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- Gaussian primitives for deformable image registration8
GaussianDIR combines high registration accuracy with computational efficiency, interpretability, and strong generalization performance, and challenged the conventional notion that iterative methods were inherently slow and overcomed the generalization limitations of data-driven methods.
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- Sparse Variable Selection on High Dimensional Heterogeneous Data With Tree Structured Responses6
This work introduces a model, the tree-guided sparse linear mixed model, that can utilize the dependency information from multiple responses to explore how specifically clusters are and select the active variables from heterogeneous data.
- SSR: Speculative Parallel Scaling Reasoning in Test-time4
SSR (Speculative Parallel Scaling Reasoning), a training-free framework that leverages a key insight: by introducing speculative decoding at the step level, can accelerate reasoning without sacrificing correctness, is proposed.
- ED-Res: Splitting ResNet with Collaborate Distributed Inference on Edge Devices2
This work is the first to utilize the class-wise pruning perspective and propose a novel ResNet splitting framework, ED-Res, which split ResNet into sub-models to do the pruning, where each targets specific class sets while ensuring the effectiveness of the deep residual structure.
- Contrastive Discrepancy: A label-free metric for deformable image registration supporting testing-time hyperparameter selection–
Comparative Discrepancy is introduced, a novel label-free metric for robust evaluation and automatic hyperparameter tuning without requiring manual annotations in DIR that enables DIR models to be dynamically optimized for individual patient data without any labeling cost.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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