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 491–500 of 4891 papers

TitleStatusHype
HER2 Expression Prediction with Flexible Multi-Modal Inputs via Dynamic Bidirectional Reconstruction—0
Successive Jump and Mode Decomposition—0
Slicing the Gaussian Mixture Wasserstein DistanceCode0
Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application—0
Reinforcement Learning-Driven Plant-Wide Refinery Planning Using Model Decomposition—0
TensorNEAT: A GPU-accelerated Library for NeuroEvolution of Augmenting TopologiesCode3
Hypergraph Vision Transformers: Images are More than Nodes, More than Edges—0
Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical ImagingCode1
SAEs Can Improve Unlearning: Dynamic Sparse Autoencoder Guardrails for Precision Unlearning in LLMs—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