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 3076–3100 of 4891 papers

TitleStatusHype
Deep Representation Learning for Prediction of Temporal Event Sets in the Continuous Time DomainCode0
Maximal Volume Matrix Cross Approximation for Image Compression and Least Squares Solution—0
PlaceNav: Topological Navigation through Place Recognition—0
Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit FieldsCode0
Benchmarking and In-depth Performance Study of Large Language Models on Habana Gaudi Processors—0
Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections—0
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics—0
An Enhanced Low-Resolution Image Recognition Method for Traffic Environments—0
A parsimonious, computationally efficient machine learning method for spatial regression—0
HyperLISTA-ABT: An Ultra-light Unfolded Network for Accurate Multi-component Differential Tomographic SAR Inversion—0
An analysis of the derivative-free loss method for solving PDEs—0
Partial Transport for Point-Cloud Registration—0
Advanced Volleyball Stats for All Levels: Automatic Setting Tactic Detection and Classification with a Single CameraCode0
Balancing Computational Efficiency and Forecast Error in Machine Learning-based Time-Series Forecasting: Insights from Live Experiments on Meteorological Nowcasting—0
Accelerating Machine Learning Algorithms with Adaptive Sampling—0
Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited StalenessCode0
Nonparametric mixed logit model with market-level parameters estimated from market share data—0
Efficient N:M Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design—0
Bridging Sensor Gaps via Attention Gated Tuning for Hyperspectral Image ClassificationCode0
Benchmarking quantized LLaMa-based models on the Brazilian Secondary School Exam—0
Source-free Active Domain Adaptation for Diabetic Retinopathy Grading Based on Ultra-wide-field Fundus ImageCode0
Learning Dynamic MRI Reconstruction with Convolutional Network Assisted Reconstruction Swin Transformer—0
Posterior sampling algorithms for unsupervised speech enhancement with recurrent variational autoencoder—0
A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis—0
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified