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 1576–1600 of 4891 papers

TitleStatusHype
Explainable Bayesian Recurrent Neural Smoother to Capture Global State Evolutionary Correlations—0
Distribution-Aware Sampling and Weighted Model Counting for SAT—0
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data—0
Efficient Relational Learning with Hidden Variable Detection—0
Efficient Representations for High-Cardinality Categorical Variables in Machine Learning—0
An efficient optimization based microstructure reconstruction approach with multiple loss functions—0
Efficient safe learning for controller tuning with experimental validation—0
American option pricing using generalised stochastic hybrid systems—0
Distributionally Robust Model Predictive Control with Total Variation Distance—0
Efficient Second-Order Neural Network Optimization via Adaptive Trust Region Methods—0
BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning—0
Distributed Traffic Signal Control via Coordinated Maximum Pressure-plus-Penalty—0
Efficient Sound Field Reconstruction with Conditional Invertible Neural Networks—0
Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement—0
Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net—0
Backhaul-Constrained Multi-Cell Cooperation Leveraging Sparsity and Spectral Clustering—0
Efficient State-Space Inference of Periodic Latent Force Models—0
Efficient Stochastic Gradient Hard Thresholding—0
Uncertainty-aware Efficient Subgraph Isomorphism using Graph Topology—0
Distributed Sketching Methods for Privacy Preserving Regression—0
Efficient Tensor Contraction via Fast Count Sketch—0
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection—0
Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning—0
Distributed Flexible Nonlinear Tensor Factorization—0
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified