Zhe Zhao
Artificial Intelligence · with Chi · 2017–2022
A researcher in the Science corpus: 20 papers; trusts 797 researchers and is trusted by 24 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 684 citations into it; #9,078 of 35,345 by trust here, #2,967 by citations
- Computer Vision and Pattern Recognition — 361 citations into it; #11,707 of 21,575 by trust here, #4,193 by citations
- Natural Language Processing — 215 citations into it
- Representation & Self-Supervised Learning — 128 citations into it; #1,593 of 3,145 by trust here, #905 by citations
- Model Architectures — 106 citations into it; #1,293 of 2,278 by trust here, #842 by citations
- Evaluation & Benchmarks — 102 citations into it
- Information Systems — 91 citations into it; #7,158 of 8,771 by trust here, #1,362 by citations
- Optimization & Training — 90 citations into it
Whom Zhe Zhao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Alex Krizhevsky
- Kaiming He
- Ilya Sutskever
- Yann LeCun
- Alex Graves
- Ross B. Girshick
- Oriol Vinyals
Who trusts Zhe Zhao overall
- Yi Tay
- Mostafa Dehghani
- Barret Zoph
- Yuyan Wang
- Udit Gupta
- Canwen Xu
- Junling Liu
- Hussein Hazimeh
- Tal Schuster
- Dara Bahri
Papers from Zhe Zhao's perspective
Ordered by how strongly the researchers Zhe Zhao 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Synthesizer: Rethinking Self-Attention for Transformer Models (2020) — 407 citations
- Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts (2018) — 1,663 citations
- The Benchmark Lottery (2021) — 147 citations
- Recommending what video to watch next: a multitask ranking system (2019) — 418 citations
- K-BERT: Enabling Language Representation with Knowledge Graph (2019) — 930 citations
- Efficiently Identifying Task Groupings for Multi-Task Learning (2021) — 332 citations
- DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning (2021) — 204 citations
- Transformer Memory as a Differentiable Search Index (2022) — 444 citations
- Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations (2017) — 473 citations
- FastBERT: a Self-distilling BERT with Adaptive Inference Time (2020) — 436 citations
- HyperPrompt: Prompt-based Task-Conditioning of Transformers (2022) — 114 citations
- UER: An Open-Source Toolkit for Pre-training Models (2019) — 145 citations
- Sampling-bias-corrected neural modeling for large corpus item recommendations (2019) — 344 citations
- HyperGrid: Efficient Multi-Task Transformers with Grid-wise Decomposable Hyper Projections (2020) — 9 citations
- Fairness in Recommendation Ranking through Pairwise Comparisons (2019) — 457 citations
- HyperGrid Transformers: Towards A Single Model for Multiple Tasks (2021) — 44 citations
- Multi-task Learning (2020)
- Small Towers Make Big Differences (2020) — 11 citations
- Synthesizer: Rethinking Self-Attention in Transformer Models (2020) — 76 citations
- Understanding and Improving Knowledge Distillation (2020) — 164 citations