SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

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Showing 321–330 of 4891 papers

TitleStatusHype
DualOpt: A Dual Divide-and-Optimize Algorithm for the Large-scale Traveling Salesman ProblemCode1
Poseidon: A ViT-based Architecture for Multi-Frame Pose Estimation with Adaptive Frame Weighting and Multi-Scale Feature FusionCode1
Flash Window Attention: speedup the attention computation for Swin TransformerCode1
DispFormer: Pretrained Transformer for Flexible Dispersion Curve Inversion from Global Synthesis to Regional ApplicationsCode1
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
KM-UNet KAN Mamba UNet for medical image segmentationCode1
Robust Self-Paced Hashing for Cross-Modal Retrieval with Noisy LabelsCode1
HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer SegmentationCode1
TeLU Activation Function for Fast and Stable Deep LearningCode1
Underwater Image Restoration via Polymorphic Large Kernel CNNsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified