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 4751–4775 of 4891 papers

TitleStatusHype
Hierarchically Constrained Adaptive Ad Exposure in Feeds—0
Hierarchical Object-Centric Learning with Capsule Networks—0
Hierarchical Provision of Distribution Grid Flexibility with Online Feedback Optimization—0
Hierarchical recurrent neural network for skeleton based action recognition—0
Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations—0
Hierarchical structure-and-motion recovery from uncalibrated images—0
Hierarchical Temperature Imaging Using Pseudo-Inversed Convolutional Neural Network Aided TDLAS Tomography—0
High-dimensional Mixed Graphical Models—0
High Energy Density Radiative Transfer in the Diffusion Regime with Fourier Neural Operators—0
Higher-order MRFs based image super resolution: why not MAP?—0
Higher Order Transformers: Efficient Attention Mechanism for Tensor Structured Data—0
High-fidelity Multiphysics Modelling for Rapid Predictions Using Physics-informed Parallel Neural Operator—0
High Fidelity Text-to-Speech Via Discrete Tokens Using Token Transducer and Group Masked Language Model—0
High Order Collaboration-Oriented Federated Graph Neural Network for Accurate QoS Prediction—0
High Performance Computing of Gene Regulatory Networks using a Message-Passing Model—0
High Performance Visual Tracking with Circular and Structural Operators—0
High-resolution efficient image generation from WiFi CSI using a pretrained latent diffusion model—0
High-Resolution Vision Transformers for Pixel-Level Identification of Structural Components and Damage—0
High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning—0
hi-RF: Incremental Learning Random Forest for large-scale multi-class Data Classification—0
HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection—0
HMoE: Heterogeneous Mixture of Experts for Language Modeling—0
Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning—0
HoneyModels: Machine Learning Honeypots—0
HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified