Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

With the widespread use of information technologies, information networks are becoming
increasingly popular to capture complex relationships across various disciplines, such as …

By encoding time series as a string of numerical digits, we can frame time series forecasting
as next-token prediction in text. Develo** this approach, we find that large language …

Transformers achieve remarkable performance in several tasks but due to their quadratic
complexity, with respect to the input's length, they are prohibitively slow for very long …

We introduce a new language representation model called BERT, which stands for
Bidirectional Encoder Representations from Transformers. Unlike recent language …

We introduce a new language representation model called BERT, which stands for
Bidirectional Encoder Representations from Transformers. Unlike recent language …

Given e-commerce scenarios that user profiles are invisible, session-based
recommendation is proposed to generate recommendation results from short sessions …

This work lists and describes the main recent strategies for building fixed-length, dense and
distributed representations for words, based on the distributional hypothesis. These …

In this work we propose a simple and efficient framework for learning sentence
representations from unlabelled data. Drawing inspiration from the distributional hypothesis …

Kwang Gi Kim https://doi. org/10.4258/hir. 2016.22. 4.351 ing those who are beginning their
careers in deep learning and artificial intelligence research. The other target audience …