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 4551–4575 of 4891 papers

TitleStatusHype
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
Augmented Space Linear Model—0
Spherical CNNsCode0
Learning to Speed Up Query Planning in Graph Databases—0
FastNetCode0
Enlarging Context with Low Cost: Efficient Arithmetic Coding with Trimmed Convolution—0
SAR Image Despeckling Using Quadratic-Linear Approximated L1-Norm—0
Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational InferenceCode0
Linear Time Clustering for High Dimensional Mixtures of Gaussian Clouds—0
Tracking objects using 3D object proposals—0
clcNet: Improving the Efficiency of Convolutional Neural Network using Channel Local ConvolutionsCode0
AdaBatch: Adaptive Batch Sizes for Training Deep Neural NetworksCode0
Achieving the time of 1-NN, but the accuracy of k-NNCode0
Solving Most Systems of Random Quadratic Equations—0
Multi-Channel CNN-based Object Detection for Enhanced Situation Awareness—0
GraphGAN: Graph Representation Learning with Generative Adversarial NetsCode0
Robust Object Tracking Based on Self-adaptive Search Area—0
Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization—0
Accelerated Alternating Projections for Robust Principal Component AnalysisCode0
Optimizing Kernel Machines using Deep Learning—0
Grafting for Combinatorial Boolean Model using Frequent Itemset MiningCode0
Learning Depthwise Separable Graph Convolution from Data Manifold—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified