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 2151–2175 of 4891 papers

TitleStatusHype
Attention-Driven Hierarchical Reinforcement Learning with Particle Filtering for Source Localization in Dynamic Fields—0
Deep Video Demoireing via Compact Invertible Dyadic Decomposition—0
Deep Unsupervised Hashing by Distilled Smooth Guidance—0
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction—0
How Much is Enough? A Study on Diffusion Times in Score-based Generative Models—0
How Useful are Gradients for OOD Detection Really?—0
Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework—0
HydraNets: Specialized Dynamic Architectures for Efficient Inference—0
Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data—0
HoneyModels: Machine Learning Honeypots—0
Deep Tensor Network—0
DeeptDCS: Deep Learning-Based Estimation of Currents Induced During Transcranial Direct Current Stimulation—0
A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables—0
HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting—0
DeepSNR: A deep learning foundation for offline gravitational wave detection—0
A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation—0
Deep Signal Recovery with One-Bit Quantization—0
A tree-based kernel for graphs with continuous attributes—0
Active Semi-Supervised Learning by Exploring Per-Sample Uncertainty and Consistency—0
How Analysis Can Teach Us the Optimal Way to Design Neural Operators—0
A kinetic approach to consensus-based segmentation of biomedical images—0
DeepRLS: A Recurrent Network Architecture with Least Squares Implicit Layers for Non-blind Image Deconvolution—0
HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection—0
Deep Reinforcement Learning with Plasticity Injection—0
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified