Curriculum learning (CL) is a training strategy that trains a machine learning model from
easier data to harder data, which imitates the meaningful learning order in human curricula …

Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is
pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

Large datasets have become commonplace in NLP research. However, the increased
emphasis on data quantity has made it challenging to assess the quality of data. We …

In the contemporary exam-driven domain of education, each time a new technology
transpires, societies want to know how it can be used to make kids get superior grades, how …

Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics
of neural networks as they train on single classification tasks. Our goal is to understand …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding
catastrophic forgetting--remains a key obstacle to achieving human-level intelligence. The …

Human attribute analysis is a challenging task in the field of computer vision. One of the
significant difficulties is brought from largely imbalance-distributed data. Conventional …

Inventors have long dreamed of creating machines that think. Ancient Greek myths tell of
intelligent objects, such as animated statues of human beings and tables that arrive full of …