Saleema Amershi
Artificial Intelligence · 2010–2019
A researcher in the Science corpus: 10 papers; trusts 25 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 68 citations into it; #16,490 of 35,345 by trust here, #9,760 by citations
- Computer Vision and Pattern Recognition — 36 citations into it; #10,725 of 21,575 by trust here, #5,375 by citations
- Natural Language Processing — 25 citations into it
- Human-Computer Interaction — 24 citations into it
- Computer Science Applications — 20 citations into it
- Evaluation & Benchmarks — 20 citations into it
- Information Systems and Management — 17 citations into it
- Safety, Robustness & Interpretability — 10 citations into it
Whom Saleema Amershi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ron Kohavi
- Simone Stumpf
- Todd Kulesza
- Andrea Lockerd Thomaz
- Brian Y. Lim
- George A. Miller
- Justin Talbot
- David A. Cohn
- Anol Bhattacherjee
- Kristina Höök
Who trusts Saleema Amershi overall
- John J. Dudley
- Minsuk Kahng
- Daniel S. Weld
- Josua Krause
- Sina Mohseni
- Per Ola Kristensson
- Todd Kulesza
- Gagan Bansal
- Q. Liao
- Yang Gao
Papers from Saleema Amershi's perspective
Ordered by how strongly the researchers Saleema Amershi trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet: A large-scale hierarchical image database
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Power to the People: The Role of Humans in Interactive Machine Learning (2014) — 1,228 citations
- ModelTracker: Redesigning Performance Analysis Tools for Machine Learning (2015) — 311 citations
- UvA-DARE (Digital Academic Repository) A Dataset and Evaluation Metrics for Abstractive Compression of Sentences and Short Paragraphs — 52 citations
- Guidelines for Human-AI Interaction (2019) — 2,339 citations
- Will You Accept an Imperfect AI?: Exploring Designs for Adjusting End-user Expectations of AI Systems (2019) — 363 citations
- Software Engineering for Machine Learning: A Case Study (2019) — 1,033 citations
- Revolt: Collaborative Crowdsourcing for Labeling Machine Learning Datasets (2017) — 273 citations
- FeatureInsight: Visual support for error-driven feature ideation in text classification (2015) — 94 citations
- Structured labeling for facilitating concept evolution in machine learning (2014) — 125 citations
- Examining multiple potential models in end-user interactive concept learning (2010) — 50 citations