SoftJAX & SoftTorch: Empowering Automatic Differentiation Libraries with Informative Gradients
SoftJAX & SoftTorch: Empowering Automatic Differentiation Libraries with Informative Gradients explores SoftJAX and SoftTorch provide open-source libraries for soft differentiable programming, enhancing automatic differentiation with informative gradients.. Commercial viability score: 9/10 in Differentiable Programming.
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 ThisSelf-Scaled Broyden Family of Quasi-Newton Methods in JAXViability: 4.0
- Builds On ThisTraining event-based neural networks with exact gradients via Differentiable ODE Solving in JAXViability: 7.0
- Builds On ThisSecond-Order, First-Class: A Composable Stack for Curvature-Aware TrainingViability: 4.0
- Builds On ThisOpenReservoirComputing: GPU-Accelerated Reservoir Computing in JAXViability: 3.0
- Builds On ThisDifferentiable Power-Flow OptimizationViability: 7.0