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 4076–4100 of 4891 papers

TitleStatusHype
Improving Spiking Sparse Recovery via Non-Convex Penalties—0
Event-based update of synapses in voltage-based learning rules—0
Empirical Fourier Decomposition: An Accurate Adaptive Signal Decomposition Method—0
A computationally efficient physiologically comprehensive 3D-0D closed-loop model of the heart and circulation—0
Accurate and efficient Simulation of very high-dimensional Neural Mass Models with distributed-delay Connectome Tensors—0
Collaborative Group Learning—0
Fixed Inducing Points Online Bayesian Calibration for Computer Models with an Application to a Scale-Resolving CFD Simulation—0
Extracting Optimal Solution Manifolds using Constrained Neural Optimization—0
Efficient Folded Attention for 3D Medical Image Reconstruction and Segmentation—0
Low-Rank Training of Deep Neural Networks for Emerging Memory Technology—0
Isotonic regression with unknown permutations: Statistics, computation, and adaptation—0
Efficiency in Real-time Webcam Gaze Tracking—0
Is the space complexity of planted clique recovery the same as that of detection?—0
Locally induced Gaussian processes for large-scale simulation experiments—0
ETC-NLG: End-to-end Topic-Conditioned Natural Language GenerationCode0
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems—0
An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation—0
Fuzzy SLIC: Fuzzy Simple Linear Iterative Clustering—0
Adversarial Imitation Learning via Random Search—0
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables—0
Intelligence plays dice: Stochasticity is essential for machine learning—0
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering—0
Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting—0
DensE: An Enhanced Non-commutative Representation for Knowledge Graph Embedding with Adaptive Semantic HierarchyCode0
A Survey on Large-scale Machine LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified