PHAROS is a real-time-centric framework that adds preemption and schedulability-oriented DSE to heterogeneous accelerators, finding more deadline-compliant configurations than throughput-only baselines while providing response-time analyses.
Point transformer v3: Simpler faster stronger
4 Pith papers cite this work. Polarity classification is still indexing.
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Invaria trains point cloud encoders with next-resolution prediction to learn scale and density invariant features, yielding higher mIoU on ScanNet under lower resolution and scaled objects while using a smaller model.
Equivariant mesh networks with anatomical priors and augmented message passing deliver stable segmentation across edge, vertex, and face supervision while resisting geometric perturbations.
citing papers explorer
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PHAROS: Pipelined Heterogeneous Accelerators for Real-time Safety-critical Systems With Deadline Compliance
PHAROS is a real-time-centric framework that adds preemption and schedulability-oriented DSE to heterogeneous accelerators, finding more deadline-compliant configurations than throughput-only baselines while providing response-time analyses.
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Invaria: Learning Scale and Density Invariance in Point Clouds via Next-Resolution Prediction
Invaria trains point cloud encoders with next-resolution prediction to learn scale and density invariant features, yielding higher mIoU on ScanNet under lower resolution and scaled objects while using a smaller model.
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Augmented Equivariant Mesh Networks for Anatomical Segmentation
Equivariant mesh networks with anatomical priors and augmented message passing deliver stable segmentation across edge, vertex, and face supervision while resisting geometric perturbations.
- Aes3D: Aesthetic Assessment in 3D Gaussian Splatting