David Snyder
April 15, 2019
When:
April 15, 2019 @ 12:00 pm – 1:15 pm
Where:
Hackerman Hall B17, 3400 N. Charles Street
Events
David Snyder
April 8, 2019
When:
April 15, 2019 @ 12:00 pm – 1:15 pm
Where:
Hackerman Hall B17, 3400 N. Charles Street, Baltimore, MD 21218
Jessica Ouyang (Columbia University) “Adapting Automatic Summarization to New Sources of Information”
February 27, 2019
When:
March 11, 2019 @ 12:00 pm – 1:15 pm
Where:
Hackerman Hall B17, 3400 N. Charles Street
Abstract English-language news articles are no longer necessarily the best source of information. The Web allows information to spread more quickly and travel farther: first-person accounts of breaking news events pop up on social media,[…]
Rediet Abebe (Cornell University) “Using Search Queries to Understand Health Information Needs in Africa”
February 27, 2019
When:
March 4, 2019 @ 12:00 pm – 1:15 pm
Abstract: Access to healthcare and health information is of major global concern. The stark inequality in the availability of health data by country, demographic groups, and socioeconomic status impedes the identification of major public[…]
Kenton Murray (University of Notre Dame) “Learning Neural Network Hyperparameters for Machine Translation”
February 21, 2019
When:
February 25, 2019 @ 12:00 pm – 1:15 pm
Abstract: Kenton Murray is a 5th year PhD Candidate at the University of Notre Dame working with David Chiang on methods for improving Neural Machine Translation for Low-Resource and Morphologically Rich Language Pairs. Prior to[…]
Sorami Hisamoto
January 29, 2019
When:
May 3, 2019 @ 12:00 pm – 1:15 pm
Where:
Hackerman Hall B17, 3400 N Charles St, Baltimore, MD 21218, USA
Le Song (Georgia Institute of Technology) “Can Graph Neural Networks Help Logic Inference?”
January 29, 2019
When:
April 29, 2019 @ 12:00 pm – 1:15 pm
Where:
Hackerman Hall B17, 3400 N Charles St, Baltimore, MD 21218, USA
Abstract Combining perceptual learning and logic inference/symbolic reasoning has been a long standing goal of AI. Graph neural networks are powerful representation learning tools for graph data, including but not limited to social networks, molecular[…]
