Joy T. Wu

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it
• Natural Language Processing — 15 citations into it

Whom Joy T. Wu trusts overall

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

1. Ronan Collobert

Papers from Joy T. Wu's perspective

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

1. Natural Language Processing (Almost) from Scratch
2. Torch7: A Matlab-like Environment for Machine Learning
3. ImageNet classification with deep convolutional neural networks
4. Scikit-learn: Machine Learning in Python
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Gradient-based learning applied to document recognition
7. ADADELTA: An Adaptive Learning Rate Method
8. Why Does Unsupervised Pre-training Help Deep Learning?
9. Attention is All you Need (Neural Information Processing Systems)
10. Long Short-Term Memory

Their papers

• Comparing Rule-Based and Deep Learning Models for Patient Phenotyping (2017) — 45 citations
• Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives (2018) — 199 citations