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 1411–1420 of 4891 papers

TitleStatusHype
A Survey on Large-scale Machine LearningCode0
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy InterpolationCode0
Dynamic Bi-Elman Attention Networks: A Dual-Directional Context-Aware Test-Time Learning for Text ClassificationCode0
Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and ProjectionCode0
A Cascaded Dilated Convolution Approach for Mpox Lesion ClassificationCode0
Filtered Markovian Projection: Dimensionality Reduction in Filtering for Stochastic Reaction NetworksCode0
Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal for Multi-Sensor Wearable Human Activity RecognitionCode0
DecomCAM: Advancing Beyond Saliency Maps through Decomposition and IntegrationCode0
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
FGP: Feature-Gradient-Prune for Efficient Convolutional Layer PruningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified