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 2951–2960 of 4891 papers

TitleStatusHype
RRWKV: Capturing Long-range Dependencies in RWKV—0
Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring—0
An adaptive augmented Lagrangian method for training physics and equality constrained artificial neural networksCode1
Estimating Koopman operators with sketching to provably learn large scale dynamical systemsCode1
Stochastic Natural Thresholding Algorithms—0
Robust-DefReg: A Robust Deformable Point Cloud Registration Method based on Graph Convolutional Neural Networks—0
FAMO: Fast Adaptive Multitask OptimizationCode1
Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks—0
Exploring the effects of robotic design on learning and neural controlCode0
The Power Of Simplicity: Why Simple Linear Models Outperform Complex Machine Learning Techniques -- Case Of Breast Cancer Diagnosis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified