[D] Adaptive Low-Rank Hypernetworks (ALRH)

The Adaptive Low-Rank Hypernetworks approach involves inserting two additional neural networks into the attention layer of a transformer model. These neural networks would generate low-rank approximations of the key and value matrices. The primary goal is to achieve both computational efficiency and flexible adaptation to new data.

1. Low-Rank Decomposition: Perform a low-rank decomposition on the key and value weight matrices of the transformer model using techniques like Singular Value Decomposition (SVD) or Truncated SVD. This will result in a …

attention computational data efficiency low machinelearning networks neural networks the key transformer transformer model value