We discuss the relevance of the recent machine learning (ML) literature for economics and
econometrics. First we discuss the differences in goals, methods, and settings between the …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

How to transfer the semantic information in a sentence to a computable numerical
embedding form is a fundamental problem in natural language processing. An informative …

Since the invention of word2vec, the skip-gram model has significantly advanced the
research of network embedding, such as the recent emergence of the DeepWalk, LINE, PTE …

Given time series data, how can we answer questions like what will happen in the
future?''and how did we get here?''These sorts of probabilistic inference questions are …

The recent tremendous success of unsupervised word embeddings in a multitude of
applications raises the obvious question if similar methods could be derived to improve …

Recently, Generative Diffusion Models (GDMs) have showcased their remarkable
capabilities in learning and generating images. A large community of GDMs has naturally …

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …

Words are not created equal. In fact, they form an aristocratic graph with a latent hierarchical
structure that the next generation of unsupervised learned word embeddings should reveal …

Instruction tuning has unlocked powerful capabilities in large language models (LLMs),
effectively using combined datasets to develop generalpurpose chatbots. However, real …