What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Algorithm
- Statistics
His main research concerns Algorithm, Statistical physics, Message passing, Belief propagation and Quantum annealing.
Florent Krzakala specializes in Algorithm, namely Compressed sensing.
His research in Statistical physics intersects with topics in Simulated annealing, Generalization, Energy landscape and Adiabatic process.
His Message passing research is multidisciplinary, incorporating elements of Matrix decomposition, Low-rank approximation and Matrix.
His studies examine the connections between Matrix and genetics, as well as such issues in Applied mathematics, with regards to Computational complexity theory and Random graph.
His Belief propagation research is multidisciplinary, incorporating perspectives in Discrete mathematics, Stochastic block model and Cavity method.
His most cited work include:
- Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications. (584 citations)
- Spectral redemption in clustering sparse networks (488 citations)
- Gibbs states and the set of solutions of random constraint satisfaction problems (397 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Algorithm, Message passing, Statistical physics, Compressed sensing and Spin glass.
His Algorithm research is mostly focused on the topic Belief propagation.
Florent Krzakala usually deals with Message passing and limits it to topics linked to Superposition principle and Hadamard transform.
His work on Cavity method as part of general Statistical physics study is frequently connected to Amorphous solid, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Compressed sensing study combines topics in areas such as MNIST database, Prior probability and Robustness.
His Spin glass research incorporates themes from Symmetry breaking and Bethe lattice.
He most often published in these fields:
- Algorithm (41.25%)
- Message passing (25.00%)
- Statistical physics (20.31%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial neural network (12.50%)
- Algorithm (41.25%)
- Matrix (16.56%)
In recent papers he was focusing on the following fields of study:
Florent Krzakala mostly deals with Artificial neural network, Algorithm, Matrix, Limit and Gaussian.
His Artificial neural network study combines topics in areas such as Training set and Data set.
Florent Krzakala studies Compressed sensing, a branch of Algorithm.
Florent Krzakala interconnects Spurious relationship, Random matrix and Maxima and minima in the investigation of issues within Matrix.
Florent Krzakala combines subjects such as Statistical theory and Generalization with his study of Limit.
Florent Krzakala has researched Gaussian in several fields, including Manifold, Linear regression, Applied mathematics and Interpolation.
Between 2018 and 2021, his most popular works were:
- Optimal errors and phase transitions in high-dimensional generalized linear models (97 citations)
- Modelling the influence of data structure on learning in neural networks: the hidden manifold model (30 citations)
- Generalisation error in learning with random features and the hidden manifold model (25 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Statistics
- Algorithm
His primary scientific interests are in Artificial neural network, Algorithm, Gaussian, Artificial intelligence and Limit.
His studies in Algorithm integrate themes in fields like Matrix, Statistical inference and Prior probability.
His Statistical inference research is multidisciplinary, relying on both Random matrix, Message passing and Inference.
His research integrates issues of Linear regression, Mutual information, Interpolation, Manifold and Applied mathematics in his study of Gaussian.
Florent Krzakala has included themes like Perceptron and Invariant in his Limit study.
His work on Cavity method as part of his general Statistical physics study is frequently connected to Contiguity, thereby bridging the divide between different branches of science.
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