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 2891–2900 of 4891 papers

TitleStatusHype
SeTformer is What You Need for Vision and Language—0
Model-Agnostic Interpretation Framework in Machine Learning: A Comparative Study in NBA Sports—0
Predicting Traffic Flow with Federated Learning and Graph Neural with Asynchronous Computations Network—0
Migrating Birds Optimization-Based Feature Selection for Text Classification—0
Kernel-U-Net: Multivariate Time Series Forecasting using Custom Kernels—0
Efficient Parallel Audio Generation using Group Masked Language Modeling—0
SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization—0
Structured Model Probing: Empowering Efficient Transfer Learning by Structured Regularization—0
Patch2Self2: Self-supervised Denoising on Coresets via Matrix Sketching—0
PromptCoT: Align Prompt Distribution via Adapted Chain-of-Thought—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified