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 2181–2190 of 4891 papers

TitleStatusHype
Accumulations of Projections--A Unified Framework for Random Sketches in Kernel Ridge Regression—0
End-to-End JPEG Decoding and Artifacts Suppression Using Heterogeneous Residual Convolutional Neural Network—0
Can pruning make Large Language Models more efficient?—0
End-to-End Imitation Learning for Optimal Asteroid Proximity Operations—0
Canonical Bayesian Linear System Identification—0
A Neural Network Subgrid Model of the Early Stages of Planet Formation—0
Encoding Categorical Variables with Conjugate Bayesian Models for WeWork Lead Scoring Engine—0
Enabling Fast, Accurate, and Efficient Real-Time Genome Analysis via New Algorithms and Techniques—0
Balancing Privacy, Robustness, and Efficiency in Machine Learning—0
Emulating the interstellar medium chemistry with neural operators—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified