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 2691–2700 of 4891 papers

TitleStatusHype
Delayed Memory Unit: Modelling Temporal Dependency Through Delay GateCode0
Scrap Your Schedules with PopDescent—0
EDGE++: Improved Training and Sampling of EDGE—0
Accelerated sparse Kernel Spectral Clustering for large scale data clustering problemsCode0
Online energy management system for a fuel cell/battery hybrid system with multiple fuel cell stacks—0
DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging DataCode0
DeepFracture: A Generative Approach for Predicting Brittle Fractures with Neural Discrete Representation Learning—0
Streamlining Brain Tumor Classification with Custom Transfer Learning in MRI Images—0
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer—0
Jorge: Approximate Preconditioning for GPU-efficient Second-order Optimization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified