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 2701–2710 of 4891 papers

TitleStatusHype
SELD-Mamba: Selective State-Space Model for Sound Event Localization and Detection with Source Distance Estimation—0
Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models—0
Selective Video Object Cutout—0
Self-Attentional Models for Lattice Inputs—0
Self-Data Distillation for Recovering Quality in Pruned Large Language Models—0
Self-Driving Car Racing: Application of Deep Reinforcement Learning—0
SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay—0
Self-Supervised Camera Self-Calibration from Video—0
Self-Supervised Learning of Compressed Video Representations—0
Self-Supervised Learning of Perceptually Optimized Block Motion Estimates for Video Compression—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified