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 2851–2860 of 4891 papers

TitleStatusHype
Optimal Clustering with Dependent Costs in Bayesian Networks—0
SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image ReconstructionCode0
CIRO: COVID-19 infection risk ontologyCode0
Explicifying Neural Implicit Fields for Efficient Dynamic Human Avatar Modeling via a Neural Explicit Surface—0
RoadScan: A Novel and Robust Transfer Learning Framework for Autonomous Pothole Detection in Roads—0
High-Resolution Vision Transformers for Pixel-Level Identification of Structural Components and Damage—0
Learning Fine-Grained Features for Pixel-wise Video CorrespondencesCode1
MiAMix: Enhancing Image Classification through a Multi-stage Augmented Mixed Sample Data Augmentation Method—0
Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries—0
A State-Space Perspective on Modelling and Inference for Online Skill RatingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified