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 1391–1400 of 4891 papers

TitleStatusHype
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous mediaCode0
Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identificationCode0
Finding Influential Training Samples for Gradient Boosted Decision TreesCode0
FGP: Feature-Gradient-Prune for Efficient Convolutional Layer PruningCode0
A Hybrid Framework for Reinsurance Optimization: Integrating Generative Models and Reinforcement LearningCode0
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy InterpolationCode0
A Survey on Prompt TuningCode0
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
Filtered Markovian Projection: Dimensionality Reduction in Filtering for Stochastic Reaction NetworksCode0
FREEtree: A Tree-based Approach for High Dimensional Longitudinal Data With Correlated FeaturesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified