Momentum SVGD-EM for Accelerated Maximum Marginal Likelihood Estimation
Momentum SVGD-EM for Accelerated Maximum Marginal Likelihood Estimation explores Accelerated maximum marginal likelihood estimation using Stein Variational Gradient Descent for faster convergence in probabilistic models.. Commercial viability score: 7/10 in Probabilistic Modeling.
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 ThisRAMPAGE: RAndomized Mid-Point for debiAsed Gradient ExtrapolationViability: 3.0
- Builds On ThisLearning Where the Physics Is: Probabilistic Adaptive Sampling for Stiff PDEsViability: 1.0
- Builds On ThisDeltaEvolve: Accelerating Scientific Discovery through Momentum-Driven EvolutionViability: 3.0
- Builds On ThisOptEMA: Adaptive Exponential Moving Average for Stochastic Optimization with Zero-Noise OptimalityViability: 2.0
- Builds On ThisConvergence of projected stochastic natural gradient variational inference for various step size and sample or batch size schedules