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 1441–1450 of 4891 papers

TitleStatusHype
Action Recognition Using Temporal Shift Module and Ensemble LearningCode0
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse HypergraphsCode0
Feed-Forward Optimization With Delayed Feedback for Neural NetworksCode0
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNsCode0
FGP: Feature-Gradient-Prune for Efficient Convolutional Layer PruningCode0
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
DBgDel: Database-Enhanced Gene Deletion Framework for Growth-Coupled Production in Genome-Scale Metabolic ModelsCode0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Data-to-Model Distillation: Data-Efficient Learning FrameworkCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified