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 2411–2420 of 4891 papers

TitleStatusHype
Emulating the interstellar medium chemistry with neural operators—0
Flexible Robust Optimal Bidding of Renewable Virtual Power Plants in Sequential MarketsCode0
FiT: Flexible Vision Transformer for Diffusion ModelCode3
Network Inversion of Binarised Neural Nets—0
DualView: Data Attribution from the Dual PerspectiveCode0
DB-LLM: Accurate Dual-Binarization for Efficient LLMs—0
Turn Waste into Worth: Rectifying Top-k Router of MoE—0
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank BanditsCode0
Random Projection Neural Networks of Best Approximation: Convergence theory and practical applications—0
PhaseEvo: Towards Unified In-Context Prompt Optimization for Large Language Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified