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 4381–4390 of 4891 papers

TitleStatusHype
Spherical U-Net on Cortical Surfaces: Methods and Applications—0
Understanding Neural Architecture Search Techniques—0
An LBP-HOG Descriptor Based on Matrix Projection For Mammogram Classification—0
Medical Time Series Classification with Hierarchical Attention-based Temporal Convolutional Networks: A Case Study of Myotonic Dystrophy Diagnosis—0
How Can We Be So Dense? The Benefits of Using Highly Sparse RepresentationsCode1
Topology-based Representative Datasets to Reduce Neural Network Training ResourcesCode0
Efficient Search-Based Weighted Model Integration—0
PointNetLK: Robust & Efficient Point Cloud Registration using PointNetCode0
Low-rank Tensor Grid for Image Completion—0
Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified