Madian Khabsa

Artificial Intelligence · 2014–2024

A researcher in the Science corpus: 18 papers; trusts 445 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 442 citations into it; #14,270 of 35,345 by trust here, #1,139 by citations
• Natural Language Processing — 252 citations into it
• Computer Vision and Pattern Recognition — 178 citations into it
• Large Language Models — 122 citations into it
• Evaluation & Benchmarks — 114 citations into it
• Representation & Self-Supervised Learning — 58 citations into it
• Prompting & Reasoning — 42 citations into it
• Model Architectures — 40 citations into it; #1,166 of 2,278 by trust here, #485 by citations

Whom Madian Khabsa trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ilya Sutskever
6. Kaiming He
7. Yann LeCun
8. Richard Socher
9. Alex Graves
10. Oriol Vinyals

Who trusts Madian Khabsa overall

1. Saif M. Mohammad
2. Yi Tay
3. Ludo Waltman
4. Michael Gusenbauer
5. Shuhao Cao
6. Bodhisattwa Prasad Majumder
7. Tri Dao
8. Markus Norman Rabe
9. Dana Pessach
10. Andrew Jaegle

Papers from Madian Khabsa's perspective

Ordered by how strongly the researchers Madian Khabsa 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. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Linformer: Self-Attention with Linear Complexity (2020) — 2,339 citations
• Llama 2: Open Foundation and Fine-Tuned Chat Models (2023) — 17,670 citations
• Entailment as Few-Shot Learner (2021) — 198 citations
• CLEAR: Contrastive Learning for Sentence Representation (2020) — 350 citations
• Language Models as Fact Checkers? (2020) — 86 citations
• The CHEMDNER corpus of chemicals and drugs and its annotation principles (2015) — 444 citations
• Effective Long-Context Scaling of Foundation Models (2023) — 346 citations
• The Llama 3 Herd of Models (2024) — 17,085 citations
• When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations
• Progressive Prompts: Continual Learning for Language Models (2023) — 243 citations
• Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations (2023) — 1,166 citations
• The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants (2023) — 336 citations
• Quantifying Adaptability in Pre-trained Language Models with 500 Tasks (2021) — 18 citations
• Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis (2021) — 114 citations
• UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning (2021) — 235 citations
• A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision (2021) — 23 citations
• Building Natural Language Interfaces to Web APIs (2017) — 64 citations
• The Number of Scholarly Documents on the Public Web (2014) — 340 citations