Tag: Alumnus Talk
All the talks with the tag "Alumnus Talk".
Balancing Explainability-Accuracy of Complex Models
Poushali SenguptaPublished: at 03:45 PMThis talk covers the Explainability through Correlation Impact Ratio (ExCIR) method, which balances explainability and accuracy in complex machine learning models, especially with independent features. ExCIR creates a streamlined data space from the original dataset, requiring fewer input samples while maintaining model accuracy. The Correlation Impact Ratio (CIR) quantifies each feature’s contribution to the model’s output, accounting for feature uncertainty regarding its presence or absence. Using the CAUEEG dataset related to dementia, we demonstrate that ExCIR maintains accuracy while enhancing explainability through feature impact scores. The method reliably ranks feature importance in both original and simplified models, validating its consistency.
End-to-End Task-Oriented Dialogue Systems
Nalin KumarPublished: at 03:45 PMThis talk covers recent developments in dialogue systems, focusing on task-oriented systems that help users accomplish specific goals, like booking services or providing customer support. It begins by distinguishing between open-domain dialogue systems, which handle general conversations, and task-oriented systems, which are goal-driven. The talk provides an overview of traditional approaches, including statistical and neural network-based methods, and highlights the shift toward end-to-end architectures, along with existing challenges in system evaluation. Despite advancements, task-oriented systems often generate dull, misaligned responses. Moreover, the speaker presents recent work, accepted at NAACL 2024, that improves the naturalness and alignment of responses in task-oriented settings without compromising task success.