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 391–400 of 4891 papers

TitleStatusHype
MobileMEF: Fast and Efficient Method for Multi-Exposure FusionCode1
SpectralEarth: Training Hyperspectral Foundation Models at ScaleCode1
MetaSeg: MetaFormer-based Global Contexts-aware Network for Efficient Semantic SegmentationCode1
PRTGaussian: Efficient Relighting Using 3D Gaussians with Precomputed Radiance TransferCode1
Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer ApproachCode1
Time is Not Enough: Time-Frequency based Explanation for Time-Series Black-Box ModelsCode1
Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image ClassificationCode1
CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image SegmentationCode1
Scalify: scale propagation for efficient low-precision LLM trainingCode1
Embedding-Free Transformer with Inference Spatial Reduction for Efficient Semantic SegmentationCode1
Show:102550
← PrevPage 40 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified