Adyasha Maharana

Artificial Intelligence · 2022–2024

A researcher in the Science corpus: 3 papers; trusts 313 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #9,954 of 35,345 by trust here, #16,771 by citations
• Natural Language Processing — 64 citations into it; #1,879 of 5,688 by trust here, #4,250 by citations
• Computer Vision and Pattern Recognition — 40 citations into it
• Evaluation & Benchmarks — 29 citations into it
• Large Language Models — 15 citations into it
• Graph Learning — 10 citations into it; #233 of 693 by trust here, #547 by citations
• Molecular Biology — 9 citations into it
• Generative Models — 7 citations into it

Whom Adyasha Maharana trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alec Radford
4. Diederik P. Kingma
5. Christian Szegedy
6. Richard S. Sutton
7. Ilya Sutskever
8. Alex Krizhevsky
9. Individualized Treat
10. Kaiming He

Who trusts Adyasha Maharana overall

1. Ethan Perez
2. Freda Shi
3. Max Bartolo
4. Or Honovich
5. Junnan Li
6. Mandar Joshi
7. Bosheng Ding
8. Douwe Kiela
9. Yu Meng
10. Karen Livescu

Papers from Adyasha Maharana's perspective

Ordered by how strongly the researchers Adyasha Maharana 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• GraDA: Graph Generative Data Augmentation for Commonsense Reasoning (2022) — 109 citations
• Evaluating Very Long-Term Conversational Memory of LLM Agents (2024) — 642 citations
• GraDA: Graph Generative Data Augmentation for Commonsense Reasoning (2022)