WE-MATH benchmark reveals most LMMs rely on rote memorization for visual math while GPT-4o has shifted toward knowledge generalization.
Gradient-based learning applied to document recognition
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EdgeFD uses a KMeans-based client-side filter to improve federated distillation accuracy close to IID levels on non-IID data distributions for resource-constrained edge devices.
Refined probabilistic and smooth l0 pruning techniques approximate minimum description length for neural networks, achieving high compression with minimal accuracy loss and empirically verifying better sample efficiency and generalization on image and text tasks.
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We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?
WE-MATH benchmark reveals most LMMs rely on rote memorization for visual math while GPT-4o has shifted toward knowledge generalization.
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Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
EdgeFD uses a KMeans-based client-side filter to improve federated distillation accuracy close to IID levels on non-IID data distributions for resource-constrained edge devices.
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Efficient compression of neural networks and datasets
Refined probabilistic and smooth l0 pruning techniques approximate minimum description length for neural networks, achieving high compression with minimal accuracy loss and empirically verifying better sample efficiency and generalization on image and text tasks.