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Found 5 papers, 1 papers with code
Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation
Despite the successes of large language models (LLMs), they exhibit significant drawbacks, particularly when processing long contexts.
FastSR-NeRF: Improving NeRF Efficiency on Consumer Devices with A Simple Super-Resolution Pipeline
Compared to existing NeRF+SR methods, our pipeline mitigates the SR computing overhead and can be trained up to 23x faster, making it feasible to run on consumer devices such as the Apple MacBook.
Do Compressed LLMs Forget Knowledge? An Experimental Study with Practical Implications
We start by proposing two conjectures on the nature of the damage: one is certain knowledge being forgotten (or erased) after LLM compression, hence necessitating the compressed model to (re)learn from data with additional parameters; the other presumes that knowledge is internally displaced and hence one requires merely "inference re-direction" with input-side augmentation such as prompting, to recover the knowledge-related performance.
On the Efficacy of Multi-scale Data Samplers for Vision Applications
Multi-scale resolution training has seen an increased adoption across multiple vision tasks, including classification and detection.
SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks
In this paper, we perform an empirical evaluation on methods for sharing parameters in isotropic networks (SPIN).
