Mahima Pushkarna

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 4 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 101 citations into it
• Natural Language Processing — 71 citations into it
• Evaluation & Benchmarks — 40 citations into it
• Safety, Robustness & Interpretability — 25 citations into it
• Safety Research — 11 citations into it
• Sociology and Political Science — 5 citations into it

Whom Mahima Pushkarna trusts overall

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

1. Emily M. Bender
2. Michael Hind
3. Batya Friedman
4. Alina Kuznetsova
5. Susan Leigh Star
6. Been Kim
7. Finale Doshi-Velez
8. Josua Krause
9. Joy Buolamwini
10. Mei Ngan

Who trusts Mahima Pushkarna overall

1. Sebastian Gehrmann
2. Amir-Hossein Karimi
3. Xi Chen
4. Hyung Won Chung

Papers from Mahima Pushkarna's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Gradient-based learning applied to document recognition
9. Learning Multiple Layers of Features from Tiny Images
10. Language Models are Unsupervised Multitask Learners

Their papers

• Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI (2022) — 352 citations
• The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models (2020) — 224 citations
• GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022) — 43 citations
• The What-If Tool: Interactive Probing of Machine Learning Models (2019) — 600 citations