BrainROI achieves leading cross-subject brain-captioning results on NSD by combining multi-atlas soft-ROI fusion with interpretable prompt optimization.
Belongie, Bharath Hariharan, and Ser-Nam Lim
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
PLACE is a prompt-augmented graph framework for attributed community search that integrates learnable tokens with GNNs via alternating training and divide-and-conquer scaling, achieving 22% higher average F1 scores than prior methods on nine real-world graphs.
DIVE proposes a dimensionality-reduction adapter using self-limiting gradients and implicit view ensembles that outperforms prior adapters on all six BEIR datasets at every tested compression ratio.
citing papers explorer
-
Unified Multimodal Brain Decoding via Cross-Subject Soft-ROI Fusion
BrainROI achieves leading cross-subject brain-captioning results on NSD by combining multi-atlas soft-ROI fusion with interpretable prompt optimization.
-
PLACE: Prompt Learning for Attributed Community Search in Large Graphs
PLACE is a prompt-augmented graph framework for attributed community search that integrates learnable tokens with GNNs via alternating training and divide-and-conquer scaling, achieving 22% higher average F1 scores than prior methods on nine real-world graphs.
-
DIVE: Embedding Compression via Self-Limiting Gradient Updates
DIVE proposes a dimensionality-reduction adapter using self-limiting gradients and implicit view ensembles that outperforms prior adapters on all six BEIR datasets at every tested compression ratio.