Navonil Majumder

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 15 papers; trusts 576 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 650 citations into it; #5,301 of 35,345 by trust here, #5,495 by citations
• Computer Vision and Pattern Recognition — 321 citations into it; #7,933 of 21,575 by trust here, #8,296 by citations
• Natural Language Processing — 229 citations into it; #2,090 of 5,688 by trust here, #2,605 by citations
• Signal Processing — 178 citations into it
• Multimodal Learning — 139 citations into it
• Speech & Audio — 97 citations into it
• Representation & Self-Supervised Learning — 86 citations into it
• Experimental and Cognitive Psychology — 70 citations into it; #2,171 of 9,731 by trust here, #1,942 by citations

Whom Navonil Majumder trusts overall

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

1. Diederik P. Kingma
2. Soujanya Poria
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Erik Cambria
6. Alex Krizhevsky
7. Amir Zadeh
8. Ilya Sutskever
9. Jimmy Ba
10. Kaiming He

Who trusts Navonil Majumder overall

1. Soujanya Poria
2. Deepanway Ghosal
3. Devamanyu Hazarika
4. Weizhou Shen
5. Jingwen Hu
6. Peixiang Zhong
7. Ringki Das
8. Duzhen Zhang
9. Ankita Gandhi
10. Mayur Wankhade

Papers from Navonil Majumder's perspective

Ordered by how strongly the researchers Navonil Majumder 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations (2018) — 1,647 citations
• Context-Dependent Sentiment Analysis in User-Generated Videos (2017) — 874 citations
• DialogueRNN: An Attentive RNN for Emotion Detection in Conversations (2018) — 973 citations
• Investigating Gender Bias in BERT (2020) — 125 citations
• A review of deep learning techniques for speech processing (2023) — 354 citations
• Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model (2023) — 205 citations
• Multimodal research in vision and language: A review of current and emerging trends (2022) — 109 citations
• CIDER: Commonsense Inference for Dialogue Explanation and Reasoning (2021) — 29 citations
• COSMIC: COmmonSense knowledge for eMotion Identification in Conversations (2020) — 418 citations
• Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020) — 308 citations
• KinGDOM: Knowledge-Guided DOMain Adaptation for Sentiment Analysis (2020) — 71 citations
• Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances (2019) — 455 citations
• Recent trends in deep learning based personality detection (2019) — 300 citations
• DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation (2019) — 678 citations
• Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling (2018) — 380 citations