Lenka Zdeborov'a

Artificial Intelligence · 2006–2021

A researcher in the Science corpus: 15 papers; trusts 129 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 184 citations into it; #12,813 of 35,345 by trust here, #6,720 by citations
• Statistical and Nonlinear Physics — 87 citations into it; #1,148 of 10,127 by trust here, #1,027 by citations
• Scaling & Learning Theory — 75 citations into it
• Statistics and Probability — 73 citations into it; #7,160 of 12,932 by trust here, #5,857 by citations
• Computer Networks and Communications — 62 citations into it; #8,748 of 9,886 by trust here, #4,950 by citations
• Computer Vision and Pattern Recognition — 59 citations into it
• Computational Mechanics — 58 citations into it; #4,558 of 10,001 by trust here, #3,463 by citations
• Atomic and Molecular Physics, and Optics — 56 citations into it

Whom Lenka Zdeborov'a trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Florent Krzakala
2. Robert Tibshirani
3. M. M'ezard
4. E.J. Candes
5. Geoffrey E. Hinton
6. Béla Bollobás
7. Gitta Kutyniok
8. David L. Donoho
9. Sundeep Rangan
10. Martin J. Wainwright

Who trusts Lenka Zdeborov'a overall

1. Florent Krzakala
2. Stefano Spigler
3. Sebastian Goldt
4. Mario Geiger
5. Benjamin Aubin
6. Sebastian Lee
7. Giorgio Parisi
8. Jean Barbier
9. Jorge Kurchan
10. Sundeep Rangan

Papers from Lenka Zdeborov'a's perspective

Ordered by how strongly the researchers Lenka Zdeborov'a trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Dynamics of stochastic gradient descent for two-layer neural networks in the teacher–student setup (2019) — 186 citations
• Gibbs states and the set of solutions of random constraint satisfaction problems (2006) — 488 citations
• Statistical physics-based reconstruction in compressed sensing (2011) — 318 citations
• Statistical physics of inference: thresholds and algorithms (2015) — 501 citations
• Machine learning and the physical sciences (2019) — 1,895 citations
• The committee machine: computational to statistical gaps in learning a two-layers neural network (2018) — 123 citations
• Phase Transitions in the Coloring of Random Graphs (2007) — 227 citations
• Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model (2021) — 35 citations
• Understanding deep learning is also a job for physicists (2020) — 82 citations
• Modelling the influence of data structure on learning in neural networks (2019) — 48 citations
• Rademacher complexity and spin glasses: A link between the replica and statistical theories of learning (2019) — 14 citations
• Optimal errors and phase transitions in high-dimensional generalized linear models (2017) — 314 citations
• Spectral redemption in clustering sparse networks (2013) — 660 citations
• Probabilistic reconstruction in compressed sensing: algorithms, phase diagrams, and threshold achieving matrices (2012) — 298 citations
• Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications (2011) — 796 citations