Devamanyu Hazarika
Artificial Intelligence · 2016–2023
A researcher in the Science corpus: 16 papers; trusts 964 researchers and is trusted by 47 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 803 citations into it; #4,066 of 35,345 by trust here, #3,183 by citations
- Natural Language Processing — 433 citations into it; #2,247 of 5,688 by trust here, #1,407 by citations
- Computer Vision and Pattern Recognition — 382 citations into it; #7,251 of 21,575 by trust here, #7,852 by citations
- Multimodal Learning — 163 citations into it
- Representation & Self-Supervised Learning — 105 citations into it
- Model Architectures — 90 citations into it
- Experimental and Cognitive Psychology — 86 citations into it; #1,757 of 9,731 by trust here, #1,493 by citations
- Signal Processing — 58 citations into it
Whom Devamanyu Hazarika trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Diederik P. Kingma
- Soujanya Poria
- Ilya Sutskever
- Alex Krizhevsky
- Richard Socher
- Tomas Mikolov
- Alex Graves
- Sepp Hochreiter
Who trusts Devamanyu Hazarika overall
- Soujanya Poria
- Navonil Majumder
- Erik Cambria
- Deepanway Ghosal
- Jingwen Hu
- Ankita Gandhi
- Rada Mihalcea
- Alexander Gelbukh
- Wenmeng Yu
- Duzhen Zhang
Papers from Devamanyu Hazarika's perspective
Ordered by how strongly the researchers Devamanyu Hazarika 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- 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
- Recent Trends in Deep Learning Based Natural Language Processing (2017) — 3,089 citations
- Context-Dependent Sentiment Analysis in User-Generated Videos (2017) — 874 citations
- A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks (2016) — 389 citations
- DialogueRNN: An Attentive RNN for Emotion Detection in Conversations (2018) — 973 citations
- Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos (2018) — 473 citations
- SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings (2018) — 408 citations
- ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection (2018) — 416 citations
- MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis (2020) — 1,255 citations
- Using In-Context Learning to Improve Dialogue Safety (2023) — 60 citations
- Multimodal research in vision and language: A review of current and emerging trends (2022) — 109 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
- Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper) (2019) — 400 citations
- Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling (2018) — 380 citations
- Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018)