GraphCBMs extend concept bottleneck models by building latent concept graphs to model correlations between concepts, yielding better image classification accuracy, more informative structure for interpretability, and stronger intervention results.
Language models are few-shot learners
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ECG-JEPA applies a joint-embedding predictive architecture with Cross-Pattern Attention to learn semantic representations from unlabeled 12-lead ECG data and reports state-of-the-art results on diagnostic classification, feature extraction, and segmentation.
NEMORI is an adaptive memory distillation framework for LLM agents that transforms raw interactions into narratives and extracts insights via prediction error to decide what deserves retention.
Ligandformer is a self-attention graph neural network framework that predicts compound properties, outputs attention maps for local structural interpretation, and claims improved robustness and generalization over prior methods.
citing papers explorer
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Graph Concept Bottleneck Models
GraphCBMs extend concept bottleneck models by building latent concept graphs to model correlations between concepts, yielding better image classification accuracy, more informative structure for interpretability, and stronger intervention results.
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Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture
ECG-JEPA applies a joint-embedding predictive architecture with Cross-Pattern Attention to learn semantic representations from unlabeled 12-lead ECG data and reports state-of-the-art results on diagnostic classification, feature extraction, and segmentation.
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What Deserves Memory: Adaptive Memory Distillation for LLM Agents
NEMORI is an adaptive memory distillation framework for LLM agents that transforms raw interactions into narratives and extracts insights via prediction error to decide what deserves retention.
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Ligandformer: A Graph Neural Network for Predicting Compound Property with Robust Interpretation
Ligandformer is a self-attention graph neural network framework that predicts compound properties, outputs attention maps for local structural interpretation, and claims improved robustness and generalization over prior methods.