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 1381–1390 of 4891 papers

TitleStatusHype
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse HypergraphsCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Deep convolutional neural network for shape optimization using level-set approachCode0
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous mediaCode0
Filtered Markovian Projection: Dimensionality Reduction in Filtering for Stochastic Reaction NetworksCode0
FGP: Feature-Gradient-Prune for Efficient Convolutional Layer PruningCode0
Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identificationCode0
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy InterpolationCode0
Lightweight Models for Emotional Analysis in VideoCode0
Feed-Forward Optimization With Delayed Feedback for Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified