Ines Chami
Artificial Intelligence · 2019–2022
A researcher in the Science corpus: 5 papers; trusts 148 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 219 citations into it
- Graph Learning — 77 citations into it
- Computer Vision and Pattern Recognition — 75 citations into it
- Representation & Self-Supervised Learning — 44 citations into it
- Natural Language Processing — 39 citations into it
- Statistical and Nonlinear Physics — 39 citations into it
- Information Systems — 27 citations into it
- Molecular Biology — 23 citations into it
Whom Ines Chami trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yann LeCun
- Maximilian Nickel
- Alex Krizhevsky
- Ilya Sutskever
- Max Welling
- Thomas Kipf
- Ruslan Salakhutdinov
Who trusts Ines Chami overall
- Christopher Morris
- Xiao Wang
- Ramit Sawhney
- Susheel Suresh
- Aaron Lou
- Gaurav Rattan
- Vijay Prakash Dwivedi
- Tuan Le
- Deyu Bo
- Harshit Joshi
Papers from Ines Chami's perspective
Ordered by how strongly the researchers Ines Chami 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- Hyperbolic Graph Convolutional Neural Networks (2019) — 888 citations
- Low-Dimensional Hyperbolic Knowledge Graph Embeddings (2020) — 475 citations
- Ask Me Anything: A simple strategy for prompting language models (2022) — 275 citations
- Can Foundation Models Wrangle Your Data? (2022) — 344 citations
- Machine Learning on Graphs: A Model and Comprehensive Taxonomy (2020) — 347 citations