3 accepted papers
Activations have become the primary memory bottleneck in large-batch LLM training. However, existing compression methods fail to exploit structure information of activations, resulting in slow convergence or limited compression. To address this, we bridge the relationship between the algorithm’s fas…
Many real-world data are inherently multi-dimensional, e.g., color images, videos, and hyperspectral images. How to effectively and compactly represent these multi-dimensional data within a unified framework is an important pursuit. Previous methods focus on tensor factorizations, convolutional netw…
Online tensor decompositions are powerful and proven techniques that address the challenges in processing high-velocity streaming tensor data, such as traffic flow and weather system. The main aim of this work is to propose a novel online functional tensor decomposition (OFTD) framework, which repre…