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 4701–4725 of 4891 papers

TitleStatusHype
GraphDART: Graph Distillation for Efficient Advanced Persistent Threat Detection—0
Graph Deep Factors for Forecasting—0
GISExplainer: On Explainability of Graph Neural Networks via Game-theoretic Interaction Subgraphs—0
Graph Guided Diffusion: Unified Guidance for Conditional Graph Generation—0
Label Efficient Regularization and Propagation for Graph Node Classification—0
Graphlets correct for the topological information missed by random walks—0
A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty—0
Graph Neural Network Enabled Pinching Antennas—0
Graph Neural Networks for Emulation of Finite-Element Ice Dynamics in Greenland and Antarctic Ice Sheets—0
Graph Neural Networks for Travel Distance Estimation and Route Recommendation Under Probabilistic Hazards—0
Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems—0
Graph Probability Aggregation Clustering—0
Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application—0
Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks—0
Graph sampling for node embedding—0
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs—0
Graph signal processing for machine learning: A review and new perspectives—0
GreedyPixel: Fine-Grained Black-Box Adversarial Attack Via Greedy Algorithm—0
GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models—0
Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data—0
Grid Evolution for Doubly Fractional Channel Estimation in OTFS Systems—0
Ground-based image deconvolution with Swin Transformer UNet—0
Group Sparse Coding—0
gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs—0
Guaranteed-Safe MPPI Through Composite Control Barrier Functions for Efficient Sampling in Multi-Constrained Robotic Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified