[Colloq] Title: Adaptive Deep Learning for Vision and Language | Speaker: Kate Saenko, UMass Lowell | Date: 3/29/16 Time: 1:30pm Location: 366 WVH

[Robert Platt]

Hi there --

We have the following hiring talk tomorrow:

Title: Adaptive Deep Learning for Vision and Language
Speaker: Kate Saenko, UMass Lowell
Date: 3/29/16   Time: 1:30--2:30  Location: 366 WVH

Title: Adaptive Deep Learning for Vision and Language

Abstract:

Advances in Machine Learning, and, in particular, Deep Learning, have 
recently propelled the state of the art in both Computer Vision (CV) and 
in Natural Language Processing (NLP), spurring strong academic and 
industry investment in these emerging areas of AI. A key aspect of this 
success is representation learning, i.e. the ability to learn useful 
feature representations from large amounts of labeled data.

In this talk, I will address two serious limitations of representation 
learning for CV and NLP. First, these two fields have evolved 
separately, focusing on either image or language representations alone. 
In contrast, our work on joint learning for vision and language creates 
representations that directly connect visual concepts to natural 
language semantics. Our research was among of the first to propose deep 
neural nets for automatic captioning of images and videos, and spatial 
memory nets for answering questions about visual scenes.

Second, a key problem in applying supervised ML, including deep methods, 
to real world environments is the dataset bias issue: Deviations from 
the training distribution at test time can lead to catastrophic failure. 
I will give an overview of our efforts to endow learning models with the 
ability to transfer knowledge between domains and adapt to real world 
environments, concluding with recently proposed methods for effective 
and simple adaptation of deep neural networks without requiring millions 
of training examples.

Bio:

Kate Saenko is an Assistant Professor of Computer Science at the 
University of Massachusetts Lowell, where she leads the Computer Vision 
and Learning Group. She received her PhD from MIT, followed by 
postdoctoral work at UC Berkeley and Harvard. Her research spans the 
areas of computer vision, machine learning, and human-robot interfaces. 
Dr Saenko’s current research interests include domain adaptation of 
machine learning models and joint modeling of language and vision.  She 
is the recipient of several research grant awards from the National 
Science Foundation, DARPA, and other government and industry agencies.