Manish Gupta

Artificial Intelligence · with Varma · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 283 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 139 citations into it; #9,113 of 35,345 by trust here, #8,077 by citations
• Computer Vision and Pattern Recognition — 71 citations into it
• Natural Language Processing — 57 citations into it; #4,097 of 5,688 by trust here, #4,250 by citations
• Efficiency & Compression — 55 citations into it
• Model Architectures — 28 citations into it
• Information Systems — 18 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Large Language Models — 16 citations into it

Whom Manish Gupta trusts overall

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

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

Who trusts Manish Gupta overall

1. Anthony Rios
2. Yonatan Belinkov
3. Pushkar Mishra
4. John Pavlopoulos
5. Tanmay Garg
6. Antigoni-Maria Founta
7. Michael Wiegand
8. Ji Ho Park
9. Endang Wahyu Pamungkas
10. Mladen Karan

Papers from Manish Gupta's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Interpretation of Semantic Tweet Representations (2017) — 8 citations
• MVAE: Multimodal Variational Autoencoder for Fake News Detection (2019) — 840 citations
• Compression of Deep Learning Models for Text: A Survey (2020) — 148 citations
• Stereotypical Bias Removal for Hate Speech Detection Task using Knowledge-based Generalizations (2019) — 111 citations
• Deep Learning for Hate Speech Detection in Tweets (2017) — 1,283 citations