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 411–420 of 4891 papers

TitleStatusHype
Hyperspectral Pansharpening: Critical Review, Tools and Future PerspectivesCode1
PolygonGNN: Representation Learning for Polygonal Geometries with Heterogeneous Visibility GraphCode1
Unified Auto-Encoding with Masked DiffusionCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
CE-SSL: Computation-Efficient Semi-Supervised Learning for ECG-based Cardiovascular Diseases DetectionCode1
YOLO9tr: A Lightweight Model for Pavement Damage Detection Utilizing a Generalized Efficient Layer Aggregation Network and Attention MechanismCode1
PixMamba: Leveraging State Space Models in a Dual-Level Architecture for Underwater Image EnhancementCode1
Enhancing Fast Feed Forward Networks with Load Balancing and a Master Leaf NodeCode1
Node Identifiers: Compact, Discrete Representations for Efficient Graph LearningCode1
FTMixer: Frequency and Time Domain Representations Fusion for Time Series ModelingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified