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Found 5 papers, 1 papers with code
Attention-aware Post-training Quantization without Backpropagation
Quantization is a promising solution for deploying large-scale language models (LLMs) on resource-constrained devices.
Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers
Through extensive experiments on various language models and complexity analysis, we demonstrate that aespa is accurate and efficient in quantizing Transformer models.
Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning
The more new features that are being added to smartphones, the harder it becomes for users to find them.
Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI
For this reason, data-centric approaches are crucial for the automation of machine learning operation pipeline.
Neural Sequence-to-grid Module for Learning Symbolic Rules
Logical reasoning tasks over symbols, such as learning arithmetic operations and computer program evaluations, have become challenges to deep learning.
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