Tianyi Zhou

Artificial Intelligence · AAAI Conference on Artificial Intelligence · 2011–2024

A researcher in the Science corpus: 16 papers; trusts 776 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 605 citations into it; #11,880 of 35,345 by trust here, #5,924 by citations
• Computer Vision and Pattern Recognition — 230 citations into it
• Natural Language Processing — 200 citations into it; #4,689 of 5,688 by trust here, #2,307 by citations
• Representation & Self-Supervised Learning — 74 citations into it
• Model Architectures — 63 citations into it; #1,074 of 2,278 by trust here, #652 by citations
• Evaluation & Benchmarks — 61 citations into it
• Optimization & Training — 56 citations into it
• Federated & Distributed Learning — 44 citations into it

Whom Tianyi Zhou trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Kaiming He
5. Diederik P. Kingma
6. Richard Socher
7. Ilya Sutskever
8. Yann LeCun
9. Alex Graves
10. Tomas Mikolov

Who trusts Tianyi Zhou overall

1. Thierry Bouwmans
2. Michael Hahn
3. Xiyu Yu
4. Namrata Vaswani
5. Yuxiang Zhang
6. Hongyan Zhang
7. Tyler L. Hayes
8. Yao Wang
9. Rongmei Lin
10. Baosong Yang

Papers from Tianyi Zhou's perspective

Ordered by how strongly the researchers Tianyi Zhou 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling (2018) — 148 citations
• DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding (2017) — 787 citations
• Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling (2018) — 153 citations
• Hallusionbench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models (2023) — 618 citations
• ODIN: Disentangled Reward Mitigates Hacking in RLHF (2024) — 129 citations
• A Survey on Knowledge Distillation of Large Language Models (2024) — 355 citations
• From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning (2023) — 373 citations
• Federated Learning from Pre-Trained Models: A Contrastive Learning Approach (2022) — 296 citations
• Retrospective Adversarial Replay for Continual Learning (2022) — 54 citations
• FedProto: Federated Prototype Learning across Heterogeneous Clients (2021) — 940 citations
• Multi-center federated learning: clients clustering for better personalization (2020) — 379 citations
• Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion (2020) — 334 citations
• Bias Also Matters: Bias Attribution for Deep Neural Network Explanation (2019) — 42 citations
• Fast Directional Self-Attention Mechanism (2018) — 5 citations
• Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity (2018) — 63 citations
• GoDec: Randomized Lowrank & Sparse Matrix Decomposition in Noisy Case (2011) — 728 citations