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

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Showing 4111–4120 of 4891 papers

TitleStatusHype
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
Split-Merge: A Difference-based Approach for Dominant Eigenvalue Problem—0
Connection Sensitivity Matters for Training-free DARTS: From Architecture-Level Scoring to Operation-Level Sensitivity Analysis—0
Discretized Gaussian Representation for Tomographic Reconstruction—0
Differentiable Point-based Inverse Rendering—0
Differentiable Turbulence: Closure as a partial differential equation constrained optimization—0
Differentially Private Community Detection for Stochastic Block Models—0
PrivateMail: Supervised Manifold Learning of Deep Features With Differential Privacy for Image Retrieval—0
Differentially Private Training of Mixture of Experts Models—0
Differential Privacy of Hierarchical Census Data: An Optimization Approach—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified