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 1721–1730 of 4891 papers

TitleStatusHype
SDP: Spiking Diffusion Policy for Robotic Manipulation with Learnable Channel-Wise Membrane Thresholds—0
Lite-FBCN: Lightweight Fast Bilinear Convolutional Network for Brain Disease Classification from MRI ImageCode0
A Hybrid Multi-Factor Network with Dynamic Sequence Modeling for Early Warning of Intraoperative HypotensionCode0
SOAP: Improving and Stabilizing Shampoo using AdamCode3
Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics—0
Provably Efficient Infinite-Horizon Average-Reward Reinforcement Learning with Linear Function Approximation—0
Detecting Sexism in German Online Newspaper Comments with Open-Source Text Embeddings (Team GDA, GermEval2024 Shared Task 1: GerMS-Detect, Subtasks 1 and 2, Closed Track)Code0
Neuromorphic Spintronics—0
Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud DetectionCode0
OML-AD: Online Machine Learning for Anomaly Detection in Time Series DataCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified