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 1691–1700 of 4891 papers

TitleStatusHype
Adaptive Data Exploitation in Deep Reinforcement LearningCode0
Attention-Driven Hierarchical Reinforcement Learning with Particle Filtering for Source Localization in Dynamic Fields—0
A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning—0
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor ContractionsCode0
Parallel Sequence Modeling via Generalized Spatial Propagation Network—0
Survey on Hand Gesture Recognition from Visual Input—0
Using Space-Filling Curves and Fractals to Reveal Spatial and Temporal Patterns in Neuroimaging DataCode0
"FRAME: Forward Recursive Adaptive Model Extraction -- A Technique for Advance Feature Selection"—0
Heuristic Deep Reinforcement Learning for Phase Shift Optimization in RIS-assisted Secure Satellite Communication Systems with RSMA—0
Hybrid Adaptive Modeling using Neural Networks Trained with Nonlinear Dynamics Based Features—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified