Gu-Yeon Wei

Artificial Intelligence · 2007–2022

A researcher in the Science corpus: 21 papers; trusts 234 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 190 citations into it; #32,780 of 35,345 by trust here, #8,077 by citations
• Computer Vision and Pattern Recognition — 174 citations into it; #13,083 of 21,575 by trust here, #4,212 by citations
• Electrical and Electronic Engineering — 131 citations into it; #8,716 of 16,539 by trust here, #3,208 by citations
• Hardware and Architecture — 110 citations into it; #997 of 1,551 by trust here, #260 by citations
• Computer Networks and Communications — 107 citations into it; #8,973 of 9,886 by trust here, #3,523 by citations
• ML Systems & Hardware — 99 citations into it; #436 of 575 by trust here, #286 by citations
• Information Systems — 85 citations into it; #7,735 of 8,771 by trust here, #5,303 by citations
• Efficiency & Compression — 76 citations into it; #1,194 of 1,264 by trust here, #588 by citations

Whom Gu-Yeon Wei trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yann LeCun
5. Karen Simonyan
6. Yoshua Bengio
7. Ilya Sutskever
8. Song Han
9. Andrew Zisserman
10. Diederik P. Kingma

Who trusts Gu-Yeon Wei overall

1. Udit Gupta
2. Vidhisha Balachandran
3. Carole-Jean Wu

Papers from Gu-Yeon Wei's perspective

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

Their papers

• MLPerf Training Benchmark (2019) — 388 citations
• Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators (2016) — 613 citations
• Fathom: reference workloads for modern deep learning methods (2016) — 189 citations
• MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance (2020) — 194 citations
• Chasing Carbon: The Elusive Environmental Footprint of Computing (2020) — 376 citations
• DeepRecSys: A System for Optimizing End-To-End At-Scale Neural Recommendation Inference (2020) — 211 citations
• Ares: A framework for quantifying the resilience of deep neural networks (2018) — 352 citations
• System level analysis of fast, per-core DVFS using on-chip switching regulators (2008) — 787 citations
• MaxNVM: Maximizing DNN Storage Density and Inference Efficiency with Sparse Encoding and Error Mitigation (2019) — 35 citations
• MASR: A Modular Accelerator for Sparse RNNs (2019) — 48 citations
• Benchmarking TPU, GPU, and CPU Platforms for Deep Learning (2019) — 325 citations
• Process Variation Tolerant 3T1D-Based Cache Architectures (2007) — 323 citations
• Thread motion: fine-grained power management for multi-core systems (2009) — 269 citations
• ACT: designing sustainable computer systems with an architectural carbon modeling tool (2022) — 280 citations
• RecSSD: near data processing for solid state drive based recommendation inference (2021) — 135 citations
• Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference (2020) — 187 citations
• Cognitive Computing Safety: The New Horizon for Reliability / The Design and Evolution of Deep Learning Workloads (2017) — 10 citations
• Weightless: Lossy Weight Encoding For Deep Neural Network Compression (2017) — 42 citations
• Profiling a warehouse-scale computer (2015) — 513 citations
• MachSuite: Benchmarks for accelerator design and customized architectures (2014) — 342 citations