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 1381–1390 of 4891 papers

TitleStatusHype
FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation—0
Dynamic Power Flow Analysis and Fault Characteristics: A Graph Attention Neural Network—0
Involution and BSConv Multi-Depth Distillation Network for Lightweight Image Super-Resolution—0
Variational Autoencoded Multivariate Spatial Fay-Herriot Models—0
Modular Distributed Nonconvex Learning with Error Feedback—0
Automatic MILP Model Construction for Multi-Robot Task Allocation and Scheduling Based on Large Language Models—0
GFSNetwork: Differentiable Feature Selection via Gumbel-Sigmoid RelaxationCode0
Towards Better Sample Efficiency in Multi-Agent Reinforcement Learning via Exploration—0
BlobCtrl: A Unified and Flexible Framework for Element-level Image Generation and Editing—0
MIXPINN: Mixed-Material Simulations by Physics-Informed Neural Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified