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 691–700 of 4891 papers

TitleStatusHype
Calibrating LLMs with Information-Theoretic Evidential Deep LearningCode1
Learning Enriched Features via Selective State Spaces Model for Efficient Image DeblurringCode1
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoopCode1
DynamicDet: A Unified Dynamic Architecture for Object DetectionCode1
Dynamic Clustering and Cluster Contrastive Learning for Unsupervised Person Re-identificationCode1
Dynamic Group Convolution for Accelerating Convolutional Neural NetworksCode1
Dynamic Implicit Image Function for Efficient Arbitrary-Scale Image RepresentationCode1
Cached Multi-Lora Composition for Multi-Concept Image GenerationCode1
Heat flux for semi-local machine-learning potentialsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified