KAN-CL cuts catastrophic forgetting by 88-93% on Split-CIFAR-10/5T and Split-CIFAR-100/10T by anchoring KAN parameters at per-knot granularity while matching baseline accuracy.
Farajtabar et al
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2026 3representative citing papers
SLE-FNO achieves zero forgetting and strong plasticity-stability balance in continual learning for FNO surrogate models of pulsatile blood flow by adding minimal single-layer extensions across four out-of-distribution tasks.
MANGO combines gradient-gating and meta-learned regularization to balance stability and plasticity in single-pass online continual learning, reporting state-of-the-art accuracy on CLEAR-10, CIFAR-100, and Tiny-ImageNet.
citing papers explorer
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KAN-CL: Per-Knot Importance Regularization for Continual Learning with Kolmogorov-Arnold Networks
KAN-CL cuts catastrophic forgetting by 88-93% on Split-CIFAR-10/5T and Split-CIFAR-100/10T by anchoring KAN parameters at per-knot granularity while matching baseline accuracy.
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SLE-FNO: Single-Layer Extensions for Task-Agnostic Continual Learning in Fourier Neural Operators
SLE-FNO achieves zero forgetting and strong plasticity-stability balance in continual learning for FNO surrogate models of pulsatile blood flow by adding minimal single-layer extensions across four out-of-distribution tasks.
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MANGO: Meta-Adaptive Network Gradient Optimization for Online Continual Learning
MANGO combines gradient-gating and meta-learned regularization to balance stability and plasticity in single-pass online continual learning, reporting state-of-the-art accuracy on CLEAR-10, CIFAR-100, and Tiny-ImageNet.