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 4221–4230 of 4891 papers

TitleStatusHype
CHIRRUP: a practical algorithm for unsourced multiple access—0
Randomized Exploration for Non-Stationary Stochastic Linear BanditsCode0
MDFN: Multi-Scale Deep Feature Learning Network for Object Detection—0
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features—0
Learning a Neural 3D Texture Space from 2D ExemplarsCode0
Value-of-Information based Arbitration between Model-based and Model-free Control—0
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image ClassificationCode1
Singleshot : a scalable Tucker tensor decomposition—0
Learning Positive Functions with Pseudo Mirror Descent—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified