Afonso S. Bandeira
Artificial Intelligence · 2012–2021
A researcher in the Science corpus: 9 papers; trusts 73 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 84 citations into it; #19,683 of 35,345 by trust here, #25,040 by citations
- Statistics and Probability — 43 citations into it; #6,392 of 12,932 by trust here, #6,853 by citations
- Computer Vision and Pattern Recognition — 42 citations into it; #16,830 of 21,575 by trust here, #18,782 by citations
- Computational Mechanics — 37 citations into it; #5,299 of 10,001 by trust here, #6,287 by citations
- Optimization & Training — 37 citations into it; #2,564 of 2,601 by trust here, #1,715 by citations
- Statistical Learning Theory — 28 citations into it
- Computational Theory and Mathematics — 22 citations into it; #4,971 of 12,182 by trust here, #4,199 by citations
- Mathematical Physics — 20 citations into it
Whom Afonso S. Bandeira trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Gábor Lugosi
- Terence Tao
- Roman Vershynin
- Stéphane Boucheron
- Francis R. Bach
- Joel A. Tropp
- Ramon van Handel
- Michel Talagrand
- Uffe Haagerup
- E.J. Candes
Who trusts Afonso S. Bandeira overall
- Ramon van Handel
- E.J. Candes
- Andreas M. Tillmann
- Emmanuel Abbe
- Joel A. Tropp
- Yue Wang
- Nicholas A. Cook
- Florent Benaych-Georges
- Noam Wies
- Xiaodong Li
Papers from Afonso S. Bandeira's perspective
Ordered by how strongly the researchers Afonso S. Bandeira 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks (2017) — 127 citations
- Sharp nonasymptotic bounds on the norm of random matrices with independent entries (2014) — 303 citations
- Saving phase: Injectivity and stability for phase retrieval (2013) — 343 citations
- Matrix concentration inequalities and free probability (2021) — 100 citations
- Spurious Valleys in One-hidden-layer Neural Network Optimization Landscapes (2019) — 87 citations
- Neural Networks with Finite Intrinsic Dimension have no Spurious Valleys (2018) — 60 citations
- SE-Sync: A certifiably correct algorithm for synchronization over the special Euclidean group (2016) — 399 citations
- Exact Recovery in the Stochastic Block Model (2014) — 615 citations
- Certifying the Restricted Isometry Property is Hard (2012) — 248 citations