λ-GELU: Learning Gating Hardness for Controlled ReLU-ization in Deep Networks
λ-GELU: Learning Gating Hardness for Controlled ReLU-ization in Deep Networks explores A novel activation function that bridges smooth neural network training with ReLU-compatible deployment pipelines by learning a controllable 'hardness' parameter.. 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 ThisGradient Regularization Prevents Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable RewardsViability: 2.0
- Builds On ThisNeural Network Approximation: A View from Polytope DecompositionViability: 3.0
- Builds On ThisRational Neural Networks have Expressivity AdvantagesViability: 2.0
- Builds On ThisGRAU: Generic Reconfigurable Activation Unit Design for Neural Network Hardware AcceleratorsViability: 3.0
- Prior WorkUncovering a Winning Lottery Ticket with Continuously Relaxed Bernoulli Gates