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 831–840 of 4891 papers

TitleStatusHype
InVDriver: Intra-Instance Aware Vectorized Query-Based Autonomous Driving Transformer—0
Design and implementation of a distributed security threat detection system integrating federated learning and multimodal LLM—0
Architecting Digital Twins for Intelligent Transportation Systems—0
Learning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learning—0
Random Projections and Natural Sparsity in Time-Series Classification: A Theoretical Analysis—0
Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical SystemsCode2
Muon is Scalable for LLM TrainingCode7
Agentic AI for Behavior-Driven Development Testing Using Large Language ModelsCode0
End-to-End Deep Learning for Structural Brain Imaging: A Unified FrameworkCode0
A Reverse Mamba Attention Network for Pathological Liver SegmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified