CS 598 PEN - Efficiency in NLP
Last offered Fall 2023
Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.
Large-scale deep learning models have profoundly shaped the landscape of natural language processing (NLP) and AI. However, the rising computational demands of these systems have not only elevated the entry barriers to cutting-edge research, but also raised environmental concerns. Recognizing these challenges, the research community is intensively working towards enhancing the efficiency of these large models, aiming to make them more accessible for practitioners with limited resources and to address environmental concerns. In this course, we will thoroughly explore the recent progress in this area, with a focus on NLP. Though this course is primarily designed for graduate students, motivated undergraduates with suitable backgrounds are also welcome. Prior research experience in related fields (such as natural language processing, machine learning, vision, etc.) and proficiency in Python and modern deep learning frameworks are assumed. The course format includes introductory lect
|Efficiency in NLP||PEN||46983||S3||4||0930 - 1045||T R||158 Loomis Laboratory||Hao Peng|