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

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Showing 451–460 of 4891 papers

TitleStatusHype
Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellationCode1
LDM-Morph: Latent diffusion model guided deformable image registrationCode1
Differentially Flat Learning-based Model Predictive Control Using a Stability, State, and Input Constraining Safety FilterCode1
Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender SystemsCode1
Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object DetectionCode1
DnS: Distill-and-Select for Efficient and Accurate Video Indexing and RetrievalCode1
Learning Signal Temporal Logic through Neural Network for Interpretable ClassificationCode1
Learning Span-Level Interactions for Aspect Sentiment Triplet ExtractionCode1
Dynamic Multimodal FusionCode1
Energy-based physics-informed neural network for frictionless contact problems under large deformationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified