Jongsoo Park

Artificial Intelligence · 2014–2022

A researcher in the Science corpus: 11 papers; trusts 59 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 198 citations into it
• Computer Vision and Pattern Recognition — 172 citations into it; #20,566 of 21,575 by trust here, #6,089 by citations
• Efficiency & Compression — 66 citations into it; #1,186 of 1,264 by trust here, #731 by citations
• Information Systems — 59 citations into it
• Model Architectures — 41 citations into it
• Hardware and Architecture — 34 citations into it
• Recommender Systems — 33 citations into it
• Computer Networks and Communications — 24 citations into it

Whom Jongsoo Park trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Ilya Sutskever
5. Yoshua Bengio
6. Yann LeCun
7. Ross B. Girshick
8. Song Han
9. Jia Deng
10. Alex Graves

Who trusts Jongsoo Park overall

1. Georgios Georgiadis
2. Wujie Wen
3. Udit Gupta
4. Changan Chen
5. Frederick Tung
6. Yingfeng Cai
7. Dheevatsa Mudigere
8. Mark Kurtz

Papers from Jongsoo Park's perspective

Ordered by how strongly the researchers Jongsoo Park 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Study of BFLOAT16 for Deep Learning Training (2019) — 492 citations
• Glow: Graph Lowering Compiler Techniques for Neural Networks (2018) — 337 citations
• Faster CNNs with Direct Sparse Convolutions and Guided Pruning (2016) — 189 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
• DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction (2022) — 17 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
• Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications (2018) — 205 citations
• Navigating the maze of graph analytics frameworks using massive graph datasets (2014) — 202 citations