Asymptotic Optimism for Tensor Regression Models with Applications to Neural Network Compression
Asymptotic Optimism for Tensor Regression Models with Applications to Neural Network Compression explores A novel rank selection method for tensor regression that optimizes neural network compression and model selection.. Commercial viability score: 4/10 in LLM Training.
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 $10K - $14K 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 ThisOptimism Stabilizes Thompson Sampling for Adaptive InferenceViability: 2.0
- Builds On ThisWorst-case low-rank approximationsViability: 3.0
- Builds On ThisA Compression Perspective on Simplicity BiasViability: 3.0
- Prior WorkTucker Diffusion Model for High-dimensional Tensor GenerationViability: 4.0
- Prior WorkAdaptive Subspace Modeling With Functional Tucker DecompositionViability: 4.0