Huaqiang Wu

Electrical and Electronic Engineering · 2017–2023

A researcher in the Science corpus: 18 papers; trusts 10 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 455 citations into it; #14,666 of 16,539 by trust here, #2,753 by citations
• Cellular and Molecular Neuroscience — 213 citations into it
• Computer Vision and Pattern Recognition — 62 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Huaqiang Wu trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Yann LeCun
4. Peter N. Belhumeur
5. Shaoqing Ren
6. Geoffrey E. Hinton
7. Jeff Donahue
8. Shimeng Yu
9. X. Zhang
10. Can Li

Who trusts Huaqiang Wu overall

1. Shimeng Yu

Papers from Huaqiang Wu's perspective

Ordered by how strongly the researchers Huaqiang Wu 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. ImageNet Large Scale Visual Recognition Challenge
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Microsoft COCO: Common Objects in Context
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• Towards artificial general intelligence with hybrid Tianjic chip architecture (2019) — 1,085 citations
• Face classification using electronic synapses (2017) — 828 citations
• Recommended Methods to Study Resistive Switching Devices (2018) — 574 citations
• Improving Analog Switching in HfOx-Based Resistive Memory With a Thermal Enhanced Layer (2017) — 187 citations
• Fully hardware-implemented memristor convolutional neural network (2020) — 2,009 citations
• Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit (2020) — 597 citations
• Edge learning using a fully integrated neuro-inspired memristor chip (2023) — 381 citations
• A compute-in-memory chip based on resistive random-access memory (2022) — 863 citations
• Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing (2021) — 516 citations
• Neuro-inspired computing chips (2020) — 772 citations
• Memory materials and devices: From concept to application (2020) — 213 citations
• Reliability of analog resistive switching memory for neuromorphic computing (2020) — 317 citations
• Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges (2019) — 708 citations
• Understanding memristive switching via in situ characterization and device modeling (2019) — 420 citations
• Synaptic silicon-nanocrystal phototransistors for neuromorphic computing (2019) — 150 citations
• Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications (2018) — 309 citations
• An artificial nociceptor based on a diffusive memristor (2018) — 447 citations
• Competition between Metallic and Vacancy Defect Conductive Filaments in a CH3NH3PbI3-Based Memory Device (2018) — 141 citations