Term Memory
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• A Study of BFLOAT16 for Deep Learning Training (2019) — 492 citations
• FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference (2021) — 69 citations
• Deep Learning Recommendation Model for Personalization and Recommendation Systems (2019) — 944 citations
• Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale (2021) — 14 citations
• Software-hardware co-design for fast and scalable training of deep learning recommendation models (2021) — 209 citations
• High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models (2021) — 49 citations