Lajanugen Logeswaran

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 6 papers; trusts 573 researchers and is trusted by 722 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 155 citations into it; #4,130 of 35,345 by trust here, #8,157 by citations
• Natural Language Processing — 84 citations into it; #1,023 of 5,688 by trust here, #1,910 by citations
• Computer Vision and Pattern Recognition — 70 citations into it; #1,620 of 21,575 by trust here, #1,353 by citations
• Representation & Self-Supervised Learning — 43 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Model Architectures — 19 citations into it
• Multimodal Learning — 16 citations into it; #280 of 1,211 by trust here, #144 by citations
• Management Science and Operations Research — 14 citations into it

Whom Lajanugen Logeswaran trusts overall

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

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

Who trusts Lajanugen Logeswaran overall

1. Sebastian Ruder
2. Matthew E. Peters
3. Alec Radford
4. Congzheng Song
5. Sanjeev Arora
6. Grzegorz Chrupała
7. Joshua Robinson
8. Tongzhou Wang
9. Ching-Yao Chuang
10. Nikunj Saunshi

Papers from Lajanugen Logeswaran's perspective

Ordered by how strongly the researchers Lajanugen Logeswaran 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Generative Adversarial Text to Image Synthesis (2016) — 3,433 citations
• An efficient framework for learning sentence representations (2018) — 579 citations
• Zero-Shot Entity Linking by Reading Entity Descriptions (2019) — 285 citations
• Sentence Ordering using Recurrent Neural Networks (2016) — 23 citations
• Few-shot Sequence Learning with Transformers (2020) — 13 citations
• Knowledge Unlearning for Mitigating Privacy Risks in Language Models (2022) — 490 citations