Popular GitHub Repositories for Hausdorff Measure

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Production-Grade ML System for Automated Unit of Measure Error Detection | 88-92% Accuracy | 94% Autonomy | KNIME Workflow

Fast underwater image enhancement for Improved Visual Perception. #TensorFlow #PyTorch #RAL2020

If you can measure it, consider it predicted

Identify bias and measure fairness of your data

Volume Under the Surface: Accuracy Measure for Time Series Anomaly Detection

Word2Vec based similarity measure of mass spectrometry data.

Simulate collaborative ML scenarios, experiment multi-partner learning approaches and measure respective contributions of different datasets to model performance.

Measure the sentiment towards a word, name or sentence on social networks

Parallel random matrix tools and complexity for deep learning

A simple Python tool to measure the performance of ONNX models.

SePer is an accurate / fast / free-of-API metric to measure document quality via information gain

Jointly Measuring Diversity and Quality in Text Generation Models

Feedback Forensics: An open-source toolkit to measure AI personality

EvalBench is a flexible framework designed to measure the quality of generative AI (GenAI) workflows around database specific tasks.

Fast, cross-platform PowerShell module to find files & folders, grep file content, and measure sizes—Linux-style find/grep/du made simple.

A Python package to compute HONEST, a score to measure hurtful sentence completions in language models. Published at NAACL 2021.

Political parties emphasize different issues in their public communication efforts to address the topics of the day and to strengthen their policy profiles. Here, we develop and evaluate models to automatically classify parties' press releases into issue categories to dynamically measure issue attention.

Computed tomography (CT) is one of the most widely used radiography exams worldwide for different diagnostic applications. However, CT scans involve ioniz- ing radiational exposure, which raises health concerns. Counter-intuitively, low- ering the adequate CT dose level introduces noise and reduces the image quality, which may impact clinical diagnosis. This study analyzed the feasibility of using a conditional generative adversarial network (cGAN) called pix2pix to learn the mapping from low dose to high dose CT images under different conditions. This study included 270 three-dimensional (3D) CT scan images (85,050 slices) from 90 unique patients imaged virtually using virtual imaging trials platform for model development and testing. Performance was reported as peak signal-to-noise ra- tio (PSNR) and structural similarity index measure