Ernie Chang

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 5 papers; trusts 135 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 103 citations into it; #23,472 of 35,345 by trust here, #19,439 by citations
• Natural Language Processing — 82 citations into it; #3,939 of 5,688 by trust here, #4,250 by citations
• Computer Vision and Pattern Recognition — 42 citations into it
• Evaluation & Benchmarks — 24 citations into it
• Model Architectures — 11 citations into it

Whom Ernie Chang trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Eneko Agirre
5. Kyunghyun Cho
6. Dzmitry Bahdanau
7. Sepp Hochreiter
8. Ilya Sutskever
9. Alex Krizhevsky
10. Jekaterina Novikova

Who trusts Ernie Chang overall

1. Soumya Batra
2. Peyman Heidari
3. Shreyan Bakshi
4. Shashank Jain
5. Xiaoyu Shen
6. Xinnuo Xu

Papers from Ernie Chang's perspective

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

1. Long Short-Term Memory
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet classification with deep convolutional neural networks
6. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence (2020) — 51 citations
• LLM-QAT: Data-Free Quantization Aware Training for Large Language Models (2023) — 398 citations
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations
• Neural Data-to-Text Generation with LM-based Text Augmentation (2021) — 46 citations
• Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model (2017) — 16 citations