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 1911–1920 of 4891 papers

TitleStatusHype
On-Device Self-Supervised Learning of Low-Latency Monocular Depth from Only Events—0
Adaptive Resolution Residual Networks -- Generalizing Across Resolutions Easily and Efficiently—0
Digital Twin-Empowered Voltage Control for Power Systems—0
Diff-GO^n: Enhancing Diffusion Models for Goal-Oriented CommunicationsCode0
XKV: Personalized KV Cache Memory Reduction for Long-Context LLM Inference—0
Leveraging Time-Series Foundation Model for Subsurface Well Logs Prediction and Anomaly Detection—0
STONet: A novel neural operator for modeling solute transport in micro-cracked reservoirsCode0
A Tiered GAN Approach for Monet-Style Image Generation—0
QueEn: A Large Language Model for Quechua-English Translation—0
Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Training—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified