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 2651–2675 of 4891 papers

TitleStatusHype
Efficient Budget Allocation for Large-Scale LLM-Enabled Virtual Screening—0
Sampling Constrained Continuous Probability Distributions: A Review—0
Sampling-guided Heterogeneous Graph Neural Network with Temporal Smoothing for Scalable Longitudinal Data Imputation—0
Sampling Strategies for Real-Time Action Recognition—0
Sampling Techniques for Streaming Cross Document Coreference Resolution—0
SAND: One-Shot Feature Selection with Additive Noise Distortion—0
SANN-PSZ: Spatially Adaptive Neural Network for Head-Tracked Personal Sound Zones—0
SAR Image Despeckling Using Quadratic-Linear Approximated L1-Norm—0
SAR Imaging of Moving Target based on Knowledge-aided Two-dimensional Autofocus—0
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models—0
Scalable Co-Clustering for Large-Scale Data through Dynamic Partitioning and Hierarchical Merging—0
Scalable computation of prediction intervals for neural networks via matrix sketching—0
Scalable CP Decomposition for Tensor Learning using GPU Tensor Cores—0
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback—0
Scalable Gaussian Processes with Low-Rank Deep Kernel Decomposition—0
Scalable Global Solution Techniques for High-Dimensional Models in Dynare—0
Scalable imputation of genetic data with a discrete fragmentation-coagulation process—0
Scalable Machine Learning Algorithms using Path Signatures—0
Scalable Non-linear Learning with Adaptive Polynomial Expansions—0
Scalable Nonlinear Learning with Adaptive Polynomial Expansions—0
Scalable Smartphone Cluster for Deep Learning—0
Scalable Subsampling Inference for Deep Neural Networks—0
Scalable Vehicle Re-Identification via Self-Supervision—0
Scale-free Unconstrained Online Learning for Curved Losses—0
ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified