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 811–820 of 4891 papers

TitleStatusHype
Graph Probability Aggregation Clustering—0
PrimeK-Net: Multi-scale Spectral Learning via Group Prime-Kernel Convolutional Neural Networks for Single Channel Speech EnhancementCode1
Long-Context Inference with Retrieval-Augmented Speculative DecodingCode1
RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering—0
Striving for Faster and Better: A One-Layer Architecture with Auto Re-parameterization for Low-Light Image EnhancementCode0
Dynamic Energy Flow Analysis of Integrated Electricity and Gas Systems: A Semi-Analytical Approach—0
Comet: Fine-grained Computation-communication Overlapping for Mixture-of-ExpertsCode5
Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs—0
Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage PerspectiveCode0
Evaluating the Suitability of Different Intraoral Scan Resolutions for Deep Learning-Based Tooth Segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified