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 2961–2970 of 4891 papers

TitleStatusHype
CRISP: Hybrid Structured Sparsity for Class-aware Model PruningCode0
Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection—0
A projected nonlinear state-space model for forecasting time series signalsCode0
Scalable CP Decomposition for Tensor Learning using GPU Tensor Cores—0
REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints—0
Multi-Resolution Planar Region Extraction for Uneven Terrains—0
Linear-time online visibility graph transformation algorithm: for both natural and horizontal visibility criteria—0
Novel OCT mosaicking pipeline with Feature- and Pixel-based registrationCode0
Neural Network Pruning by Gradient DescentCode0
Quantum-Enhanced Support Vector Machine for Large-Scale Stellar Classification with GPU Acceleration—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified