Karen Hambardzumyan

Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 3 papers; trusts 70 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #11,537 of 35,345 by trust here, #8,706 by citations
• Computer Vision and Pattern Recognition — 32 citations into it
• Natural Language Processing — 31 citations into it
• Large Language Models — 23 citations into it
• Safety, Robustness & Interpretability — 18 citations into it; #793 of 2,251 by trust here, #758 by citations
• Evaluation & Benchmarks — 15 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Efficiency & Compression — 8 citations into it

Whom Karen Hambardzumyan trusts overall

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

1. Timo Schick
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Alec Radford
6. Ilya Sutskever
7. Alex Krizhevsky
8. Alex Wang
9. Yann LeCun
10. Quoc V. Le

Who trusts Karen Hambardzumyan overall

1. Hyojin Bahng
2. Brian Lester
3. Zonghan Yang
4. Colin Wei
5. Patrick Schramowski
6. Ali Jahanian
7. Peter West
8. Tu Vu
9. Pengfei Liu
10. Yu Meng

Papers from Karen Hambardzumyan's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
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

• Technical Report on the CleverHans v2.1.0 Adversarial Examples Library (2016) — 544 citations
• WARP: Word-level Adversarial ReProgramming (2021) — 380 citations
• Scaling Laws for Generative Mixed-Modal Language Models (2023) — 159 citations