A VAE and slot-attention scheme learns interpretable object concepts from 1% labels, enabling symbolic reasoning that outperforms foundation models under domain shift.
MIT Press, 2016
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LILogicNet trains compact logic-gate networks with learnable sparse connectivity via Top-K selection, reaching 98.45% MNIST accuracy with 8k gates and 60.98% CIFAR-10 accuracy with 256k gates while using far fewer gates than prior logic models.
citing papers explorer
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Weakly Supervised Concept Learning for Object-centric Visual Reasoning
A VAE and slot-attention scheme learns interpretable object concepts from 1% labels, enabling symbolic reasoning that outperforms foundation models under domain shift.
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LILogic Net: Compact Logic Gate Networks with Learnable Connectivity for Efficient Hardware Deployment
LILogicNet trains compact logic-gate networks with learnable sparse connectivity via Top-K selection, reaching 98.45% MNIST accuracy with 8k gates and 60.98% CIFAR-10 accuracy with 256k gates while using far fewer gates than prior logic models.