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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2151–2160 of 4891 papers

TitleStatusHype
Rethinking Graph Transformer Architecture Design for Node Classification—0
Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling—0
A few-shot Label Unlearning in Vertical Federated Learning—0
fastHDMI: Fast Mutual Information Estimation for High-Dimensional Data—0
SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep ModelsCode0
Echo State Networks for Spatio-Temporal Area-Level Data—0
Ada-K Routing: Boosting the Efficiency of MoE-based LLMs—0
Large Language Model Evaluation via Matrix Nuclear-NormCode0
Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning—0
Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified