Newsha Ardalani
Artificial Intelligence · 2017–2021
A researcher in the Science corpus: 5 papers; trusts 42 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 100 citations into it; #34,918 of 35,345 by trust here, #7,945 by citations
- Computer Vision and Pattern Recognition — 66 citations into it
- Optimization & Training — 32 citations into it
- Natural Language Processing — 29 citations into it
- Computer Networks and Communications — 28 citations into it
- Electrical and Electronic Engineering — 26 citations into it
- Evaluation & Benchmarks — 23 citations into it
- ML Systems & Hardware — 22 citations into it
Whom Newsha Ardalani trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Shun-ichi Amari
- Yoshua Bengio
- Alex Graves
- Ilya Sutskever
- Thang Luong
- Diederik P. Kingma
- Oriol Vinyals
Who trusts Newsha Ardalani overall
Papers from Newsha Ardalani's perspective
Ordered by how strongly the researchers Newsha Ardalani 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
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Going deeper with convolutions
Their papers
- Deep Learning Scaling is Predictable, Empirically (2017) — 1,031 citations
- Beyond human-level accuracy: computational challenges in deep learning (2019) — 87 citations
- Sustainable AI: Environmental Implications, Challenges and Opportunities (2021) — 662 citations
- Time and the Value of Data (2020) — 21 citations
- Empirically Characterizing Overparameterization Impact on Convergence (2018) — 4 citations