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Path-Coordinated Continual Learning with Neural Tangent Kernel-Justified Plasticity: A Theoretical Framework with Near State-of-the-Art Performance

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📄 Abstract

Abstract: Catastrophic forgetting is one of the fundamental issues of continual learning because neural networks forget the tasks learned previously when trained on new tasks. The proposed framework is a new path-coordinated framework of continual learning that unites the Neural Tangent Kernel (NTK) theory of principled plasticity bounds, statistical validation by Wilson confidence intervals, and evaluation of path quality by the use of multiple metrics. Experimental evaluation shows an average accuracy of 66.7% at the cost of 23.4% catastrophic forgetting on Split-CIFAR10, a huge improvement over the baseline and competitive performance achieved, which is very close to state-of-the-art results. Further, it is found out that NTK condition numbers are predictive indicators of learning capacity limits, showing the existence of a critical threshold at condition number $>10^{11}$. It is interesting to note that the proposed strategy shows a tendency of lowering forgetting as the sequence of tasks progresses (27% to 18%), which is a system stabilization. The framework validates 80% of discovered paths with a rigorous statistical guarantee and maintains 90-97% retention on intermediate tasks. The core capacity limits of the continual learning environment are determined in the analysis, and actionable insights to enhance the adaptive regularization are offered.

Key Contributions

Proposes a path-coordinated continual learning framework that unifies NTK theory for plasticity bounds with statistical validation and path quality evaluation. This framework achieves near state-of-the-art performance with significantly reduced forgetting and identifies NTK condition numbers as predictive indicators of learning capacity limits.

Business Value

Enables AI systems to learn continuously and adapt to new information without forgetting past knowledge, crucial for applications requiring long-term learning and adaptation, such as personalized assistants or autonomous systems.

Paper Metadata

Innovation Type

Algorithmic and Theoretical Innovation

Deployment Feasibility

Moderate. Theoretical framework requires careful implementation and validation for practical systems.

Limitations Addressed

Catastrophic forgetting, a fundamental issue in continual learning where networks forget previously learned tasks when trained on new ones.

Performance Gains

Significant improvement over baseline methods in terms of accuracy and reduced forgetting.

Technical Tags

continual learningcatastrophic forgettingNeural Tangent Kernel (NTK)plasticity boundspath-coordinated learningstatistical validationlearning capacitySplit-CIFAR10

Research Topics

Continual LearningCatastrophic ForgettingMachine Learning TheoryNeural Network DynamicsModel Robustness

Methods & Architectures

Path-Coordinated Continual Learning FrameworkNeural Tangent Kernel (NTK) TheoryStatistical Validation (Wilson confidence intervals) Neural Networks

Applications & Tasks

Continual Learning Systems Machine Learning Lifelong Learning Catastrophic Forgetting in Continual LearningBalancing Plasticity and StabilityPredicting Learning Capacity Limits Continual LearningMitigating ForgettingAnalyzing Learning Dynamics

Datasets & Benchmarks

Datasets

Split-CIFAR10

Benchmarks

Split-CIFAR10: 66.7% average accuracy, 23.4% catastrophic forgetting

AccuracyCatastrophic ForgettingNTK condition numbers

Related Fields

Machine Learning TheoryContinual LearningDeep LearningStatisticsOptimization

Keywords

continual learningcatastrophic forgettingNeural Tangent Kernelplasticitylifelong learningmodel robustnesslearning theoryneural networksAI safetytask learning

Academic Context

#Continual Learning#Catastrophic Forgetting#Machine Learning Theory#Neural Network Dynamics#Model Robustness

Technology Stack

Frameworks & Libraries

PyTorch

Commercial Potential

Potential Products

Continual learning frameworksAI systems that adapt over time

Target Industries

TechnologyRoboticsAutonomous SystemsPersonalized Services

Use Case Examples

Robots that learn new tasks without forgetting old onesAI systems that continuously update their knowledge basePersonalized recommendation systems that adapt to user preferences over time

Competitive Edge

Offers a theoretically grounded framework for continual learning that achieves competitive performance while providing insights into learning capacity limits.

Market Opportunity

Growing importance of lifelong learning capabilities in AI.

Revenue Models

Licensing of continual learning algorithmsdevelopment of adaptive AI systems.

Resource Requirements

Compute Needs

High (for training on datasets like CIFAR)

Data Requirements

Sequential task datasets (e.g., Split-CIFAR10).

Deployment Constraints

Complexity of implementing the path-coordinated framework.

Scalability

Scalability depends on the NTK computation and the continual learning strategy.

Production Readiness

Maturity Level

Research

Time to Market

3-5 years

Patent Potential

Moderate

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