Vlad Niculae
Artificial Intelligence · 2012–2020
A researcher in the Science corpus: 7 papers; trusts 189 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 113 citations into it; #8,642 of 35,345 by trust here, #8,999 by citations
- Natural Language Processing — 57 citations into it; #2,902 of 5,688 by trust here, #3,638 by citations
- Computer Vision and Pattern Recognition — 47 citations into it; #10,399 of 21,575 by trust here, #15,529 by citations
- Optimization & Training — 34 citations into it
- Model Architectures — 28 citations into it; #866 of 2,278 by trust here, #1,215 by citations
- Computational Mechanics — 18 citations into it
- Evaluation & Benchmarks — 18 citations into it
- Computational Theory and Mathematics — 11 citations into it
Whom Vlad Niculae trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- André F. T. Martins
- Yoshua Bengio
- Diederik P. Kingma
- Geoffrey E. Hinton
- Michael Collins
- Alex Krizhevsky
- Ilya Sutskever
- Dzmitry Bahdanau
- Kaiming He
- Yann LeCun
Who trusts Vlad Niculae overall
- Ben Peters
- Mathieu Blondel
- Joris Baan
- André F. T. Martins
- Derya Soydaner
- Sergei Popov
- Zi-Yi Dou
- Gonçalo M. Correia
- Maartje ter Hoeve
- William Fedus
Papers from Vlad Niculae's perspective
Ordered by how strongly the researchers Vlad Niculae 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- 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
- Adaptively Sparse Transformers (2019) — 304 citations
- SparseMAP: Differentiable Sparse Structured Inference (2018) — 133 citations
- Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions (2016) — 435 citations
- Sparse Sequence-to-Sequence Models (2019) — 301 citations
- Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms (2018) — 55 citations
- Learning Binary Trees via Sparse Relaxation (2020) — 4 citations
- The Romanian Neuter Examined Through A Two-Gender N-Gram Classification System (2012) — 5 citations