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 3821–3830 of 4891 papers

TitleStatusHype
CAT: A Conditional Adaptation Tailor for Efficient and Effective Instance-Specific Pansharpening on Real-World Data—0
CATP: Cross-Attention Token Pruning for Accuracy Preserved Multimodal Model Inference—0
Causal Generative Domain Adaptation Networks—0
Causal Inference based Transfer Learning with LLMs: An Efficient Framework for Industrial RUL Prediction—0
CausalX: Causal Explanations and Block Multilinear Factor Analysis—0
CCDSReFormer: Traffic Flow Prediction with a Criss-Crossed Dual-Stream Enhanced Rectified Transformer Model—0
CCSNet: a deep learning modeling suite for CO_2 storage—0
CD-ROM: Complemented Deep-Reduced Order Model—0
Joint Location and Velocity Estimation and Fundamental CRLB Analysis for Cell-Free MIMO-ISAC—0
Cerebral cortical communication overshadows computational energy-use, but these combine to predict synapse number—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified