Novi Quadrianto
Artificial Intelligence · 2008–2017
A researcher in the Science corpus: 10 papers; trusts 297 researchers and is trusted by 55 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 73 citations into it; #7,249 of 35,345 by trust here, #9,465 by citations
- Computer Vision and Pattern Recognition — 42 citations into it; #4,522 of 21,575 by trust here, #8,612 by citations
- Representation & Self-Supervised Learning — 31 citations into it; #2,674 of 3,145 by trust here, #2,805 by citations
- Statistical Learning Theory — 22 citations into it
- Statistics and Probability — 14 citations into it
- Safety, Robustness & Interpretability — 11 citations into it; #898 of 2,251 by trust here, #1,994 by citations
- Signal Processing — 11 citations into it; #4,387 of 7,572 by trust here, #5,570 by citations
- Optimization & Training — 10 citations into it
Whom Novi Quadrianto trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Bernhard Schölkopf
- Alex Smola
- Geoffrey E. Hinton
- Vladimir Naumovich Vapnik
- Ruslan Salakhutdinov
- David G. Lowe
- Alan L. Yuille
- Arthur Gretton
- Alex Krizhevsky
- Yoshua Bengio
Who trusts Novi Quadrianto overall
- Giorgio Patrini
- Kaiye Wang
- Christoph H. Lampert
- Viktoriia Sharmanska
- Luca Oneto
- Shruti Bhargava
- Chang Xu
- Feng Zhou
- Guiguang Ding
- Amélie Royer
Papers from Novi Quadrianto's perspective
Ordered by how strongly the researchers Novi Quadrianto 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)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Augmented Attribute Representations (2012) — 90 citations
- Learning to Rank Using Privileged Information (2013) — 199 citations
- Learning from Labeled and Unlabeled Data (2010) — 355 citations
- Estimating labels from label proportions (2008) — 276 citations
- Kernelized Sorting (2008) — 110 citations
- Recycling Privileged Learning and Distribution Matching for Fairness (2017) — 87 citations
- Learning Multi-View Neighborhood Preserving Projections (2011) — 118 citations
- Multitask Learning without Label Correspondences (2010) — 44 citations
- Learning in Logic (2010) — 104 citations
- Distribution Matching for Transduction (2009) — 48 citations