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 4061–4070 of 4891 papers

TitleStatusHype
Deep Penalty Methods: A Class of Deep Learning Algorithms for Solving High Dimensional Optimal Stopping Problems—0
Deep Plug-and-Play HIO Approach for Phase Retrieval—0
Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes—0
Deep Reinforcement Learning with Plasticity Injection—0
DeepRLS: A Recurrent Network Architecture with Least Squares Implicit Layers for Non-blind Image Deconvolution—0
Deep Signal Recovery with One-Bit Quantization—0
DeepSNR: A deep learning foundation for offline gravitational wave detection—0
DeeptDCS: Deep Learning-Based Estimation of Currents Induced During Transcranial Direct Current Stimulation—0
Deep Tensor Network—0
Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified