263 skills found · Page 4 of 9
trendmicro / Vision One MCP ServerThe Trend Vision One Model Context Protocol (MCP) Server enables natural language interaction between your favourite AI tooling and the Trend Vision One web APIs. This allows users to harness the power of Large Language Models (LLM) to interpret and respond to security events.
naotoo1 / Beyond Neural ScalingImplementation of Beyond Neural Scaling beating power laws for deep models and prototype-based models
willbakst / Pytorch LatticeA PyTorch implementation of constrained optimization and modeling techniques
deepfx / NetlensA toolkit for interpreting and analyzing neural networks (vision)
cwangrun / ST ProtoPNet[ICCV 2023] Learning Support and Trivial Prototypes for Interpretable Image Classification
CORE-Robotics-Lab / Interpretable DDTS AISTATS2020Public code for implementation and experiments with differentiable decision trees.
ChatterjeeAyan / AI BindInterpretable AI pipeline improving binding predictions for novel protein targets and ligands
tldraw / Agent TemplateEnable AI agents to interpret and interact with canvas drawings and elements.
guidelabs / InfembedFind the samples, in the test data, on which your (generative) model makes mistakes.
AbdelStark / Awesome AI SafetyA curated list of AI safety resources: alignment, interpretability, governance, verification, and responsible deployment of frontier AI systems.
oslabs-beta / WatchtowerMonitor and analyze DynamoDB metrics and interpret this data with amazon's bedrock AI.
ataturhan21 / Titanic Survival PredictionA comprehensive solution for the Kaggle Titanic Challenge, featuring advanced data exploration, feature engineering, model training, and explainable AI techniques. Includes Logistic Regression, RandomForest, XGBoost, and Stacked Ensembles with SHAP and permutation importance for model interpretability.
koriavinash1 / BioExpExplainability of Deep Learning Models
AmirhosseinHonardoust / Algorithmic Empath Human FallibilityA deep exploration of Algorithmic Empathy, the next frontier in AI understanding. This project examines how machines can learn from human fallibility, model disagreement, and align with moral reasoning. It blends psychology, fairness metrics, interpretability, and co-learning design into one framework for humane intelligence.
Naviden / Introduction To XAIThis repository provides a range of practical examples and educational resources for exploring the field of Explainable AI (XAI). You'll find examples using tools like LIME and SHAP to interpret machine learning model predictions, making the decision-making processes of complex algorithms more transparent and accessible
ktnCodes / Icm TemplateA model-agnostic template for organizing AI workflows using folder structure — based on the Interpretable Context Methodology
ottenbreit-data-science / AplrAPLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.
12wang3 / MllpThe code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
jphall663 / Hc MlSlides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
maragraziani / InterpretAI DigiPathHands-on Sessions 1 and 2 at the Building Interpretable AI for Digital Pathology AMLD workshop 2021
