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Found 7 papers, 3 papers with code
Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models
Experiments show that QST can reduce the total memory footprint by up to 2. 3 $\times$ and speed up the finetuning process by up to 3 $\times$ while achieving competent performance compared with the state-of-the-art.
SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification
Our evaluation shows that SpecInfer outperforms existing LLM serving systems by 1. 5-2. 8x for distributed LLM inference and by 2. 6-3. 5x for offloading-based LLM inference, while preserving the same generative performance.
Cycle Consistent Probability Divergences Across Different Spaces
Discrepancy measures between probability distributions are at the core of statistical inference and machine learning.
Non-Asymptotic Performance Guarantees for Neural Estimation of $\mathsf{f}$-Divergences
Statistical distances (SDs), which quantify the dissimilarity between probability distributions, are central to machine learning and statistics.
ZQM at SemEval-2019 Task9: A Single Layer CNN Based on Pre-trained Model for Suggestion Mining
In our system, the input of convolutional neural network is the embedding vectors which are drawn from the pre-trained BERT model.
NLPZZX at SemEval-2018 Task 1: Using Ensemble Method for Emotion and Sentiment Intensity Determination
We participate in two subtasks for English tweets: EI-reg and V-reg.
Road Extraction by Deep Residual U-Net
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis.
