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 1491–1500 of 4891 papers

TitleStatusHype
KunlunBaize: LLM with Multi-Scale Convolution and Multi-Token Prediction Under TransformerX Framework—0
Dynamic Energy Flow Analysis of Integrated Electricity and Gas Systems: A Semi-Analytical Approach—0
FedMentalCare: Towards Privacy-Preserving Fine-Tuned LLMs to Analyze Mental Health Status Using Federated Learning Framework—0
Graph Probability Aggregation Clustering—0
Deep Learning-Based Approach for Automatic 2D and 3D MRI Segmentation of Gliomas—0
CLIP-Optimized Multimodal Image Enhancement via ISP-CNN Fusion for Coal Mine IoVT under Uneven Illumination—0
Evaluating the Suitability of Different Intraoral Scan Resolutions for Deep Learning-Based Tooth Segmentation—0
Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage PerspectiveCode0
Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs—0
High-fidelity Multiphysics Modelling for Rapid Predictions Using Physics-informed Parallel Neural Operator—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified