His scientific interests lie mostly in Mathematical optimization, Artificial intelligence, Convex optimization, Pattern recognition and Machine learning. His study in Mathematical optimization is interdisciplinary in nature, drawing from both Regularization, Algorithm, Set, Convex function and Rate of convergence. The concepts of his Convex optimization study are interwoven with issues in Quadratic programming and Unsupervised learning.
His research in Pattern recognition intersects with topics in Contextual image classification and Iterative reconstruction, Computer vision. His Machine learning study combines topics from a wide range of disciplines, such as Multi-task learning, Feature extraction and Pooling. His research integrates issues of Matrix decomposition, Online machine learning and Theoretical computer science in his study of K-SVD.
His main research concerns Artificial intelligence, Mathematical optimization, Algorithm, Applied mathematics and Convex optimization. His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition. His work carried out in the field of Pattern recognition brings together such families of science as Probabilistic logic and Computer vision.
His Submodular set function, Optimization problem and Stochastic optimization study in the realm of Mathematical optimization connects with subjects such as Convexity. His Algorithm study incorporates themes from Kernel, Artificial neural network, Matrix, Independent component analysis and Function. The study incorporates disciplines such as Stochastic gradient descent, Convergence, Rate of convergence, Regularization and Gradient descent in addition to Applied mathematics.
Francis Bach mainly investigates Algorithm, Applied mathematics, Artificial intelligence, Rate of convergence and Regularization. His Algorithm research includes themes of Artificial neural network, Independent component analysis, Simple, Nonlinear system and Function. His Applied mathematics research is multidisciplinary, incorporating elements of Stochastic gradient descent, Leverage, Convergence, Gradient descent and Convex optimization.
His Convex optimization study which covers Mathematical optimization that intersects with Computation and Bregman divergence. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Key. Francis Bach interconnects Convex function and Gradient method in the investigation of issues within Rate of convergence.
His primary areas of investigation include Algorithm, Applied mathematics, Convex function, Convergence and Rate of convergence. The various areas that Francis Bach examines in his Algorithm study include Artificial neural network, Simple, Neural coding, Nonlinear system and Differentiable function. He has included themes like Iterated function, Stochastic gradient descent, Regression, Norm and Least squares in his Applied mathematics study.
His Convex function research incorporates elements of Sampling, Upper and lower bounds, Graph and Convex optimization. He combines subjects such as Function and Smoothness with his study of Convex optimization. His study focuses on the intersection of Stochastic optimization and fields such as Machine learning with connections in the field of Artificial intelligence.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Online Learning for Matrix Factorization and Sparse Coding
Julien Mairal;Francis Bach;Jean Ponce;Guillermo Sapiro.
Journal of Machine Learning Research (2010)
Online dictionary learning for sparse coding
Julien Mairal;Francis Bach;Jean Ponce;Guillermo Sapiro.
international conference on machine learning (2009)
Kernel independent component analysis
Francis R. Bach;Michael I. Jordan.
Journal of Machine Learning Research (2003)
Multiple kernel learning, conic duality, and the SMO algorithm
Francis R. Bach;Gert R. G. Lanckriet;Michael I. Jordan.
international conference on machine learning (2004)
Non-local sparse models for image restoration
Julien Mairal;Francis Bach;Jean Ponce;Guillermo Sapiro.
international conference on computer vision (2009)
Online Learning for Latent Dirichlet Allocation
Matthew Hoffman;Francis R. Bach;David M. Blei.
neural information processing systems (2010)
Supervised Dictionary Learning
Julien Mairal;Jean Ponce;Guillermo Sapiro;Andrew Zisserman.
neural information processing systems (2008)
Learning mid-level features for recognition
Y-Lan Boureau;Francis Bach;Yann LeCun;Jean Ponce.
computer vision and pattern recognition (2010)
Discriminative learned dictionaries for local image analysis
J. Mairal;F. Bach;J. Ponce;G. Sapiro.
computer vision and pattern recognition (2008)
Task-Driven Dictionary Learning
J. Mairal;F. Bach;J. Ponce.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)
Profile was last updated on December 6th, 2021.
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