Nazanin Alipourfard
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 4 papers; trusts 11 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 22 citations into it; #18,642 of 35,345 by trust here, #12,244 by citations
- Sociology and Political Science — 12 citations into it
- Statistical and Nonlinear Physics — 12 citations into it
- Statistics and Probability — 10 citations into it; #6,781 of 12,932 by trust here, #9,233 by citations
- Information Systems — 8 citations into it; #3,297 of 8,771 by trust here, #6,195 by citations
- Social Sciences & Humanities — 6 citations into it; #180 of 309 by trust here, #245 by citations
- Communication — 5 citations into it; #1,885 of 3,460 by trust here, #2,850 by citations
- Computer Vision and Pattern Recognition — 5 citations into it
Whom Nazanin Alipourfard trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kristina Lerman
- C. Blyth
- Emilio Ferrara
- Elias Bareinboim
- Burr Settles
- George Casella
- Daniel A. McFarland
- Judea Pearl
- Brendan Meeder
- James W. Vaupel
Who trusts Nazanin Alipourfard overall
- Kristina Lerman
- Ninareh Mehrabi
- Fred Morstatter
- Peter G. Fennell
- Nripsuta Ani Saxena
- Erwan Le Merrer
- Ruocheng Guo
Papers from Nazanin Alipourfard's perspective
Ordered by how strongly the researchers Nazanin Alipourfard trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Dropout: a simple way to prevent neural networks from overfitting
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Semi-Supervised Classification with Graph Convolutional Networks (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Using Simpson's Paradox to Discover Interesting Patterns in Behavioral Data (2018) — 22 citations
- Can you Trust the Trend?: Discovering Simpson's Paradoxes in Social Data (2018) — 36 citations
- MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing (2019) — 1,157 citations
- Dynamics of Content Quality in Collaborative Knowledge Production (2017) — 15 citations