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 141–150 of 4891 papers

TitleStatusHype
Large Scale Longitudinal Experiments: Estimation and InferenceCode2
Balancing LoRA Performance and Efficiency with Simple Shard SharingCode2
RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking FrameworkCode2
Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence ModelingCode2
3D-RCNet: Learning from Transformer to Build a 3D Relational ConvNet for Hyperspectral Image ClassificationCode2
Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space CoverageCode2
Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion ModelsCode2
FuXi Weather: A data-to-forecast machine learning system for global weatherCode2
RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution NetworksCode2
LoRA-Pro: Are Low-Rank Adapters Properly Optimized?Code2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified