Research Post
Abstract: A method uses natural language for visual analysis of a dataset. A data visualization is displayed based on a first dataset. The method then extracts analytic phrases from a natural language command related to the data visualization. The method computes conversation centers based on the analytic phrases and computes analytical functions associated with the conversation centers, thereby creating functional phrases. The method updates the data visualization according to the functional phrases. The method then extracts analytic phrases from a second natural language command related to the updated data visualization, and computes temporary conversation centers from these analytic phrases. The method then computes cohesion between the first analytic phrases and the second analytic phrases to build a set of conversation centers, and computes analytical functions from this set of conversation centers, thereby creating functional phrases. The method updates the data visualization based on the created functional phrases.
Aug 8th 2022
Research Post
Read this research paper co-authored by Canada CIFAR AI Chair Angel Chang: Learning Expected Emphatic Traces for Deep RL
Jul 22nd 2022
Research Post
Read this research paper, co-authored by Canada CIFAR AI Chair Angel Chang: D3Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding
Jul 7th 2022
Research Post
Read this research paper, co-authored by Fellow & Canada CIFAR AI Chair Russ Greiner: Prediction of Obsessive-Compulsive Disorder: Importance of neurobiology-aided feature design and cross-diagnosis transfer learning
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