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 591–600 of 4891 papers

TitleStatusHype
Exact Characterization of Aggregate Flexibility via Generalized Polymatroids—0
KernelDNA: Dynamic Kernel Sharing via Decoupled Naive AdaptersCode0
DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization—0
Enhancing Physics-Informed Neural Networks with a Hybrid Parallel Kolmogorov-Arnold and MLP Architecture—0
Beyond Standard MoE: Mixture of Latent Experts for Resource-Efficient Language Models—0
Unsupervised Learning: Comparative Analysis of Clustering Techniques on High-Dimensional Data—0
AuditVotes: A Framework Towards More Deployable Certified Robustness for Graph Neural Networks—0
Action Recognition in Real-World Ambient Assisted Living EnvironmentCode0
Resona: Improving Context Copying in Linear Recurrence Models with Retrieval—0
A Deep Learning Framework for Boundary-Aware Semantic Segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified