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 3371–3380 of 4891 papers

TitleStatusHype
AdamNODEs: When Neural ODE Meets Adaptive Moment EstimationCode0
PointNorm: Dual Normalization is All You Need for Point Cloud AnalysisCode1
Safe Drone Flight with Time-Varying Backup Controllers—0
Numerical Comparisons of Linear Power Flow Approximations: Optimality, Feasibility, and Computation Time—0
DeepSNR: A deep learning foundation for offline gravitational wave detection—0
ATC-Based Scenario Decomposition Algorithm for Optimal Power Flow of Distribution Networks Considering High Photovoltaic Penetration—0
Online SuBmodular + SuPermodular (BP) Maximization with Bandit FeedbackCode0
Style Interleaved Learning for Generalizable Person Re-identificationCode0
Robust optimal well control using an adaptive multi-grid reinforcement learning frameworkCode0
Careful Seeding for k-Medois Clustering with Incremental k-Means++ Initialization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified