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 3971–3980 of 4891 papers

TitleStatusHype
Synthetic Training for Monocular Human Mesh Recovery—0
Multimodal Topic Learning for Video Recommendation—0
Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies—0
Nearly Optimal Variational Inference for High Dimensional Regression with Shrinkage Priors—0
Spectral folding and two-channel filter-banks on arbitrary graphs—0
A Modular Framework for Distributed Model Predictive Control of Nonlinear Continuous-Time Systems (GRAMPC-D)—0
Denoising Atmospheric Temperature Measurements Taken by the Mars Science Laboratory on the Martian Surface—0
Computationally and Statistically Efficient Truncated Regression—0
Low-complexity decentralized algorithm for aggregate load control of thermostatic loadsCode0
Sparse Gaussian Process Variational AutoencodersCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified