Nithin Holla

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 201 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 104 citations into it
• Computer Vision and Pattern Recognition — 65 citations into it
• Natural Language Processing — 50 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Meta & Continual Learning — 22 citations into it
• Large Language Models — 14 citations into it
• Optimization & Training — 11 citations into it

Whom Nithin Holla trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. J. Urgen Schmidhuber
6. Yann LeCun
7. Ilya Sutskever
8. Sepp Hochreiter
9. Kaiming He
10. Jimmy Ba

Who trusts Nithin Holla overall

1. Chengyu Wang
2. Yanzhe Zhang

Papers from Nithin Holla's perspective

Ordered by how strongly the researchers Nithin Holla 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Multimodal Framework for the Detection of Hateful Memes (2020) — 103 citations
• Towards a robust experimental framework and benchmark for lifelong language learning (2021) — 9 citations
• Meta-Learning with Sparse Experience Replay for Lifelong Language Learning (2020) — 25 citations