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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1111–1120 of 4891 papers

TitleStatusHype
Color Correction Meets Cross-Spectral Refinement: A Distribution-Aware Diffusion for Underwater Image Restoration—0
HyFusion: Enhanced Reception Field Transformer for Hyperspectral Image Fusion—0
Learnable Scaled Gradient Descent for Guaranteed Robust Tensor PCA—0
A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI DataCode0
CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection—0
DispFormer: Pretrained Transformer for Flexible Dispersion Curve Inversion from Global Synthesis to Regional ApplicationsCode1
On Computational Limits and Provably Efficient Criteria of Visual Autoregressive Models: A Fine-Grained Complexity Analysis—0
SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single Images—0
ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono—0
AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified