Lukas Balles

According to our database1, Lukas Balles authored at least 20 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
On the Choice of Learning Rate for Local SGD.
Trans. Mach. Learn. Res., 2024

u-μP: The Unit-Scaled Maximal Update Parametrization.
CoRR, 2024

Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need.
CoRR, 2024

2023
A Negative Result on Gradient Matching for Selective Backprop.
CoRR, 2023

Continual Learning with Low Rank Adaptation.
CoRR, 2023

Renate: A Library for Real-World Continual Learning.
CoRR, 2023

PASHA: Efficient HPO and NAS with Progressive Resource Allocation.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
PASHA: Efficient HPO with Progressive Resource Allocation.
CoRR, 2022

Gradient-Matching Coresets for Rehearsal-Based Continual Learning.
CoRR, 2022

2021
Gradient-matching coresets for continual learning.
CoRR, 2021

2020
The Geometry of Sign Gradient Descent.
CoRR, 2020

Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering.
Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

2019
Limitations of the Empirical Fisher Approximation.
CoRR, 2019

Limitations of the empirical Fisher approximation for natural gradient descent.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

DeepOBS: A Deep Learning Optimizer Benchmark Suite.
Proceedings of the 7th International Conference on Learning Representations, 2019

Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Early Stopping without a Validation Set.
CoRR, 2017

Follow the Signs for Robust Stochastic Optimization.
CoRR, 2017

Coupling Adaptive Batch Sizes with Learning Rates.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017


  Loading...