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 1301–1310 of 4891 papers

TitleStatusHype
Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle PhysicsCode0
ICE-BA: Incremental, Consistent and Efficient Bundle Adjustment for Visual-Inertial SLAMCode0
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture DesignCode0
Adapting GT2-FLS for Uncertainty Quantification: A Blueprint Calibration StrategyCode0
DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis Across the SpectrumCode0
Flover: A Temporal Fusion Framework for Efficient Autoregressive Model Parallel InferenceCode0
FlowKac: An Efficient Neural Fokker-Planck solver using Temporal Normalizing flows and the Feynman Kac-FormulaCode0
Discovering and Deciphering Relationships Across Disparate Data ModalitiesCode0
Auto-nnU-Net: Towards Automated Medical Image SegmentationCode0
Deep-learning the Latent Space of Light TransportCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified