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

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Showing 2101–2110 of 4891 papers

TitleStatusHype
LightCPPgen: An Explainable Machine Learning Pipeline for Rational Design of Cell Penetrating Peptides—0
Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion ModelsCode0
Effective Interplay between Sparsity and Quantization: From Theory to Practice—0
YotoR-You Only Transform One Representation—0
TetSphere Splatting: Representing High-Quality Geometry with Lagrangian Volumetric Meshes—0
Hierarchical Object-Centric Learning with Capsule Networks—0
MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution ShiftsCode0
DecomCAM: Advancing Beyond Saliency Maps through Decomposition and IntegrationCode0
CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation in Ultra-Lightweight and One-Shot Graph Classification on Edge—0
Use of Boosting Algorithms in Household-Level Poverty Measurement: A Machine Learning Approach to Predict and Classify Household Wealth Quintiles in the Philippines—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified