Chunting Zhou

Artificial Intelligence · 2015–2024

A researcher in the Science corpus: 15 papers; trusts 778 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 433 citations into it; #6,847 of 35,345 by trust here, #4,682 by citations
• Natural Language Processing — 276 citations into it; #3,828 of 5,688 by trust here, #2,407 by citations
• Computer Vision and Pattern Recognition — 205 citations into it; #13,065 of 21,575 by trust here, #5,806 by citations
• Large Language Models — 94 citations into it
• Evaluation & Benchmarks — 76 citations into it
• Model Architectures — 62 citations into it; #1,626 of 2,278 by trust here, #1,378 by citations
• Generative Models — 56 citations into it
• Representation & Self-Supervised Learning — 52 citations into it

Whom Chunting Zhou trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ilya Sutskever
6. Jimmy Ba
7. Kaiming He
8. Yann LeCun
9. Alex Graves
10. Ruslan Salakhutdinov

Who trusts Chunting Zhou overall

1. Jiatao Gu
2. Weijia Xu
3. Jungo Kasai
4. Gang Liu
5. T. Kim
6. Yung-Sung Chuang
7. Zhen Wang
8. Namgyu Ho
9. Marine Carpuat
10. Jianping Gou

Papers from Chunting Zhou's perspective

Ordered by how strongly the researchers Chunting 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Towards a Unified View of Parameter-Efficient Transfer Learning (2021) — 1,223 citations
• FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow (2019) — 203 citations
• StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing (2018) — 103 citations
• Understanding Knowledge Distillation in Non-autoregressive Machine Translation (2019) — 237 citations
• Density Matching for Bilingual Word Embedding (2019) — 41 citations
• Prompt Consistency for Zero-Shot Task Generalization (2022) — 97 citations
• A C-LSTM Neural Network for Text Classification (2015) — 931 citations
• Training Trajectories of Language Models Across Scales (2022) — 81 citations
• Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model (2024) — 446 citations
• LIMA: Less Is More for Alignment (2023) — 1,362 citations
• FacTool: Factuality Detection in Generative AI - A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios (2023) — 315 citations
• In-context Examples Selection for Machine Translation (2022) — 260 citations
• Luna: Linear Unified Nested Attention (2021) — 140 citations
• Detecting Hallucinated Content in Conditional Neural Sequence Generation (2020) — 234 citations
• MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders (2019) — 41 citations