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 4041–4050 of 4891 papers

TitleStatusHype
Synthetic Training for Monocular Human Mesh Recovery—0
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret PerformanceCode0
A Data-Driven Sparse Polynomial Chaos Expansion Method to Assess Probabilistic Total Transfer Capability for Power Systems with Renewables—0
Multimodal Topic Learning for Video Recommendation—0
Nearly Optimal Variational Inference for High Dimensional Regression with Shrinkage Priors—0
Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies—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
Low-complexity decentralized algorithm for aggregate load control of thermostatic loadsCode0
Computationally and Statistically Efficient Truncated Regression—0
Show:102550
← PrevPage 405 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified