Armen Aghajanyan

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 14 papers; trusts 643 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 476 citations into it; #10,539 of 35,345 by trust here, #3,155 by citations
• Natural Language Processing — 294 citations into it; #5,371 of 5,688 by trust here, #1,546 by citations
• Computer Vision and Pattern Recognition — 246 citations into it; #12,777 of 21,575 by trust here, #3,404 by citations
• Large Language Models — 156 citations into it; #570 of 799 by trust here, #334 by citations
• Evaluation & Benchmarks — 100 citations into it
• Multimodal Learning — 60 citations into it; #1,195 of 1,211 by trust here, #499 by citations
• Information Retrieval & Search — 55 citations into it; #964 of 1,022 by trust here, #748 by citations
• Representation & Self-Supervised Learning — 48 citations into it; #2,955 of 3,145 by trust here, #1,006 by citations

Whom Armen Aghajanyan trusts overall

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

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

Who trusts Armen Aghajanyan overall

1. Luke S. Zettlemoyer
2. Jing Yu Koh
3. Anastasia Razdaibiedina
4. Daniel Fried
5. Michihiro Yasunaga
6. Yi Tay
7. Mo Bavarian
8. Vishakh Padmakumar
9. Michael Lewis
10. Patrick Huber

Papers from Armen Aghajanyan's perspective

Ordered by how strongly the researchers Armen Aghajanyan 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding (2021) — 814 citations
• CM3: A Causal Masked Multimodal Model of the Internet (2022) — 180 citations
• Muppet: Massive Multi-task Representations with Pre-Finetuning (2021) — 301 citations
• Pre-training via Paraphrasing (2020) — 165 citations
• HTLM: Hyper-Text Pre-Training and Prompting of Language Models (2021) — 83 citations
• Retrieval-Augmented Multimodal Language Modeling (2023) — 166 citations
• Better Fine-Tuning by Reducing Representational Collapse (2020) — 241 citations
• Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning (2020) — 936 citations
• Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models (2022) — 288 citations
• MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers (2023) — 177 citations
• Improving Passage Retrieval with Zero-Shot Question Generation (2022) — 249 citations
• CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training (2021) — 16 citations
• Scaling Laws for Generative Mixed-Modal Language Models (2023) — 159 citations
• InCoder: A Generative Model for Code Infilling and Synthesis (2022) — 909 citations