Idea
SDM activation function API enhances neural network robustness and interpretability for AI developers and language model users.
Research Paper
Core Innovation
This paper presents the Similarity-Distance-Magnitude (SDM) activation function that extends softmax by incorporating similarity and distance awareness to training data distribution. This approach improves robustness to distribution shifts and out-of-distribution inputs while enabling interpretability through exemplar-based matching. It also supports selective classification by partitioning class-wise empirical CDFs to maintain recall.
Market Size (TAM)
$2–10B TAM for AI model optimization tools; $1–2B SAM from enterprises deploying large language models. Driven by demand for robust AI and interpretability in critical applications.
Potential Customers & Pain Points
- AI Developers Needing Robust Model Outputs
- Enterprises Facing Model Performance Drops Due to Distribution Shifts
- Researchers Seeking Interpretable Neural Network Activations
Business Model
Offer SDM activation as a licensed API or SDK for integration into AI frameworks; provide consulting for model robustness improvements.
Competitive Landscape
- Temperature Scaling
- Ensemble Methods
- Bayesian Neural Networks
Implementation Challenges
- Integration Complexity with Existing Models
- Computational Overhead Compared to Softmax
- Adoption Resistance Due to Established Softmax Usage
Validation Strategy
- Benchmark SDM against softmax on standard OOD datasets
- Demonstrate improved selective classification recall in real-world language models
- Pilot deployments with AI development teams for feedback
Research Paper Overview
Similarity-Distance-Magnitude Activations
Summary
We introduce a more robust and interpretable formulation of the standard softmax activation function commonly used with neural networks by adding Similarity awareness and Distance-to-training-distribution awareness to the existing output Magnitude awareness. When used as the final-layer activation with language models, the resulting Similarity-Distance-Magnitude (SDM) activation function is more robust than softmax to co-variate shifts and out-of-distribution inputs in high-probability regions and provides interpretability-by-exemplar via dense matching. The SDM activation enables partitioning of class-wise empirical CDFs to guard against low class-wise recall among selective classifications, making it preferable for selective classification even when considering post-hoc calibration methods over softmax.