Ankush Chatterjee
Artificial Intelligence · 2016–2019
A researcher in the Science corpus: 3 papers; trusts 52 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 53 citations into it; #31,667 of 35,345 by trust here, #18,186 by citations
- Natural Language Processing — 36 citations into it
- Computer Vision and Pattern Recognition — 13 citations into it
- Model Architectures — 13 citations into it
- Representation & Self-Supervised Learning — 13 citations into it
- Experimental and Cognitive Psychology — 5 citations into it
- Optimization & Training — 5 citations into it
Whom Ankush Chatterjee trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Ilya Sutskever
- Richard Socher
- Sepp Hochreiter
- Jeffrey Pennington
- Tomas Mikolov
- Carlo Strapparava
Who trusts Ankush Chatterjee overall
- Saif M. Mohammad
- Peixiang Zhong
- Soujanya Poria
- Duzhen Zhang
- Francisca Adoma Acheampong
- Jamin Shin
- Di Wang
Papers from Ankush Chatterjee's perspective
Ordered by how strongly the researchers Ankush Chatterjee trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference (2016) — 13 citations
- Understanding Emotions in Text Using Deep Learning and Big Data (2019) — 350 citations
- SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text (2019) — 332 citations