Description

In recent years, fuelled by the advances in supervised machine learning, we have seen astonishing leaps in the application of deep neural networks. Despite the remarkable results, these models are data-hungry and their performance relies heavily on the quality and size of the training data. In real-world scenarios, this can increase the time to value add significantly for businesses as collecting huge amounts of labeled data is usually very time and cost-consuming. This phenomenon—known as the cold start problem—is a pain point for almost any AI company that wants to scale. 

In this talk, we demonstrate how this problem can be addressed by aggregating data across sources and leveraging previously trained models with using domain adaptation and ensemble learning techniques.


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Instructor's Bio

Azin Asgarian

 Applied Research Scientist | Georgian

Azin is currently an applied research scientist on Georgian’s R&D team where she works with Georgian’s portfolio companies to help adopt applied research techniques to overcome business challenges. Azin holds a Master of Science in Computer Science from the University of Toronto and a Bachelor of Computer Science from the University of Tehran. Prior to joining Georgian, Azin was a research assistant at the University of Toronto and University Health Network (UHN) where she was working on the intersection of Machine Learning, Transfer Learning, and Computer Vision.


Franziska Kirschner, PhD

Research and Product Lead | Tractable

Franziska Kirschner is the Research and Product Lead of Car Inspection at Tractable. Her team uses machine learning to automate car damage appraisal across a range of applications. Her research interests include domain adaptation, and multitask- and multi-instance learning. In a previous life, she did a PhD in Physics at the University of Oxford. In her spare time, she enjoys cooking and making bad puns.

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    Overcoming the Cold Start Problem: How to Make New Tasks Tractable

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