RATIONAL ANOVA NETWORKS: STABLE DEEP LEARNING WITH INTERPRETABILITY
Rational ANOVA Networks: Stable Deep Learning with Interpretability
Unlocking the next generation of AI with inherently interpretable and stable deep learning architectures for enterprise applications.
Executive Impact & Key Findings
RAN revolutionizes AI with unmatched stability, interpretability, and efficiency, directly impacting key enterprise metrics.
74.2%
Top-1 Accuracy on ImageNet-1K (ViT-T/16)
1.2x
Increased Throughput (Avg. across benchmarks)
100x
Precision Improvement in Scientific Discovery
72
Parameters for Lorentzian Potential (vs. 5k+ for baselines)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Core Innovations
RAN introduces Padé-style rational units and functional ANOVA decomposition, offering enhanced stability and interpretability over traditional MLPs and spline-based KANs.
Performance & Efficiency
RAN consistently matches or surpasses parameter-matched MLPs and KANs across diverse benchmarks, demonstrating better stability, throughput, and significantly improved extrapolation.
Interpretability & Discovery
The ANOVA structure and rational parameterization enable explicit low-order interaction bias, leading to symbolic discovery and better understanding of underlying physical laws, outperforming other methods in tasks like symbolic regression.
74.2%
Top-1 Accuracy on ImageNet-1K (ViT-T/16)
RAN improves Top-1 accuracy from 72.3% (MLP baseline) to 74.2% under the same Params/FLOPs, attributed to learnable rational nonlinearities and stabilization constraints.
Enterprise Process Flow
Data Ingestion
→
Rational ANOVA Training
→
Auto-Sparsification
→
Implicit Symbolic Regression
→
Exact Law Identification
| Feature | MLP | KAN | RAN (Ours) |
|---|---|---|---|
| Nonlinearity Type | Fixed (ReLU/GELU) | Learnable (Splines) | Learnable (Rational Units) |
| Interaction Topology | Dense Entanglement | Dense, Edge-based | Sparse ANOVA Decomposition |
| Poles/Stability | Generally Stable | Boundary Instability/Ripples | Pole-Free (Positive Denominator) |
| Interpretability | Low | Moderate (Manual Pruning) | High (Automated Symbolic Discovery) |
| Extrapolation | Poor (Linear Bias) | Poor (Runge Phenomenon) | Excellent (Global Rational Fit) |
Case Study: Lorentzian Potential Discovery
RAN's Parameter Efficiency in Action
In a 'Davids vs. Goliaths' experiment, RAN with only 72 parameters outperformed MLPs and KANs (with 5000+ parameters) in discovering the Lorentzian potential. RAN achieved 100x better precision and successfully recovered the exact symbolic form, demonstrating that structural alignment (matching inductive bias to target function) is more critical than parameter quantity for scientific modeling tasks.
MSE for Lorentzian Potential
1.3 × 10-7
Quantify Your AI Impact
Estimate the potential annual savings and reclaimed hours by integrating Rational ANOVA Networks into your enterprise.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0 hrs
Your Strategic AI Implementation Roadmap
A structured approach to integrating RAN into your enterprise, ensuring maximum impact with minimal disruption.
Phase 1: Discovery & Strategy
Engage with our AI architects to define use cases, assess current infrastructure, and map out a tailored RAN integration strategy.
Phase 2: Pilot & Validation
Implement a pilot RAN project on a selected high-impact task, demonstrating early ROI and refining the model for your specific data.
Phase 3: Scaled Deployment
Roll out RAN across relevant enterprise systems, establishing monitoring, and training your teams for sustained operational excellence.
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Schedule a personalized strategy session with our experts to explore how Rational ANOVA Networks can drive your business forward.
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