Tuo Zhao

Artificial Intelligence · 2011–2023

A researcher in the Science corpus: 20 papers; trusts 553 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 329 citations into it; #11,864 of 35,345 by trust here, #5,749 by citations
• Natural Language Processing — 202 citations into it; #4,926 of 5,688 by trust here, #3,421 by citations
• Computer Vision and Pattern Recognition — 164 citations into it; #12,241 of 21,575 by trust here, #5,205 by citations
• Optimization & Training — 71 citations into it; #2,433 of 2,601 by trust here, #1,298 by citations
• Model Architectures — 59 citations into it
• Large Language Models — 52 citations into it
• Computational Mechanics — 47 citations into it; #9,707 of 10,001 by trust here, #9,276 by citations
• Evaluation & Benchmarks — 44 citations into it

Whom Tuo Zhao trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Yurii E. Nesterov
6. Ilya Sutskever
7. Kaiming He
8. Yann LeCun
9. Jerome H. Friedman
10. Jimmy Ba

Who trusts Tuo Zhao overall

1. Mathias Drton
2. Jonas M. B. Haslbeck
3. Preetam Nandy
4. Siqi Sun
5. Safiye Celik
6. Marloes H. Maathuis
7. Christopher Lucas
8. Zachary D. Kurtz
9. Lourens J. Waldorp
10. Weiyang Liu

Papers from Tuo Zhao's perspective

Ordered by how strongly the researchers Tuo Zhao 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization (2019) — 613 citations
• Deep Hyperspherical Learning (2017) — 153 citations
• Taming Sparsely Activated Transformer with Stochastic Experts (2021) — 152 citations
• Knowledge-Based Question Answering as Machine Translation (2014) — 136 citations
• Neural Document Summarization by Jointly Learning to Score and Select Sentences (2018) — 346 citations
• Table-to-Text: Describing Table Region With Natural Language (2018) — 116 citations
• Transformer Hawkes Process (2020) — 399 citations
• Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning (2023) — 729 citations
• AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning (2023) — 404 citations
• OPERA: Operation-Pivoted Discrete Reasoning over Text (2022) — 11 citations
• Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting (2020) — 81 citations
• Differentiable Top-k with Optimal Transport (2020) — 114 citations
• Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing (2019) — 37 citations
• Online Factorization and Partition of Complex Networks From Random Walks (2017) — 8 citations
• NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization (2016) — 45 citations
• The Physical Systems Behind Optimization Algorithms (2016) — 19 citations
• Automatic Image Dataset Construction from Click-through Logs Using Deep Neural Network (2015) — 32 citations
• Accelerated Mini-batch Randomized Block Coordinate Descent Method (2014) — 74 citations
• The huge Package for High-dimensional Undirected Graph Estimation in R (2012) — 531 citations
• Target-dependent Twitter Sentiment Classification (2011) — 1,112 citations