nannyml: post-deployment data science in python
Comparing different performance estimation methods for time series forecasting tasks
A codebase accompanying the paper "Improving Performance Estimation for FPGA-based Accelerators for Convolutional Neural Networks", by Ferianc et al. presented at ARC'2020
Code for "Distance Matters for Improving Performance Estimation Under Covariate Shift", ICCV Workshop on Uncertainty Quantification 2023, Roschewitz & Glocker.
Exact dimension-gap results and verification supplements for prescribed gradient-descent schedules
Help the bank monitoring their fraud detection model and figuring out why it's not performing as expected.
Proof-carrying joint performance estimation for two-oracle cost certification
HLS performance estimation with LLMs
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