Yun-Hsuan Sung
Artificial Intelligence · 2016–2021
A researcher in the Science corpus: 9 papers; trusts 108 researchers and is trusted by 28 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 208 citations into it; #7,568 of 35,345 by trust here, #1,392 by citations
- Natural Language Processing — 160 citations into it; #1,842 of 5,688 by trust here, #705 by citations
- Computer Vision and Pattern Recognition — 110 citations into it; #14,384 of 21,575 by trust here, #2,295 by citations
- Representation & Self-Supervised Learning — 60 citations into it; #2,296 of 3,145 by trust here, #253 by citations
- Evaluation & Benchmarks — 46 citations into it
- Model Architectures — 35 citations into it
- Large Language Models — 24 citations into it
- Optimization & Training — 7 citations into it
Whom Yun-Hsuan Sung trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Ilya Sutskever
- Oriol Vinyals
- Quoc V. Le
- Alex Krizhevsky
- Diederik P. Kingma
- J. Urgen Schmidhuber
- Tomas Mikolov
- Sepp Hochreiter
Who trusts Yun-Hsuan Sung overall
- Yinfei Yang
- Daniel Matthew Cer
- Matthew Henderson
- H. Brendan McMahan
- Holger Schwenk
- Jianmo Ni
- Amin Ahmad
Papers from Yun-Hsuan Sung's perspective
Ordered by how strongly the researchers Yun-Hsuan Sung trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision (2021) — 5,764 citations
- Universal Sentence Encoder (2018) — 2,093 citations
- Learning Semantic Textual Similarity from Conversations (2018) — 167 citations
- Multilingual Universal Sentence Encoder for Semantic Retrieval (2019) — 534 citations
- Efficient Natural Language Response Suggestion for Smart Reply (2017) — 514 citations
- Effective Parallel Corpus Mining using Bilingual Sentence Embeddings (2018) — 115 citations
- Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax (2019) — 124 citations
- Conversational Contextual Cues: The Case of Personalization and History for Response Ranking (2016) — 93 citations
- LongT5: Efficient Text-To-Text Transformer for Long Sequences (2021) — 414 citations