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 4051–4075 of 4891 papers

TitleStatusHype
Denoising Atmospheric Temperature Measurements Taken by the Mars Science Laboratory on the Martian Surface—0
Sparse Gaussian Process Variational AutoencodersCode0
CIMON: Towards High-quality Hash Codes—0
Multi-Agent Motion Planning using Deep Learning for Space Applications—0
Graph Deep Factors for Forecasting—0
Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank EstimationCode0
Sample and Computationally Efficient Stochastic Kriging in High Dimensions—0
How important are faces for person re-identification?—0
Towards human-level performance on automatic pose estimation of infant spontaneous movements—0
Mining Truck Platooning Patterns Through Massive Trajectory Data—0
Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits—0
SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors—0
Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control—0
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective InferenceCode0
Episodic Memory for Learning Subjective-Timescale Models—0
A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks—0
A Survey on Deep Learning Methods for Semantic Image Segmentation in Real-Time—0
Towards a Systematic Computational Framework for Modeling Multi-Agent Decision-Making at Micro Level for Smart Vehicles in a Smart World—0
Event-Driven Receding Horizon Control for Distributed Estimation in Network Systems—0
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control—0
Self-Weighted Robust LDA for Multiclass Classification with Edge Classes—0
A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks—0
Region Growing with Convolutional Neural Networks for Biomedical Image Segmentation—0
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based ModelsCode0
Overlapping community detection in networks via sparse spectral decompositionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified