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

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Showing 4251–4275 of 4891 papers

TitleStatusHype
Conditional Expectation Propagation—0
A Statistical Learning Approach to Reactive Power Control in Distribution Systems—0
Learning Mixtures of Plackett-Luce Models from Structured Partial OrdersCode0
CrevNet: Conditionally Reversible Video Prediction—0
ProLFA: Representative Prototype Selection for Local Feature AggregationCode0
Context-endcoding for neural network based skull stripping in magnetic resonance imaging—0
Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud RegistrationCode0
Multiphase flow prediction with deep neural networks—0
SDCNet: Smoothed Dense-Convolution Network for Restoring Low-Dose Cerebral CT PerfusionCode0
Coping With Simulators That Don’t Always Return—0
Scale-Equivariant Steerable NetworksCode0
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
Accelerated Structured Alternating Projections for Robust Spectrally Sparse Signal RecoveryCode0
Commonsense Knowledge Base Completion with Structural and Semantic ContextCode0
Neural Multisensory Scene InferenceCode0
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and MappingCode1
Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction MethodCode0
Multiagent Rollout Algorithms and Reinforcement LearningCode0
Integrated Triaging for Fast Reading Comprehension—0
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural NetworksCode0
Online Learning of Dynamical Systems: An Operator Theoretic Approach—0
A Refined Equilibrium Generative Adversarial Network for Retinal Vessel Segmentation—0
Meta-Learning for Variational Inference—0
Deep Auto-Deferring Policy for Combinatorial Optimization—0
Extreme Value k-means Clustering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified