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 1121–1130 of 4891 papers

TitleStatusHype
AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features—0
DehazeGS: Seeing Through Fog with 3D Gaussian Splatting—0
Exploring Molecule Generation Using Latent Space Graph DiffusionCode0
A GPU Implementation of Multi-Guiding Spark Fireworks Algorithm for Efficient Black-Box Neural Network OptimizationCode0
The Power of Negative Zero: Datatype Customization for Quantized Large Language ModelsCode0
CodeVision: Detecting LLM-Generated Code Using 2D Token Probability Maps and Vision Models—0
Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure MeasurementsCode0
Samba-ASR: State-Of-The-Art Speech Recognition Leveraging Structured State-Space Models—0
LightGNN: Simple Graph Neural Network for RecommendationCode2
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified