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 1611–1620 of 4891 papers

TitleStatusHype
Data denoising with self consistency, variance maximization, and the Kantorovich dominance—0
Entropy Adaptive Decoding: Dynamic Model Switching for Efficient Inference—0
Contextual Memory Reweaving in Large Language Models Using Layered Latent State Reconstruction—0
Composite Gaussian Processes Flows for Learning Discontinuous Multimodal Policies—0
Orientation-aware interaction-based deep material network in polycrystalline materials modeling—0
Learning to generate physical ocean states: Towards hybrid climate modeling—0
Boosting Multimodal Reasoning with MCTS-Automated Structured Thinking—0
An Efficient Local Search Approach for Polarized Community Discovery in Signed Networks—0
Efficient Dynamic Scene Editing via 4D Gaussian-based Static-Dynamic Separation—0
Ilargi: a GPU Compatible Factorized ML Model Training Framework—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified