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 2571–2580 of 4891 papers

TitleStatusHype
From Structured to Unstructured:A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs—0
An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing—0
LightCPPgen: An Explainable Machine Learning Pipeline for Rational Design of Cell Penetrating Peptides—0
Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion ModelsCode0
YotoR-You Only Transform One Representation—0
Hierarchical Object-Centric Learning with Capsule Networks—0
TetSphere Splatting: Representing High-Quality Geometry with Lagrangian Volumetric Meshes—0
CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation in Ultra-Lightweight and One-Shot Graph Classification on Edge—0
DecomCAM: Advancing Beyond Saliency Maps through Decomposition and IntegrationCode0
MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution ShiftsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified