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 4251–4275 of 4891 papers

TitleStatusHype
Accelerated Alternating Projections for Robust Principal Component AnalysisCode0
A Cascaded Dilated Convolution Approach for Mpox Lesion ClassificationCode0
Curbing Task Interference using Representation Similarity-Guided Multi-Task Feature SharingCode0
A Bootstrap-based Method for Testing Network SimilarityCode0
CrowdSplat: Exploring Gaussian Splatting For Crowd RenderingCode0
A LoRA-Based Approach to Fine-Tuning LLMs for Educational Guidance in Resource-Constrained SettingsCode0
Learning-based model augmentation with LFRsCode0
TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly DetectionCode0
Learning Causal Dynamics Models in Object-Oriented EnvironmentsCode0
Bi-fidelity Variational Auto-encoder for Uncertainty QuantificationCode0
Efficient End-to-End Video Question Answering with Pyramidal Multimodal TransformerCode0
Cross Language Soccer Framework: An Open Source Framework for the RoboCup 2D Soccer SimulationCode0
SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with MambaCode0
Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random BasesCode0
Learning Decision Trees and Forests with Algorithmic RecourseCode0
Robust optimal well control using an adaptive multi-grid reinforcement learning frameworkCode0
Efficient Deployment of Transformer Models in Analog In-Memory Computing HardwareCode0
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive UncertaintiesCode0
Learning Directly from Grammar Compressed TextCode0
YOLIC: An Efficient Method for Object Localization and Classification on Edge DevicesCode0
Cross-D Conv: Cross-Dimensional Transferable Knowledge Base via Fourier Shifting OperationCode0
Learning Dynamical Systems Encoding Non-Linearity within Space CurvatureCode0
A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral imagesCode0
The Power of Negative Zero: Datatype Customization for Quantized Large Language ModelsCode0
Learning Efficient Representations of Neutrino Telescope EventsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified