Sublime generalizes Count-Min and Count Sketch with dynamically elongating counters and expanding counter arrays to deliver sublinear error growth and lower memory use on skewed unbounded streams.
GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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VoxShield disrupts inter-slice frequency consistency and semantic logits in 3D medical images to degrade segmentation model performance to near-random levels with epsilon=4/255 perturbations.
Mochi aligns pre-training with inference via meta-learning for efficient graph foundation models, matching or exceeding prior models on 25 datasets with 8-27x less training time.
TextBridgeGNN pre-trains GNNs using text-guided hierarchical propagation to enable effective cross-domain knowledge transfer in recommendations.
citing papers explorer
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Sublime: Sublinear Error & Space for Unbounded Skewed Streams
Sublime generalizes Count-Min and Count Sketch with dynamically elongating counters and expanding counter arrays to deliver sublinear error growth and lower memory use on skewed unbounded streams.
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VoxShield: Protecting 3D Medical Datasets from Unauthorized Training via Frequency-Aware Inter-Slice Disruption
VoxShield disrupts inter-slice frequency consistency and semantic logits in 3D medical images to degrade segmentation model performance to near-random levels with epsilon=4/255 perturbations.
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Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning
Mochi aligns pre-training with inference via meta-learning for efficient graph foundation models, matching or exceeding prior models on 25 datasets with 8-27x less training time.
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TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer
TextBridgeGNN pre-trains GNNs using text-guided hierarchical propagation to enable effective cross-domain knowledge transfer in recommendations.