Over the past few years, neural networks have re-emerged as powerful machine-learning
models, yielding state-of-the-art results in fields such as image recognition and speech …

We review machine learning methods employing positive definite kernels. These methods
formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of …

The advent of large language models trained on code (code LLMs) has led to significant
progress in language-to-code generation. State-of-the-art approaches in this area combine …

With direct access to human-written reference as memory, retrieval-augmented generation
has achieved much progress in a wide range of text generation tasks. Since better memory …

Neural networks are a family of powerful machine learning models and this book focuses on
their application to natural language data. The first half of the book (Parts I and II) covers the …

Sequence-to-sequence neural networks have recently achieved great success in abstractive
summarization, especially through fine-tuning large pre-trained language models on the …

BERTs are incapable of processing long texts due to its quadratically increasing memory
and time consumption. The straightforward thoughts to address this problem, such as slicing …

We present a novel attention-based recurrent neural network for joint extraction of entity
mentions and relations. We show that attention along with long short term memory (LSTM) …

Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems
from sequence labeling to span prediction. Despite its preliminary effectiveness, the span …

A promising approach for improving reasoning in large language models is to use process
reward models (PRMs). PRMs provide feedback at each step of a multi-step reasoning trace …