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 2951–2960 of 4891 papers

TitleStatusHype
Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning—0
TCM-FTP: Fine-Tuning Large Language Models for Herbal Prescription Prediction—0
TCM-GPT: Efficient Pre-training of Large Language Models for Domain Adaptation in Traditional Chinese Medicine—0
TDDBench: A Benchmark for Training data detection—0
Teacher Guided Architecture Search—0
Teaching Tailored to Talent: Adverse Weather Restoration via Prompt Pool and Depth-Anything Constraint—0
Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning—0
Temporal-Coded Spiking Neural Networks with Dynamic Firing Threshold: Learning with Event-Driven Backpropagation—0
Temporally Resolution Decrement: Utilizing the Shape Consistency for Higher Computational Efficiency—0
Temporal Separation with Entropy Regularization for Knowledge Distillation in Spiking Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified