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 3421–3430 of 4891 papers

TitleStatusHype
Learning k-Level Structured Sparse Neural Networks Using Group Envelope Regularization—0
SuperGF: Unifying Local and Global Features for Visual Localization—0
Joint Delay-Phase Precoding Under True-Time Delay Constraints in Wideband Sub-THz Hybrid Massive MIMO Systems—0
Single Cell Training on Architecture Search for Image Denoising—0
Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion—0
Tool flank wear prediction using high-frequency machine data from industrial edge device—0
Enhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networks—0
STLGRU: Spatio-Temporal Lightweight Graph GRU for Traffic Flow PredictionCode0
Learning to Optimize in Model Predictive Control—0
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified