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 3351–3360 of 4891 papers

TitleStatusHype
Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks—0
Robust Graph Neural Networks using Weighted Graph LaplacianCode0
A Screening Strategy for Structured Optimization Involving Nonconvex _q,p Regularization—0
Accelerated and interpretable oblique random survival forestsCode1
A Real-time Edge-AI System for Reef Surveys—0
Neural Architecture Search on Efficient Transformers and Beyond—0
Distributed Differential Dynamic Programming Architectures for Large-Scale Multi-Agent Control—0
Integrating Statistical and Machine Learning Approaches to Identify Receptive Field Structure in Neural Populations—0
TransCL: Transformer Makes Strong and Flexible Compressive LearningCode1
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified