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 671–680 of 4891 papers

TitleStatusHype
Calibrating LLMs with Information-Theoretic Evidential Deep LearningCode1
Cached Multi-Lora Composition for Multi-Concept Image GenerationCode1
DispFormer: Pretrained Transformer for Flexible Dispersion Curve Inversion from Global Synthesis to Regional ApplicationsCode1
Multiscale Context-Aware Ensemble Deep KELM for Efficient Hyperspectral Image ClassificationCode1
Learning Enriched Features via Selective State Spaces Model for Efficient Image DeblurringCode1
Multi-View Learning with Context-Guided Receptance for Image DenoisingCode1
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
MUXConv: Information Multiplexing in Convolutional Neural NetworksCode1
HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance ScalingCode1
H3DE-Net: Efficient and Accurate 3D Landmark Detection in Medical ImagingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified