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 3976–4000 of 4891 papers

TitleStatusHype
A Modular Framework for Distributed Model Predictive Control of Nonlinear Continuous-Time Systems (GRAMPC-D)—0
Denoising Atmospheric Temperature Measurements Taken by the Mars Science Laboratory on the Martian Surface—0
Computationally and Statistically Efficient Truncated Regression—0
Low-complexity decentralized algorithm for aggregate load control of thermostatic loadsCode0
Sparse Gaussian Process Variational AutoencodersCode0
Federated Bayesian Optimization via Thompson SamplingCode1
Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire DetectionCode1
Multi-Agent Motion Planning using Deep Learning for Space Applications—0
CIMON: Towards High-quality Hash Codes—0
Decomposing non-stationary signals with time-varying wave-shape functionsCode1
Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank EstimationCode0
Graph Deep Factors for Forecasting—0
Sample and Computationally Efficient Stochastic Kriging in High Dimensions—0
How important are faces for person re-identification?—0
Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental EvaluationCode1
Towards human-level performance on automatic pose estimation of infant spontaneous movements—0
Mining Truck Platooning Patterns Through Massive Trajectory Data—0
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image ClassificationCode1
Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation LearningCode1
SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors—0
Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits—0
Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control—0
A Transformer-based Framework for Multivariate Time Series Representation LearningCode1
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective InferenceCode0
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified