23 citations · 44 across the 7 of their papers we have counts for
7 papers
Scalable Adaptive Stochastic Optimization Using Random Projections
Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher +2
Adaptive stochastic gradient methods such as AdaGrad have gained popularity in particular for training deep neural networks. The most commonly used and studied variant maintains a…
Mean-Field Variational Inference for Gradient Matching with Gaussian Processes
Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann
Gradient matching with Gaussian processes is a promising tool for learning parameters of ordinary differential equations (ODE's). The essence of gradient matching is to model the p…
Model Selection for Gaussian Process Regression by Approximation Set Coding
Benjamin Fischer, Nico Gorbach, Stefan Bauer +2
Gaussian processes are powerful, yet analytically tractable models for supervised learning. A Gaussian process is characterized by a mean function and a covariance function (kernel…
Greedy MAXCUT Algorithms and their Information Content
Yatao Bian, Alexey Gronskiy, Joachim M. Buhmann
MAXCUT defines a classical NP-hard problem for graph partitioning and it serves as a typical case of the symmetric non-monotone Unconstrained Submodular Maximization (USM) problem.…
Kickback cuts Backprop's red-tape: Biologically plausible credit assignment in neural networks
David Balduzzi, Hastagiri Vanchinathan, Joachim Buhmann
Error backpropagation is an extremely effective algorithm for assigning credit in artificial neural networks. However, weight updates under Backprop depend on lengthy recursive com…
Fast and Robust Least Squares Estimation in Corrupted Linear Models
Brian McWilliams, Gabriel Krummenacher, Mario Lucic +1
Subsampling methods have been recently proposed to speed up least squares estimation in large scale settings. However, these algorithms are typically not robust to outliers or corr…