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 1501–1510 of 4891 papers

TitleStatusHype
Fast Multi-grid Methods for Minimizing Curvature EnergyCode0
OpenRANet: Neuralized Spectrum Access by Joint Subcarrier and Power Allocation with Optimization-based Deep LearningCode0
A Social Robot with Inner Speech for Dietary GuidanceCode0
Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methodsCode0
FastNetCode0
FDAPT: Federated Domain-adaptive Pre-training for Language ModelsCode0
Bi-fidelity Variational Auto-encoder for Uncertainty QuantificationCode0
Overlapping community detection in networks via sparse spectral decompositionCode0
Feed-Forward Optimization With Delayed Feedback for Neural NetworksCode0
Data-based stochastic modeling reveals sources of activity bursts in single-cell TGF-β signalingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified