David W. Grant

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 David W. Grant trusts

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

1. Yoon Kim (in Artificial Intelligence)

Papers from David W. Grant's perspective

Ordered by how strongly the researchers David W. Grant 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. Scikit-learn: Machine Learning in Python
4. ADADELTA: An Adaptive Learning Rate Method
5. Why Does Unsupervised Pre-training Help Deep Learning?
6. Association for Computational Linguistics " Why Should I Trust You? " Explaining the Predictions of Any Classifier
7. The mythos of model interpretability
8. Rationalizing Neural Predictions
9. The Unified Medical Language System (UMLS): integrating biomedical terminology
10. MIMIC-III, a freely accessible critical care database

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