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Learning Visiolinguistic Representations with ViLBERT w/ Stefan Lee - #358

Learning Visiolinguistic Representations with ViLBERT w/ Stefan Lee - #358

FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)


Learning Visiolinguistic Representations with ViLBERT w/ Stefan Lee - #358

FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

ratings:
Length:
28 minutes
Released:
Mar 18, 2020
Format:
Podcast episode

Description

Today we’re joined by Stefan Lee, assistant professor at the school of electrical engineering and computer science at Oregon State University. Stefan, who we sat down with at NeurIPS this past winter, is focused on the development of agents that can perceive their environment and communicate their understanding with humans in order to coordinate their actions to achieve mutual goals.  In our conversation, we focus on his paper ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks, a model for learning joint representations of image content and natural language. We talk through the development and training process for this model, the adaptation of the training process to incorporate additional visual information to BERT models, where this research leads from the perspective of integration between visual and language tasks and finally, we discuss the importance of visual grounding. Check out the complete show notes page at twimlai.com/talk/358.
Released:
Mar 18, 2020
Format:
Podcast episode

Titles in the series (100)

This Week in Machine Learning & AI is the most popular podcast of its kind. TWiML & AI caters to a highly-targeted audience of machine learning & AI enthusiasts. They are data scientists, developers, founders, CTOs, engineers, architects, IT & product leaders, as well as tech-savvy business leaders. These creators, builders, makers and influencers value TWiML as an authentic, trusted and insightful guide to all that’s interesting and important in the world of machine learning and AI. Technologies covered include: machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning and more.