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

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Showing 2661–2670 of 4891 papers

TitleStatusHype
Interpretable Neural PDE Solvers using Symbolic Frameworks—0
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation—0
Video Frame Interpolation with Many-to-many Splatting and Spatial Selective Refinement—0
Triplet Attention Transformer for Spatiotemporal Predictive Learning—0
Optimization-Free Test-Time Adaptation for Cross-Person Activity RecognitionCode1
Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation—0
Lookup Table meets Local Laplacian Filter: Pyramid Reconstruction Network for Tone MappingCode1
Image Prior and Posterior Conditional Probability Representation for Efficient Damage Assessment—0
Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach—0
Efficient safe learning for controller tuning with experimental validation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified