Oblivious MPGNNs cannot simulate WL color refinement with shallow depth and small messages without randomness; bounded-error randomness enables logarithmic resources for large color sets, while small color sets force layer-message trade-offs.
Random walks on graphs.Combinatorics, Paul erdos is eighty, 2(1-46):4, 1993
2 Pith papers cite this work. Polarity classification is still indexing.
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Pretrained instruction-based image editing models exhibit early foreground-background separability that enables a training-free framework for zero-shot referring image segmentation using a single denoising step.
citing papers explorer
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How Hard Is It for Message-Passing GNNs to Simulate One Weisfeiler-Lehman Color-Refinement Step?
Oblivious MPGNNs cannot simulate WL color refinement with shallow depth and small messages without randomness; bounded-error randomness enables logarithmic resources for large color sets, while small color sets force layer-message trade-offs.
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Early Semantic Grounding in Image Editing Models for Zero-Shot Referring Image Segmentation
Pretrained instruction-based image editing models exhibit early foreground-background separability that enables a training-free framework for zero-shot referring image segmentation using a single denoising step.