2 citations · 4 across the 8 of their papers we have counts for
4 papers · 1 filter
Mitigating Forgetting in Low Rank Adaptation
Joanna Sliwa, Frank Schneider, Philipp Hennig +1
Parameter-efficient fine-tuning methods, such as Low-Rank Adaptation (LoRA), enable fast specialization of large pre-trained models to different downstream applications. However, t…
Sketching Low-Rank Plus Diagonal Matrices
Andres Fernandez, Felix Dangel, Philipp Hennig +1
Many relevant machine learning and scientific computing tasks involve high-dimensional linear operators accessible only via costly matrix-vector products. In this context, recent a…
Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods
Andres Fernandez, Frank Schneider, Maren Mahsereci +1
Recently, it has been observed that when training a deep neural net with SGD, the majority of the loss landscape's curvature quickly concentrates in a tiny *top* eigenspace of the…
Accelerating Neural Network Training: An Analysis of the AlgoPerf Competition
Priya Kasimbeg, Frank Schneider, Runa Eschenhagen +11
The goal of the AlgoPerf: Training Algorithms competition is to evaluate practical speed-ups in neural network training achieved solely by improving the underlying training algorit…