Wei Wei

Artificial Intelligence · with Juan · 2018–2022

A researcher in the Science corpus: 13 papers; trusts 867 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 268 citations into it
• Computer Vision and Pattern Recognition — 215 citations into it
• Optimization & Training — 57 citations into it
• Model Architectures — 55 citations into it
• Meta & Continual Learning — 50 citations into it
• Representation & Self-Supervised Learning — 49 citations into it
• Reinforcement Learning — 40 citations into it; #2,147 of 2,242 by trust here, #1,609 by citations
• Natural Language Processing — 36 citations into it

Whom Wei Wei trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Karen Simonyan
9. Jimmy Ba
10. Ross B. Girshick

Who trusts Wei Wei overall

1. Xiangxiang Chu
2. Dimitrios Stamoulis
3. Thomas Elsken
4. Pietro Buzzega
5. Raia Hadsell

Papers from Wei Wei's perspective

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

Their papers

• COCO-GAN: Generation by Parts via Conditional Coordinating (2019) — 149 citations
• DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures (2018) — 197 citations
• Task Transfer by Preference-Based Cost Learning (2018) — 56 citations
• On the Robustness of Self-Attentive Models (2019) — 110 citations
• Balancing Between Forgetting and Acquisition in Incremental Subpopulation Learning (2022) — 16 citations
• Differentiable Top-k with Optimal Transport (2020) — 114 citations
• Remix: Rebalanced Mixup (2020) — 304 citations
• Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization (2020) — 50 citations
• Overcoming Catastrophic Forgetting by Generative Regularization (2019)
• Overcoming Catastrophic Forgetting by Bayesian Generative Regularization (2019) — 19 citations
• InstaNAS: Instance-aware Neural Architecture Search (2018) — 54 citations
• PPP-Net: Platform-aware Progressive Search for Pareto-optimal Neural Architectures (2018) — 58 citations
• MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning (2018) — 149 citations