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 491–500 of 4891 papers

TitleStatusHype
xVal: A Continuous Numerical Tokenization for Scientific Language ModelsCode1
Fast, Expressive SE(n) Equivariant Networks through Weight-Sharing in Position-Orientation SpaceCode1
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model TrainingCode1
VENOM: A Vectorized N:M Format for Unleashing the Power of Sparse Tensor CoresCode1
DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable ConvolutionCode1
Improving Facade Parsing with Vision Transformers and Line IntegrationCode1
AsymFormer: Asymmetrical Cross-Modal Representation Learning for Mobile Platform Real-Time RGB-D Semantic SegmentationCode1
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelsCode1
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science ApplicationsCode1
Prompt Tuned Embedding Classification for Multi-Label Industry Sector AllocationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified