From the 1 of 6 linked papers with an AI index.
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Memory Efficient Tabular Foundation Models
Shuting Luo, Monika Mikhail Kanaan, Cameron Gordon +2
The paper studies how to reduce the memory footprint of tabular foundation models like TabPFN using compression techniques, achieving up to 7.6× memory savings with little performa…
From Tables to Signals: Revealing Spectral Adaptivity in TabPFN
Jianqiao Zheng, Cameron Gordon, Yiping Ji +2
Task-agnostic tabular foundation models such as TabPFN have achieved impressive performance on tabular learning tasks, yet the origins of their inductive biases remain poorly under…
SineLoRA: Sine-Activated Delta Compression
Cameron Gordon, Yiping Ji, Hemanth Saratchandran +2
Resource-constrained weight deployment is a task of immense practical importance. Recently, there has been interest in the specific task of \textit{Delta Compression}, where partie…
Efficient Learning With Sine-Activated Low-rank Matrices
Yiping Ji, Hemanth Saratchandran, Cameron Gordon +2
Low-rank decomposition has emerged as a vital tool for enhancing parameter efficiency in neural network architectures, gaining traction across diverse applications in machine learn…
D'OH: Decoder-Only Random Hypernetworks for Implicit Neural Representations
Cameron Gordon, Lachlan Ewen MacDonald, Hemanth Saratchandran +1
Deep implicit functions have been found to be an effective tool for efficiently encoding all manner of natural signals. Their attractiveness stems from their ability to compactly r…