Quantum annealing solves a combinatorial feature-map selection problem for CNNs, yielding improved class disentanglement over GradCAM and GradCAM++ in the reported evaluation.
Multipole graph neural operator for parametric partial differential equations
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
FlowForge predicts flow fields via staged local updates with a shared lightweight predictor, matching or exceeding baselines in accuracy while improving robustness to noise and reducing latency.
TEMPO-Diffusion is a targeted backdoor attack framework for diffusion models that uses time-conditioned triggers to poison class-specific synthetic data, achieving high attack success in downstream classifiers.
2D diffusion-generated synthetic X-rays enable training of anatomical landmark detectors that generalize to real images with performance rivaling real-data training.
FontFusion adds hierarchical token conditioning, position-aware embeddings, and multi-level dropping to DiT diffusion models, yielding 76% relative gains on decorative fonts and 68-76% consistency improvements via a dual DeepFont+DINOv2 encoder.
citing papers explorer
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Towards interpretable AI with quantum annealing feature selection
Quantum annealing solves a combinatorial feature-map selection problem for CNNs, yielding improved class disentanglement over GradCAM and GradCAM++ in the reported evaluation.
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FlowForge: A Staged Local Rollout Engine for Flow-Field Prediction
FlowForge predicts flow fields via staged local updates with a shared lightweight predictor, matching or exceeding baselines in accuracy while improving robustness to noise and reducing latency.
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TEMPO-Diffusion: Temporally Exposed Malicious Poisoning of Diffusion Models
TEMPO-Diffusion is a targeted backdoor attack framework for diffusion models that uses time-conditioned triggers to poison class-specific synthetic data, achieving high attack success in downstream classifiers.
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2D Versus 3D Diffusion for In Silico Training of Interventional X-ray AI Models
2D diffusion-generated synthetic X-rays enable training of anatomical landmark detectors that generalize to real images with performance rivaling real-data training.
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FontFusion: Enhancing Generative Text in Diffusion Models with Typographic Conditioning
FontFusion adds hierarchical token conditioning, position-aware embeddings, and multi-level dropping to DiT diffusion models, yielding 76% relative gains on decorative fonts and 68-76% consistency improvements via a dual DeepFont+DINOv2 encoder.