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Found 5 papers, 2 papers with code
Second-Order Fine-Tuning without Pain for LLMs:A Hessian Informed Zeroth-Order Optimizer
Fine-tuning large language models (LLMs) with classic first-order optimizers entails prohibitive GPU memory due to the backpropagation process.
Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting
Time series analysis is vital for numerous applications, and transformers have become increasingly prominent in this domain.
Computational Efficiency
Multivariate Time Series Forecasting
+2
SVQ: Sparse Vector Quantization for Spatiotemporal Forecasting
Moreover, we approximate the sparse regression process using a blend of a two-layer MLP and an extensive codebook.
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GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting
On the other hand, the long input sequence usually leads to large model size and high time complexity.
Unsupervised Learning of Deep-Learned Features from Breast Cancer Images
We demonstrate the effectiveness of the proposed approach for cancer detection in BRCA and show how the machine can choose the most appropriate clusters during the unsupervised learning procedure.
