Florent Krzakala
Artificial Intelligence · 2006–2021
A researcher in the Science corpus: 15 papers; trusts 124 researchers and is trusted by 32 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 111 citations into it; #15,797 of 35,345 by trust here, #8,439 by citations
- Statistics and Probability — 76 citations into it; #7,112 of 12,932 by trust here, #5,857 by citations
- Scaling & Learning Theory — 66 citations into it
- Statistical and Nonlinear Physics — 63 citations into it; #1,187 of 10,127 by trust here, #1,027 by citations
- Computer Networks and Communications — 59 citations into it; #5,167 of 9,886 by trust here, #3,426 by citations
- Computational Mechanics — 56 citations into it; #4,553 of 10,001 by trust here, #3,463 by citations
- Optimization & Training — 39 citations into it
- Computer Vision and Pattern Recognition — 38 citations into it; #15,143 of 21,575 by trust here, #16,787 by citations
Whom Florent Krzakala trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- E.J. Candes
- David L. Donoho
- M. M'ezard
- Sundeep Rangan
- Andrea Montanari
- T. Richardson
- Gitta Kutyniok
- Lenka Zdeborov'a
- Robert Tibshirani
- Judea Pearl
Who trusts Florent Krzakala overall
- Lenka Zdeborov'a
- Sundeep Rangan
- Stefano Spigler
- Jean Barbier
- Mario Geiger
- Giorgio Parisi
- Zhengdao Chen
- Madhu S. Advani
- Mark Kempton
- Benjamin Aubin
Papers from Florent Krzakala's perspective
Ordered by how strongly the researchers Florent Krzakala 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
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- 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
- 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
- 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
- Fundamental limits of detection in the spiked Wigner model (2018) — 56 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
- A Landscape Analysis of Constraint Satisfaction Problems (2007) — 123 citations