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 3721–3730 of 4891 papers

TitleStatusHype
Logspace Reducibility From Secret Leakage Planted Clique—0
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare SettingsCode1
Optimal Operation of Power Systems with Energy Storage under Uncertainty: A Scenario-based Method with Strategic Sampling—0
Precision-Weighted Federated Learning—0
Stein Variational Gradient Descent with Multiple Kernel—0
Adaptive wavelet distillation from neural networks through interpretationsCode1
Robust Topology Optimization Using Multi-Fidelity Variational Autoencoders—0
Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model—0
Transformer-based Machine Learning for Fast SAT Solvers and Logic SynthesisCode1
Continuous vs. Discrete Optimization of Deep Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified