FourierMoE: Fourier Mixture-of-Experts Adaptation of Large Language Models
FourierMoE: Fourier Mixture-of-Experts Adaptation of Large Language Models explores FourierMoE enables parameter-efficient LLM adaptation by leveraging spectral domain experts, outperforming existing methods across numerous benchmarks with fewer trainable parameters.. Commercial viability score: 7/10 in LLM Fine-tuning.
Updated 4 hours ago
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 ThisLatentMoE: Toward Optimal Accuracy per FLOP and Parameter in Mixture of ExpertsViability: 3.0
- Builds On ThisHolistic Scaling Laws for Optimal Mixture-of-Experts Architecture OptimizationViability: 3.0
- Builds On ThisMoEless: Efficient MoE LLM Serving via Serverless ComputingViability: 3.0
- Prior WorkExpert Pyramid Tuning: Efficient Parameter Fine-Tuning for Expertise-Driven Task AllocationViability: 7.0
- Prior WorkpMoE: Prompting Diverse Experts Together Wins More in Visual Adaptation