Daniel Fried (CMU)
January 17, 2023
When:
January 30, 2023 @ 12:00 pm – 1:15 pm
Where:
Hackerman Hall B17, 3400 N. Charles Street, Baltimore, MD 21218
Events
CLSP Student Seminar
January 17, 2023
When:
January 27, 2023 @ 12:00 pm – 1:15 pm
Where:
Hackerman Hall B17, 3400 N. Charles Street, Baltimore, MD 21218
CLSP Student Seminar – Kelly Marchisio – “Efficient Multilingual NLP”
January 17, 2023
When:
January 23, 2023 @ 12:00 pm – 1:15 pm
Where:
Hackerman Hall B17, 3400 N. Charles Street, Baltimore, MD 21218
Abstract Kelly’s research spans three broad directions in multilingual NLP and representation learning: (1) diagnosing and fixing failure modes in translation technologies (2) data-efficient and low-resource NLP, and (3) compute-efficient NLP. This talk is an[…]
Student Seminar
September 29, 2022
When:
October 3, 2022 @ 12:00 pm – 1:15 pm
Where:
Hackerman Hall B17, 3400 N. Charles Street, Baltimore, MD 21218
Ariya Rastrow (Amazon)
September 15, 2022
When:
October 7, 2022 @ 12:00 pm – 1:15 pm
Where:
Hackerman Hall B17, 3400 N. Charles Street, Baltimore, MD 21218
Mark Hasegawa-Johnson (University of Illinois Urbana-Champaign) “Zipf’s Law Suggests a Three-Pronged Approach to Inclusive Speech Recognition”
September 15, 2022
When:
December 9, 2022 @ 12:00 pm – 1:15 pm
Where:
Hackerman Hall B17, 3400 N. Charles Street, Baltimore, MD 21218
Abstract Zipf’s law is commonly glossed by the aphorism “infrequent words are frequent,” but in practice, it has often meant that there are three types of words: frequent, infrequent, and out-of-vocabulary (OOV). Speech recognition solved[…]
Minje Kim (Indiana University) “Personalized Speech Enhancement: Data- and Resource-Efficient Machine Learning”
September 15, 2022
When:
December 2, 2022 @ 12:00 pm – 1:15 pm
Where:
Hackerman Hall B17, 3400 N. Charles Street, Baltimore, MD 21218
Abstract One of the keys to success in machine learning applications is to improve each user’s personal experience via personalized models. A personalized model can be a more resource-efficient solution than a general-purpose model, too,[…]
