8 citations · 16 across the 4 of their papers we have counts for
5 papers
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering
Ricky T. Q. Chen, Dami Choi, Lukas Balles +2
Standard first-order stochastic optimization algorithms base their updates solely on the average mini-batch gradient, and it has been shown that tracking additional quantities such…
The Geometry of Sign Gradient Descent
Lukas Balles, Fabian Pedregosa, Nicolas Le Roux
Sign-based optimization methods have become popular in machine learning due to their favorable communication cost in distributed optimization and their surprisingly good performanc…
Limitations of the Empirical Fisher Approximation for Natural Gradient Descent
Frederik Kunstner, Lukas Balles, Philipp Hennig
Natural gradient descent, which preconditions a gradient descent update with the Fisher information matrix of the underlying statistical model, is a way to capture partial second-o…
DeepOBS: A Deep Learning Optimizer Benchmark Suite
Frank Schneider, Lukas Balles, Philipp Hennig
Because the choice and tuning of the optimizer affects the speed, and ultimately the performance of deep learning, there is significant past and recent research in this area. Yet,…
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
Anurag Ranjan, Varun Jampani, Lukas Balles +4
We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation…