Abstract The success of Deep Learning (DL) on visual perception has led to rapid progress on Reinforcement Learning (RL) tasks with visual inputs. More recently, Deep Learning is showing promise at certain kinds of supervised[…]

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Abstract In a multi-domain conversational system, such as Alexa, a key challenge is to enable transfer of actionable information across applications. Most often these systems have evolved independently, with their own local schemas and interoperability[…]

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Abstract Neural networks have recently become ubiquitous in natural language processing systems, but we typically have little understanding of specific capabilities of these networks beyond their overall accuracy in an applied task. The present work[…]

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Absract In this talk, you will get an exposure to the various types of deep learning frameworks – declarative and imperative frameworks such as TensorFlow and PyTorch. After a broad overview of frameworks, you will[…]

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Abstract Microsoft recently achieved a historical human parity milestone to recognize conversational speech on the switchboard task.  Microsoft Cognitive Toolkit (CNTK) is the secret weapon that enabled this historical breakthrough.  This talk will explain the[…]

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Abstract In this talk, I will discuss my group’s recent work on using logically-implied textual entailment knowledge to improve a variety of downstream natural language generation tasks. First, we employ a multi-task learning setup to[…]

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Abstract Deep learning has helped speech systems surpass humans on speech recognition tasks for multiple languages.  One could say, therefore, that the automatic speech recognition (ASR) task may be considered “solved” for any domain where[…]

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