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 4526–4550 of 4891 papers

TitleStatusHype
Attention U-Net: Learning Where to Look for the PancreasCode1
Sequence Training of DNN Acoustic Models With Natural Gradient—0
Sparse Principal Component Analysis via Variable Projection—0
Robust Subspace Clustering with Compressed Data—0
CSfM: Community-based Structure from Motion—0
Fast Sequence Based Embedding with Diffusion GraphsCode1
Simple random search provides a competitive approach to reinforcement learningCode1
Provable Convex Co-clustering of Tensors—0
Heuristics for vehicle routing problems: Sequence or set optimization?—0
Fast and Accurate Semantic Mapping through Geometric-based Incremental Segmentation—0
L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks—0
Heron Inference for Bayesian Graphical ModelsCode0
Finding Influential Training Samples for Gradient Boosted Decision TreesCode0
HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under UncertaintyCode0
Mining Sub-Interval Relationships In Time Series Data—0
Systematic Weight Pruning of DNNs using Alternating Direction Method of MultipliersCode0
Phased Microphone Array for Sound Source Localization with Deep Learning—0
The Need for Speed of AI Applications: Performance Comparison of Native vs. Browser-based Algorithm Implementations—0
Spectral Learning of Binomial HMMs for DNA Methylation Data—0
Efficient Large-Scale Multi-Modal Classification—0
Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification—0
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution LayersCode1
Augmented Space Linear Model—0
Spherical CNNsCode0
Learning to Speed Up Query Planning in Graph Databases—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified