WTA bottlenecks enforce highly symbolic, disentangled categorical representations of latent factors under defined conditions in multi-task DNNs, shown via theorem and experiments on two datasets.
On the computational power of winner-take-all.Neural computation, 12(11):2519–2535
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FTN achieves near-zero forgetting on continual learning benchmarks by isolating task subnetworks via self-organizing binary masks generated through gradient descent, smoothing, and k-winner-take-all.
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Winner-Take-All bottlenecks enforce disentangled symbolic representations in multi-task learning
WTA bottlenecks enforce highly symbolic, disentangled categorical representations of latent factors under defined conditions in multi-task DNNs, shown via theorem and experiments on two datasets.
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Cortex-Inspired Continual Learning: Unsupervised Instantiation and Recovery of Functional Task Networks
FTN achieves near-zero forgetting on continual learning benchmarks by isolating task subnetworks via self-organizing binary masks generated through gradient descent, smoothing, and k-winner-take-all.