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Sebastian Bugge Loeschcke

2 accepted papers

2024

Coarse-To-Fine Tensor Trains for Compact Visual Representations

ICML 2024poster

The ability to learn compact, high-quality, and easy-to-optimize representations for visual data is paramount to many applications such as novel view synthesis and 3D reconstruction. Recent work has shown substantial success in using tensor networks to design such compact and high-quality representa…

2024

LoQT: Low-Rank Adapters for Quantized Pretraining

NeurIPS 2024poster

Despite advances using low-rank adapters and quantization, pretraining of large models on consumer hardware has not been possible without model sharding, offloading during training, or per-layer gradient updates. To address these limitations, we propose Low-Rank Adapters for Quantized Training (LoQT…