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 311–320 of 4891 papers

TitleStatusHype
Fine-tuning a Large Language Model for Automating Computational Fluid Dynamics SimulationsCode1
Exploring _0 Sparsification for Inference-free Sparse RetrieversCode1
Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent CodingCode1
Exploiting Redundancy: Separable Group Convolutional Networks on Lie GroupsCode1
Exploring Quality and Generalizability in Parameterized Neural Audio EffectsCode1
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science ApplicationsCode1
Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated UncertaintyCode1
EViT: An Eagle Vision Transformer with Bi-Fovea Self-AttentionCode1
Adaptive Fourier Neural Operators: Efficient Token Mixers for TransformersCode1
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PCCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified