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PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning
However, current approaches typically either train adapters on individual tasks or distill shared knowledge from source tasks, failing to fully exploit task-specific knowledge and the correlation between source and target tasks.
Calibration of Time-Series Forecasting Transformers: Detecting and Adapting Context-Driven Distribution Shift
In this paper, we introduce a universal calibration methodology for the detection and adaptation of CDS with a trained Transformer model.
MCEN: Bridging Cross-Modal Gap between Cooking Recipes and Dish Images with Latent Variable Model
Nowadays, driven by the increasing concern on diet and health, food computing has attracted enormous attention from both industry and research community.
A Machine Learning Framework for Data Ingestion in Document Images
Paper documents are widely used as an irreplaceable channel of information in many fields, especially in financial industry, fostering a great amount of demand for systems which can convert document images into structured data representations.
Reference Network for Neural Machine Translation
Neural Machine Translation (NMT) has achieved notable success in recent years.
