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 3701–3725 of 4891 papers

TitleStatusHype
Automatic Operator-level Parallelism Planning for Distributed Deep Learning -- A Mixed-Integer Programming Approach—0
Autonomous and Connected Intersection Crossing Traffic Management using Discrete-Time Occupancies Trajectory—0
Autonomous Structural Memory Manipulation for Large Language Models Using Hierarchical Embedding Augmentation—0
Autonomous Wheel Loader Trajectory Tracking Control Using LPV-MPC—0
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders—0
Auto Tensor Singular Value Thresholding: A Non-Iterative and Rank-Free Framework for Tensor Denoising—0
AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features—0
A VAE-Bayesian Deep Learning Scheme for Solar Generation Forecasting based on Dimensionality Reduction—0
Avoiding Obfuscation with Prover-Estimator Debate—0
Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection—0
Backhaul-Constrained Multi-Cell Cooperation Leveraging Sparsity and Spectral Clustering—0
Backpropagation-free Spiking Neural Networks with the Forward-Forward Algorithm—0
Backstepping Mean-Field Density Control for Large-Scale Heterogeneous Nonlinear Stochastic Systems—0
Backup Plan Constrained Model Predictive Control—0
Backup Plan Constrained Model Predictive Control with Guaranteed Stability—0
Balancing Computational Efficiency and Forecast Error in Machine Learning-based Time-Series Forecasting: Insights from Live Experiments on Meteorological Nowcasting—0
Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications—0
Balancing Performance and Efficiency in Zero-shot Robotic Navigation—0
Balancing training time vs. performance with Bayesian Early Pruning—0
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control—0
Bandit-Driven Batch Selection for Robust Learning under Label Noise—0
Basis functions nonlinear data-enabled predictive control: Consistent and computationally efficient formulations—0
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography—0
Batches Stabilize the Minimum Norm Risk in High Dimensional Overparameterized Linear Regression—0
Batch-FPM: Random batch-update multi-parameter physical Fourier ptychography neural network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified