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.
Gradient descent optimization curves: consecutive convexity violations, convex and strongly convex optimization, PEPit performance estimation, exact certificates, counterexamples and selected Lean 4 proofs. | انكسارات التحدّب المتتالية في منحنيات التحسين للانحدار التدريجي
CaliPPer: Quantifying, predicting and improving AI model performance for binding prediction
HLS performance estimation with LLMs
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
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