Francisca Adoma Acheampong

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 51 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it
• Natural Language Processing — 36 citations into it
• Experimental and Cognitive Psychology — 11 citations into it
• Large Language Models — 11 citations into it
• Social Psychology — 8 citations into it
• Information Systems — 7 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Francisca Adoma Acheampong trusts overall

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

1. Erik Cambria
2. Saif M. Mohammad
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Alec Radford
7. Richard Socher
8. Ilya Sutskever
9. Alex Krizhevsky
10. Tomas Mikolov

Who trusts Francisca Adoma Acheampong overall

1. Saidul Islam
2. Mayur Wankhade

Papers from Francisca Adoma Acheampong's perspective

Ordered by how strongly the researchers Francisca Adoma Acheampong 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Transformer models for text-based emotion detection: a review of BERT-based approaches (2021) — 598 citations
• Text‐based emotion detection: Advances, challenges, and opportunities (2020) — 349 citations