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 1651–1660 of 4891 papers

TitleStatusHype
Architecting Digital Twins for Intelligent Transportation Systems—0
A few-shot Label Unlearning in Vertical Federated Learning—0
Contingency-constrained economic dispatch with safe reinforcement learning—0
Contextual Reinforcement in Multimodal Token Compression for Large Language Models—0
Contextual Optimization under Covariate Shift: A Robust Approach by Intersecting Wasserstein Balls—0
Contextual Multinomial Logit Bandits with General Value Functions—0
Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing—0
AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds—0
Contextual Memory Reweaving in Large Language Models Using Layered Latent State Reconstruction—0
Contextually Structured Token Dependency Encoding for Large Language Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified