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 2801–2810 of 4891 papers

TitleStatusHype
SparseDet: A Simple and Effective Framework for Fully Sparse LiDAR-based 3D Object Detection—0
Sparse Dictionary Learning for Image Recovery by Iterative Shrinkage—0
Sparse Diffusion-Convolutional Neural Networks—0
Sparse Distance Weighted Discrimination—0
Sparse Exact PGA on Riemannian Manifolds—0
Sparse Imagination for Efficient Visual World Model Planning—0
Sparse Least Squares Low Rank Kernel Machines—0
Cross-token Modeling with Conditional Computation—0
Sparse Optimization for Transfer Learning: A L0-Regularized Framework for Multi-Source Domain Adaptation—0
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified