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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 781–790 of 4891 papers

TitleStatusHype
Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image ClassificationCode1
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and MappingCode1
Improved Techniques for Training Adaptive Deep NetworksCode1
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional NetworksCode1
Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing ImagesCode1
How Can We Be So Dense? The Benefits of Using Highly Sparse RepresentationsCode1
MaCow: Masked Convolutional Generative FlowCode1
Efficient Neural Network Robustness Certification with General Activation FunctionsCode1
Attention U-Net: Learning Where to Look for the PancreasCode1
Fast Sequence Based Embedding with Diffusion GraphsCode1
Show:102550
← PrevPage 79 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified