98 citations · 313 across the 27 of their papers we have counts for
28 papers
Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models?
Jacob Nielsen, Peter Schneider-Kamp, Lukas Galke
Large language models (LLMs) require immense resources for training and inference. Quantization, a technique that reduces the precision of model parameters, offers a promising solu…
DeToNATION: Decoupled Torch Network-Aware Training on Interlinked Online Nodes
Mogens Henrik From, Jacob Nielsen, Lukas Galke Poech +1
Training large neural network models requires extensive computational resources, often distributed across several nodes and accelerators. Recent findings suggest that it may be suf…
When are 1.58 bits enough? A Bottom-up Exploration of BitNet Quantization
Jacob Nielsen, Lukas Galke, Peter Schneider-Kamp
Contemporary machine learning models, such as language models, are powerful, but come with immense resource requirements both at training and inference time. It has been shown that…
BitNet b1.58 Reloaded: State-of-the-art Performance Also on Smaller Networks
Jacob Nielsen, Peter Schneider-Kamp
Recently proposed methods for 1-bit and 1.58-bit quantization aware training investigate the performance and behavior of these methods in the context of large language models, find…
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Marcos V. Conde, Radu Timofte, Yibin Huang +40
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP). Numerous low-level…
DRHDR: A Dual branch Residual Network for Multi-Bracket High Dynamic Range Imaging
Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp +1
We introduce DRHDR, a Dual branch Residual Convolutional Neural Network for Multi-Bracket HDR Imaging. To address the challenges of fusing multiple brackets from dynamic scenes, we…