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 161–170 of 4891 papers

TitleStatusHype
Generalized and Efficient 2D Gaussian Splatting for Arbitrary-scale Super-ResolutionCode2
L4acados: Learning-based models for acados, applied to Gaussian process-based predictive controlCode2
ClearSight: Visual Signal Enhancement for Object Hallucination Mitigation in Multimodal Large language ModelsCode2
AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic PotentialCode2
Accelerating Direct Preference Optimization with Prefix SharingCode2
Latent Neural Operator for Solving Forward and Inverse PDE ProblemsCode2
GotenNet: Rethinking Efficient 3D Equivariant Graph Neural NetworksCode2
BEBLID: Boosted efficient binary local image descriptorCode2
LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image SegmentationCode2
Flow Matching in Latent SpaceCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified