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 1601–1625 of 4891 papers

TitleStatusHype
Exploring the effects of robotic design on learning and neural controlCode0
HSRMamba: Efficient Wavelet Stripe State Space Model for Hyperspectral Image Super-ResolutionCode0
Exploring the Open World Using Incremental Extreme Value MachinesCode0
FAMED-Net: A Fast and Accurate Multi-scale End-to-end Dehazing NetworkCode0
Commonsense Knowledge Base Completion with Structural and Semantic ContextCode0
Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction MethodCode0
A general framework for supporting economic feasibility of generator and storage energy systems through capacity and dispatch optimizationCode0
EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural NetworksCode0
Explain to Fix: A Framework to Interpret and Correct DNN Object Detector PredictionsCode0
A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral imagesCode0
Exploring Kolmogorov-Arnold Networks for Interpretable Time Series ClassificationCode0
Cortical surface registration using unsupervised learningCode0
ETC-NLG: End-to-end Topic-Conditioned Natural Language GenerationCode0
Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud DetectionCode0
Estimating Time-Varying Graphical ModelsCode0
COrAL: Order-Agnostic Language Modeling for Efficient Iterative RefinementCode0
BUZZ: Beehive-structured Sparse KV Cache with Segmented Heavy Hitters for Efficient LLM InferenceCode0
Exploring Molecule Generation Using Latent Space Graph DiffusionCode0
Coping With Simulators That Don't Always ReturnCode0
Enhancing Character-Level Understanding in LLMs through Token Internal Structure LearningCode0
Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST)Code0
Electric Field Models of Transcranial Magnetic Stimulation Coils with Arbitrary Geometries: Reconstruction from Incomplete Magnetic Field MeasurementsCode0
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based ModelsCode0
Scalable Regularised Joint Mixture ModelsCode0
End-to-end reconstruction meets data-driven regularization for inverse problemsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified