Alvin Wan
Computer Vision and Pattern Recognition · 2017–2020
A researcher in the Science corpus: 8 papers; trusts 490 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 193 citations into it; #8,000 of 21,575 by trust here, #4,212 by citations
- Artificial Intelligence — 146 citations into it; #23,425 of 35,345 by trust here, #5,924 by citations
- Efficiency & Compression — 65 citations into it
- Model Architectures — 56 citations into it
- Optimization & Training — 18 citations into it
- Representation & Self-Supervised Learning — 8 citations into it
- Aerospace Engineering — 7 citations into it
- Automotive Engineering — 7 citations into it; #399 of 3,081 by trust here, #834 by citations
Whom Alvin Wan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Kaiming He
- Karen Simonyan
- Diederik P. Kingma
- Ross B. Girshick
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
- Jia Deng
Who trusts Alvin Wan overall
- Xuanyi Dong
- Bichen Wu
- Meike Nauta
- Xiaoliang Dai
- Sergey I. Nikolenko
- Vinícius G. Costa
- Michael Janner
- Ji Lin
- Ron van Bree
- Ignasi Clavera
Papers from Alvin Wan's perspective
Ordered by how strongly the researchers Alvin Wan trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Visual Transformers: Token-based Image Representation and Processing for Computer Vision (2020) — 755 citations
- Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions (2017) — 401 citations
- Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning (2018) — 351 citations
- SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud (2017) — 932 citations
- FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions (2020) — 334 citations
- FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function (2020) — 77 citations
- NBDT: Neural-Backed Decision Trees (2020) — 125 citations
- SegNBDT: Visual Decision Rules for Segmentation (2020) — 7 citations