Shiyu Wang

Artificial Intelligence · International Conference on Learning Representations · 2023–2024

A researcher in the Science corpus: 4 papers; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it
• Signal Processing — 64 citations into it
• Management Science and Operations Research — 47 citations into it
• Model Architectures — 27 citations into it
• Hardware and Architecture — 6 citations into it

Whom Shiyu Wang trusts overall

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

1. Haixu Wu
2. Noam Shazeer
3. Ashish Vaswani
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Ailing Zeng
7. Diederik P. Kingma
8. Alex Krizhevsky
9. Sepp Hochreiter
10. Ilya Sutskever

Papers from Shiyu Wang's perspective

Ordered by how strongly the researchers Shiyu Wang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Long Short-Term Memory
4. ImageNet classification with deep convolutional neural networks
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• iTransformer: Inverted Transformers Are Effective for Time Series Forecasting (2023) — 2,065 citations
• TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting (2024) — 722 citations
• Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts (2024) — 314 citations
• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models (2023) — 1,070 citations