The paper claims that in-context learning with finite example sets can approximate supervised fine-tuning in transformers, but the proof assumes the very approximation it sets out to establish.
A nested MLMC framework for efficient simulations on FPGAs
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Multilevel Monte Carlo (MLMC) reduces the total computational cost of financial option pricing by combining SDE approximations with multiple resolutions. This paper explores a further avenue for reducing cost and improving power efficiency through the use of low precision calculations on configurable hardware devices such as Field-Programmable Gate Arrays (FPGAs). We propose a new framework that exploits approximate random variables and fixed-point operations with optimised precision to generate most SDE paths with a lower cost and reduce the overall cost of the MLMC framework. We first discuss several methods for the cheap generation of approximate random Normal increments. To set the bit-width of variables in the path generation we then propose a rounding error model and optimise the precision of all variables on each MLMC level. With these key improvements, our proposed framework offers higher computational savings than the existing mixed-precision MLMC frameworks.
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques
The paper claims that in-context learning with finite example sets can approximate supervised fine-tuning in transformers, but the proof assumes the very approximation it sets out to establish.