Kashif Shah

Artificial Intelligence · 2013–2021

A researcher in the Science corpus: 9 papers; trusts 77 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 92 citations into it; #25,924 of 35,345 by trust here, #19,873 by citations
• Natural Language Processing — 86 citations into it; #1,165 of 5,688 by trust here, #2,106 by citations
• Safety, Robustness & Interpretability — 43 citations into it
• Evaluation & Benchmarks — 34 citations into it; #903 of 3,147 by trust here, #1,856 by citations
• Probabilistic & Bayesian Methods — 8 citations into it
• Large Language Models — 7 citations into it
• Machine Translation — 6 citations into it; #62 of 292 by trust here, #150 by citations
• Representation & Self-Supervised Learning — 6 citations into it

Whom Kashif Shah trusts overall

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

1. Lucia Specia
2. Holger Schwenk
3. Trevor Cohn
4. Yoshua Bengio
5. Tomas Mikolov
6. Ergun Biçici
7. Geoffrey E. Hinton
8. Matthew G. Snover
9. Ilya Sutskever
10. Dzmitry Bahdanau

Who trusts Kashif Shah overall

1. Lucia Specia
2. Varvara Logacheva
3. Carolina Scarton
4. Ondrej Bojar
5. Daniel Beck
6. David Langlois
7. Gustavo Paetzold
8. Trevor Cohn
9. Daniel Preotiuc-Pietro
10. Christian Buck

Papers from Kashif Shah's perspective

Ordered by how strongly the researchers Kashif Shah 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics (2021) — 140 citations
• Word embeddings and discourse information for Quality Estimation (2016) — 11 citations
• SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features (2016) — 17 citations
• SHEF-NN: Translation Quality Estimation with Neural Networks (2015) — 25 citations
• SHEF-Lite 2.0: Sparse Multi-task Gaussian Processes for Translation Quality Estimation (2014) — 14 citations
• Quality estimation for translation selection (2014) — 17 citations
• An Investigation on the Effectiveness of Features for Translation Quality Estimation (2013) — 48 citations
• SHEF-Lite: When Less is More for Translation Quality Estimation (2013) — 26 citations
• QuEst - A translation quality estimation framework (2013) — 238 citations