Computationally lightweight classifiers with frequentist bounds on predictions
Computationally lightweight classifiers with frequentist bounds on predictions explores A computationally efficient classifier for safety-critical applications that provides actionable uncertainty bounds, suitable for real-time medical monitoring.. Commercial viability score: 5/10 in Medical AI.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisBeyond Accuracy: Reliability and Uncertainty Estimation in Convolutional Neural NetworksViability: 4.0
- Builds On ThisOptimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual functionViability: 2.0
- Builds On ThisDirect Learning of Calibration-Aware Uncertainty for Neural PDE SurrogatesViability: 2.0
- Prior WorkUsing predefined vector systems to speed up neural network multimillion class classificationViability: 5.0
- Higher ViabilityECG Classification on PTB-XL: A Data-Centric Approach with Simplified CNN-VAE