5 papers
A Geometric Lens on Physics-Aligned Data Compression
Aleix Segui, Wesley Armour
In AI for Science, physics-informed losses are increasingly used to train learned compressors for scientific data, but their rate-distortion implications remain poorly understood.…
Beyond the Mean: Fisher-Orthogonal Projection for Natural Gradient Descent in Large Batch Training
Yishun Lu, Wesley Armour
Modern GPUs are equipped with large amounts of high-bandwidth memory, enabling them to support mini-batch sizes of up to tens of thousands of training samples. However, most existi…
Adaptive Illumination-Invariant Synergistic Feature Integration in a Stratified Granular Framework for Visible-Infrared Re-Identification
Yuheng Jia, Wesley Armour
Visible-Infrared Person Re-Identification (VI-ReID) plays a crucial role in applications such as search and rescue, infrastructure protection, and nighttime surveillance. However,…
Double-Exponential Increases in Inference Energy: The Cost of the Race for Accuracy
Zeyu Yang, Karel Adamek, Wesley Armour
Deep learning models in computer vision have achieved significant success but pose increasing concerns about energy consumption and sustainability. Despite these concerns, there is…
Part-time Power Measurements: nvidia-smi's Lack of Attention
Zeyu Yang, Karel Adamek, Wesley Armour
The GPU has emerged as the go-to accelerator for high throughput and parallel workloads, spanning scientific simulations to AI, thanks to its performance and power efficiency. Give…