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Found 4 papers, 4 papers with code
Parameter-Efficient Instance-Adaptive Neural Video Compression
Inspired by the remarkable success of parameter-efficient fine-tuning on large-scale neural network models, we propose to use a lightweight adapter module that can be easily attached to the pretrained NVCs and fine-tuned for test video sequences.
Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuning
The recent surge in large-scale foundation models has spurred the development of efficient methods for adapting these models to various downstream tasks.
Separable Physics-Informed Neural Networks
Furthermore, we present that SPINN can solve a chaotic (2+1)-d Navier-Stokes equation significantly faster than the best-performing prior method (9 minutes vs 10 hours in a single GPU), maintaining accuracy.
Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks
SPINN operates on a per-axis basis instead of point-wise processing in conventional PINNs, decreasing the number of network forward passes.
