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 4191–4200 of 4891 papers

TitleStatusHype
DST-TransitNet: A Dynamic Spatio-Temporal Deep Learning Model for Scalable and Efficient Network-Wide Prediction of Station-Level Transit Ridership—0
DS-VIO: Robust and Efficient Stereo Visual Inertial Odometry based on Dual Stage EKF—0
DTFSal: Audio-Visual Dynamic Token Fusion for Video Saliency Prediction—0
DTMNet: A Discrete Tchebichef Moments-Based Deep Neural Network for Multi-Focus Image Fusion—0
Dual Conditional Diffusion Models for Sequential Recommendation—0
Dual-control based approach to batch process operation under uncertainty based on optimality-conditions parameterization—0
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime—0
Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces—0
Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization—0
Dual optimization for convex constrained objectives without the gradient-Lipschitz assumption—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified