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 731–740 of 4891 papers

TitleStatusHype
Automatic Operator-level Parallelism Planning for Distributed Deep Learning -- A Mixed-Integer Programming Approach—0
Reliable Solution to Dynamic Optimization Problems using Integrated Residual Regularized Direct Collocation—0
STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive ApplicationsCode1
A Triple-Inertial Accelerated Alternating Optimization Method for Deep Learning TrainingCode0
Gradient-guided Attention Map Editing: Towards Efficient Contextual Hallucination Mitigation—0
Rel-UNet: Reliable Tumor Segmentation via Uncertainty Quantification in nnU-Net—0
Revisiting Frank-Wolfe for Structured Nonconvex Optimization—0
Efficient and Accurate Estimation of Lipschitz Constants for Hybrid Quantum-Classical Decision Models—0
ELECTRA: A Cartesian Network for 3D Charge Density Prediction with Floating Orbitals—0
Just Functioning as a Hook for Two-Stage Referring Multi-Object Tracking—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified