BestTechVideos: Videos Tagged with '006'

NIPS 2011 Domain Adaptation Workshop: History Dependent Domain Adaptation

BestVideos — Sun, 01 Apr 2012 04:27:25 GMT

Domain Adaptation Workshop: Theory and Application at NIPS 2011
Invited Talk: History Dependent Domain Adaptation by Allen Lavoie

Abstract:
We study a novel variant of the domain adaptation problem, in which the loss function on test data changes due to dependencies on prior predictions. One important instance of this problem area occurs in settings where it is more costly to make a new error than to repeat a previous error. We propose several methods for learning effectively in this setting, and test them empirically on the real-world tasks of malicious URL classification and adversarial advertisement detection.

NIPS 2011 Music and Machine Learning Workshop: This is the Remix: Structural Improvisation...

BestVideos — Wed, 07 Mar 2012 12:01:01 GMT

International Music and Machine Learning Workshop: Learning from Musical Structure at NIPS 2011

Invited Talk: This is the Remix: Structural Improvisation using Automated Pattern Discovery by Sean Whalen

Sean Whalen is a postdoctoral researcher in the IDS lab at Columbia University, focusing on anomaly detection in the cloud for the MEERKATS project. His research interests include computer security, machine learning, network theory, statistical inference, complex systems, and immersive visualization.

NIPS 2011 Learning Semantics Workshop: Learning Semantics of Movement

BestVideos — Fri, 02 Mar 2012 13:14:46 GMT

Learning Semantics Workshop at NIPS 2011
Invited Talk: Learning Semantics of Movement by Timo Honkela

Abstract:
In this presentation, we consider how to computationally model the interrelated processes of understanding natural language and perceiving and producing movement in multimodal real world contexts. Movement is the specific focus of this presentation for several reasons. For instance, it is a fundamental part of human activities that ground our understanding of the world. We are developing methods and technologies to automatically associate human movements detected by motion capture and in video sequences with their linguistic descriptions. When the association between human movement and their linguistic descriptions has been learned using pattern recognition and statistical machine learning methods, the system is also used to produce animations based on written instructions and for labeling motion capture and video sequences. We consider three different aspects: using video and motion tracking data, applying multi-task learning methods, and framing the problem within cognitive linguistics research.