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 1751–1760 of 4891 papers

TitleStatusHype
HyFusion: Enhanced Reception Field Transformer for Hyperspectral Image Fusion—0
Exploring Molecule Generation Using Latent Space Graph DiffusionCode0
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
Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets through Strongly Scattering Media—0
DehazeGS: Seeing Through Fog with 3D Gaussian Splatting—0
A GPU Implementation of Multi-Guiding Spark Fireworks Algorithm for Efficient Black-Box Neural Network OptimizationCode0
Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure MeasurementsCode0
CodeVision: Detecting LLM-Generated Code Using 2D Token Probability Maps and Vision Models—0
Samba-ASR: State-Of-The-Art Speech Recognition Leveraging Structured State-Space Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified