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 1921–1930 of 4891 papers

TitleStatusHype
A Non-commutative Bilinear Model for Answering Path Queries in Knowledge Graphs—0
FacEnhance: Facial Expression Enhancing with Recurrent DDPMs—0
Federated Smoothing ADMM for Localization—0
FedMentalCare: Towards Privacy-Preserving Fine-Tuned LLMs to Analyze Mental Health Status Using Federated Learning Framework—0
FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization—0
Ex uno plures: Splitting One Model into an Ensemble of Subnetworks—0
Extrinsic Fluctuations in the p53 Cycle—0
CoDBench: A Critical Evaluation of Data-driven Models for Continuous Dynamical Systems—0
Fed-ZOE: Communication-Efficient Over-the-Air Federated Learning via Zeroth-Order Estimation—0
A Non-asymptotic comparison of SVRG and SGD: tradeoffs between compute and speed—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified