Ming Jin

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

A researcher in the Science corpus: 3 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it
• Model Architectures — 17 citations into it

Whom Ming Jin trusts overall

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

1. Spyros Makridakis
2. Haixu Wu
3. Sepp Hochreiter
4. Geoffrey E. Hinton
5. Alec Radford
6. J. Urgen Schmidhuber
7. Yoshua Bengio
8. Diederik P. Kingma
9. Ilya Sutskever
10. Alex Krizhevsky

Who trusts Ming Jin overall

1. Gerald Woo

Papers from Ming Jin's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts (2024) — 314 citations
• Foundation Models for Time Series Analysis: A Tutorial and Survey (2024) — 440 citations
• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models (2023) — 1,070 citations