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 2801–2825 of 4891 papers

TitleStatusHype
SparseDet: A Simple and Effective Framework for Fully Sparse LiDAR-based 3D Object Detection—0
Sparse Dictionary Learning for Image Recovery by Iterative Shrinkage—0
Sparse Diffusion-Convolutional Neural Networks—0
Sparse Distance Weighted Discrimination—0
Sparse Exact PGA on Riemannian Manifolds—0
Sparse Imagination for Efficient Visual World Model Planning—0
Sparse Least Squares Low Rank Kernel Machines—0
Cross-token Modeling with Conditional Computation—0
Sparse Optimization for Transfer Learning: A L0-Regularized Framework for Multi-Source Domain Adaptation—0
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines—0
Sparse Principal Component Analysis via Variable Projection—0
Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory—0
Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN—0
Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting—0
Spatially Adaptive Cloth Regression with Implicit Neural Representations—0
Spatially Covariant Lesion Segmentation—0
Spatio-temporal Causal Learning for Streamflow Forecasting—0
Spatio-temporal Fourier Transformer (StFT) for Long-term Dynamics Prediction—0
Spatio-temporal point processes with deep non-stationary kernels—0
SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization—0
Speckle Reduction with Trained Nonlinear Diffusion Filtering—0
Spectral Clustering for Discrete Distributions—0
Spectral Densities, Structured Noise and Ensemble Averaging within Open Quantum Dynamics—0
Spectral folding and two-channel filter-banks on arbitrary graphs—0
Spectral Graph Matching and Regularized Quadratic Relaxations I: The Gaussian Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified