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 1601–1610 of 4891 papers

TitleStatusHype
Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving RegularizationCode0
Falcon: Fair Active Learning using Multi-armed BanditsCode0
Commonsense Knowledge Base Completion with Structural and Semantic ContextCode0
Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction MethodCode0
Bridging Sensor Gaps via Attention Gated Tuning for Hyperspectral Image ClassificationCode0
A general framework for supporting economic feasibility of generator and storage energy systems through capacity and dispatch optimizationCode0
EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural NetworksCode0
A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral imagesCode0
Explain to Fix: A Framework to Interpret and Correct DNN Object Detector PredictionsCode0
Cortical surface registration using unsupervised learningCode0
Show:102550
← PrevPage 161 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified