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 1211–1220 of 4891 papers

TitleStatusHype
A Signal Matrix-Based Local Flaw Detection Framework for Steel Wire Ropes Using Convolutional Neural Networks—0
DTFSal: Audio-Visual Dynamic Token Fusion for Video Saliency Prediction—0
Computationally Efficient State and Model Estimation via Interval Observers for Partially Unknown Systems—0
Ride-pool Assignment Algorithms: Modern Implementation and Swapping Heuristics—0
CAT: A Conditional Adaptation Tailor for Efficient and Effective Instance-Specific Pansharpening on Real-World Data—0
Mavors: Multi-granularity Video Representation for Multimodal Large Language Model—0
OmniMamba4D: Spatio-temporal Mamba for longitudinal CT lesion segmentation—0
Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?—0
Enhancing Mathematical Reasoning in Large Language Models with Self-Consistency-Based Hallucination Detection—0
Integrated GARCH-GRU in Financial Volatility Forecasting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified