Premkumar Natarajan
Artificial Intelligence · 2011–2022
A researcher in the Science corpus: 16 papers; trusts 1,017 researchers and is trusted by 43 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 349 citations into it; #6,963 of 35,345 by trust here, #3,609 by citations
- Computer Vision and Pattern Recognition — 266 citations into it; #8,332 of 21,575 by trust here, #6,873 by citations
- Natural Language Processing — 212 citations into it; #1,295 of 5,688 by trust here, #842 by citations
- Safety, Robustness & Interpretability — 115 citations into it; #463 of 2,251 by trust here, #282 by citations
- Large Language Models — 61 citations into it
- Generative Models — 45 citations into it
- Evaluation & Benchmarks — 43 citations into it
- Representation & Self-Supervised Learning — 34 citations into it
Whom Premkumar Natarajan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Yoshua Bengio
- Yann LeCun
- Ilya Sutskever
- Kaiming He
- Alec Radford
- Karen Simonyan
- Andrew Zisserman
Who trusts Premkumar Natarajan overall
- Emily Sheng
- Kai-Wei Chang
- Luisa Verdoliva
- Sunipa Dev
- Percy Liang
- Eric Michael Smith
- Nanyun Peng
- Ninareh Mehrabi
- Xuefeng Hu
- Ayush Jaiswal
Papers from Premkumar Natarajan's perspective
Ordered by how strongly the researchers Premkumar Natarajan trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- The Woman Worked as a Babysitter: On Biases in Language Generation (2019) — 839 citations
- AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model (2022) — 91 citations
- Recurrent Convolutional Strategies for Face Manipulation Detection in Videos (2019) — 581 citations
- MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages (2022) — 219 citations
- Societal Biases in Language Generation: Progress and Challenges (2021) — 287 citations
- CapsuleGAN: Generative Adversarial Capsule Network (2018) — 169 citations
- Bidirectional Conditional Generative Adversarial Networks (2017) — 42 citations
- ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features (2019) — 655 citations
- “Nice Try, Kiddo”: Investigating Ad Hominems in Dialogue Responses (2021) — 62 citations
- Unsupervised Adversarial Invariance (2018) — 52 citations
- Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering (2022) — 93 citations
- Towards Controllable Biases in Language Generation (2020) — 163 citations
- Invariant Representations through Adversarial Forgetting (2019) — 47 citations
- Learning Pose-Aware Models for Pose-Invariant Face Recognition in the Wild (2019) — 102 citations
- BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization (2018) — 306 citations
- Shannon Entropy based Randomness Measurement and Test for Image Encryption (2011) — 581 citations