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 1971–1980 of 4891 papers

TitleStatusHype
ADMM Algorithms for Residual Network Training: Convergence Analysis and Parallel Implementation—0
Flexible Mesh Segmentation via Reeb Graph Representation of Geometrical and Topological Features—0
3D Gaussian Inverse Rendering with Approximated Global Illumination—0
Flexible-weighted Chamfer Distance: Enhanced Objective Function for Point Cloud Completion—0
Exploiting inter-agent coupling information for efficient reinforcement learning of cooperative LQR—0
FlexMotion: Lightweight, Physics-Aware, and Controllable Human Motion Generation—0
Closed-form Filtering for Non-linear Systems—0
FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs—0
Exploiting Frequency Correlation for Hyperspectral Image Reconstruction—0
Exploiting Estimation Bias in Clipped Double Q-Learning for Continous Control Reinforcement Learning Tasks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified