3 citations · 3 across the 5 of their papers we have counts for
5 papers
On Neural BRDFs: A Thorough Comparison of State-of-the-Art Approaches
Florian Hofherr, Bjoern Haefner, Daniel Cremers
The bidirectional reflectance distribution function (BRDF) is an essential tool to capture the complex interaction of light and matter. Recently, several works have employed neural…
Variational Low-Rank Adaptation Using IVON
Bai Cong, Nico Daheim, Yuesong Shen +4
We show that variational learning can significantly improve the accuracy and calibration of Low-Rank Adaptation (LoRA) without a substantial increase in the cost. We replace AdamW…
Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo
Mohammed Brahimi, Bjoern Haefner, Zhenzhang Ye +2
Neural approaches have shown a significant progress on camera-based reconstruction. But they require either a fairly dense sampling of the viewing sphere, or pre-training on an exi…
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization
Zhenzhang Ye, Gabriel Peyré, Daniel Cremers +1
Bilevel optimization aims to optimize an outer objective function that depends on the solution to an inner optimization problem. It is routinely used in Machine Learning, notably f…
Variational Learning is Effective for Large Deep Networks
Yuesong Shen, Nico Daheim, Bai Cong +8
We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks. We show that an optimizer called Improved Variati…