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 1221–1230 of 4891 papers

TitleStatusHype
HER2 Expression Prediction with Flexible Multi-Modal Inputs via Dynamic Bidirectional Reconstruction—0
Reinforcement Learning-Driven Plant-Wide Refinery Planning Using Model Decomposition—0
Successive Jump and Mode Decomposition—0
Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application—0
SAEs Can Improve Unlearning: Dynamic Sparse Autoencoder Guardrails for Precision Unlearning in LLMs—0
Hypergraph Vision Transformers: Images are More than Nodes, More than Edges—0
Slicing the Gaussian Mixture Wasserstein DistanceCode0
Cluster-Driven Expert Pruning for Mixture-of-Experts Large Language ModelsCode0
V2V3D: View-to-View Denoised 3D Reconstruction for Light-Field Microscopy—0
How to Detect and Defeat Molecular Mirage: A Metric-Driven Benchmark for Hallucination in LLM-based Molecular Comprehension—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified