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 3631–3640 of 4891 papers

TitleStatusHype
A Semi-Lagrangian two-level preconditioned Newton-Krylov solver for constrained diffeomorphic image registration—0
A Sensorimotor Vision Transformer—0
A Shallow High-Order Parametric Approach to Data Visualization and Compression—0
A Short-Term Predict-Then-Cluster Framework for Meal Delivery Services—0
A Signal Matrix-Based Local Flaw Detection Framework for Steel Wire Ropes Using Convolutional Neural Networks—0
A Simple Baseline with Single-encoder for Referring Image Segmentation—0
A Simple Strategy to Provable Invariance via Orbit Mapping—0
A Simple Unified Information Regularization Framework for Multi-Source Domain Adaptation—0
A Single Shot Text Detector with Scale-adaptive Anchors—0
A Sparse Expansion For Deep Gaussian Processes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified