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 2191–2200 of 4891 papers

TitleStatusHype
Off-OAB: Off-Policy Policy Gradient Method with Optimal Action-Dependent Baseline—0
TLINet: Differentiable Neural Network Temporal Logic Inference—0
Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning—0
Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric spaceCode0
RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank—0
Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models—0
On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?Code2
MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving—0
Interpretable Data-driven Anomaly Detection in Industrial Processes with ExIFFI—0
PAM-UNet: Shifting Attention on Region of Interest in Medical Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified