Active lifting of data distributions via latent-sequence sampling and max-rate uncertainty reduction formally derives slow-thinking LLMs and places them on representation and sampler hierarchies that can be climbed.
Visualizing and understanding convolutional networks
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The paper derives a posteriori error estimates for neural network depth adaptation by formulating training as an optimal control problem and using dual weighted residuals to insert layers where error is highest.
Benchmark study of ten GNN explainers on eight architectures and six datasets that isolates usable components and issues practical recommendations.
Simple training code produces complex neural networks, suggesting that brain learning rules may be easier to understand than mature brain properties and that neuroscience should shift focus accordingly.
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A First-Principles Theory of Slow Thinking and Active Perception
Active lifting of data distributions via latent-sequence sampling and max-rate uncertainty reduction formally derives slow-thinking LLMs and places them on representation and sampler hierarchies that can be climbed.
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An optimal control approach for neural network architecture adaptation with a posteriori error estimation
The paper derives a posteriori error estimates for neural network depth adaptation by formulating training as an optimal control problem and using dual weighted residuals to insert layers where error is highest.
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Explaining the Explainers in Graph Neural Networks: a Comparative Study
Benchmark study of ten GNN explainers on eight architectures and six datasets that isolates usable components and issues practical recommendations.
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What does it mean to understand a neural network?
Simple training code produces complex neural networks, suggesting that brain learning rules may be easier to understand than mature brain properties and that neuroscience should shift focus accordingly.