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 2451–2475 of 4891 papers

TitleStatusHype
Curriculum reinforcement learning for quantum architecture search under hardware errors—0
Representation Surgery for Multi-Task Model MergingCode1
Nonlinear subspace clustering by functional link neural networks—0
A Survey on Graph Condensation—0
kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection—0
Scalable Multi-modal Model Predictive Control via Duality-based Interaction PredictionsCode1
A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography—0
Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations—0
Multivariate Probabilistic Time Series Forecasting with Correlated ErrorsCode1
Parallel Spiking Unit for Efficient Training of Spiking Neural Networks—0
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State SpacesCode3
Reimagining Reality: A Comprehensive Survey of Video Inpainting Techniques—0
Speeding up and reducing memory usage for scientific machine learning via mixed precisionCode1
Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models—0
EmoDM: A Diffusion Model for Evolutionary Multi-objective Optimization—0
Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier SeriesCode0
Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks—0
OMPGPT: A Generative Pre-trained Transformer Model for OpenMP—0
Statistical Significance of Feature Importance RankingsCode0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial AttacksCode2
Open-RadVLAD: Fast and Robust Radar Place RecognitionCode1
Adaptive Deep Learning for Efficient Visual Pose Estimation aboard Ultra-low-power Nano-drones—0
Linear Periodically Time-Variant Digital PLL Phase Noise Modeling Using Conversion Matrices and Uncorrelated UpsamplingCode0
Spectral Clustering for Discrete Distributions—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified