A blank-slate neural network grows via expansion, generalization, forgetting, and backpropagation for lifelong learning with claimed gains in accuracy, efficiency, and versatility.
MIT press (2016)
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Resampling methods achieve near-perfect utility (TSTR 0.997) but fail privacy (DCR ~0), while VAEs balance 83.3% utility with full privacy protection for synthetic educational data.
citing papers explorer
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Lifelong Learning Starting From Zero
A blank-slate neural network grows via expansion, generalization, forgetting, and backpropagation for lifelong learning with claimed gains in accuracy, efficiency, and versatility.
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Synthetic Data in Education: Empirical Insights from Traditional Resampling and Deep Generative Models
Resampling methods achieve near-perfect utility (TSTR 0.997) but fail privacy (DCR ~0), while VAEs balance 83.3% utility with full privacy protection for synthetic educational data.