[talks] Monday, 11AM: Geoff Hinton on neural networks

[Nicole E. Wagenblast]

hi cs faculty and graduate students, 


geoff hinton from U of T is speaking tomorrow at 11am in the small auditorium. geoff is one of the founders of modern machine learning and artificial intelligence. (and, he's an incredibly witty and engaging speaker.) see the full announcement below. 


best 
dave 




---------- Forwarded message ---------- 
From: David Mimno < mimno at cs.princeton.edu > 
Date: Thu, May 12, 2011 at 2:57 PM 
Subject: [Ml-stat-talks] Monday, 11AM: Geoff Hinton on neural networks 



To: ml-stat-talks < ml-stat-talks at lists.cs.princeton.edu > 



For those not familiar with his work: "Geoffrey Hinton talks about neural 
networks" is roughly equivalent to "J.S. Bach talks about fugues". 
This is not to be missed. Drag your friends. 

Geoff's schedule is filling up, but if you would like to meet with him 
before the talk or in the afternoon, please let me know. 

Monday, May 16, 11AM (note the change), CS105 

============================= 

How to force unsupervised neural networks to discover 
the right representation of images 

Geoffrey Hinton 
University of Toronto 

One appealing way to design an object recognition system is to define 
objects recursively in terms of their parts and the required spatial 
relationships between the parts and the whole. These relationships can 
be represented by the coordinate transformation between an intrinsic 
frame of reference embedded in the part and an intrinsic frame 
embedded in the whole. This transformation is unaffected by the 
viewpoint so this form of knowledge about the shape of an object is 
viewpoint invariant. A natural way for a neural network to implement 
this knowledge is by using a matrix of weights to represent each 
part-whole relationship and a vector of neural activities to represent 
the pose of each part or whole relative to the viewer. The pose of the 
whole can then be predicted from the poses of the parts and, if the 
predictions agree, the whole is present. This leads to neural networks 
that can recognize objects over a wide range of viewpoints using 
neural activities that are ``equivariant'' rather than invariant: as 
the viewpoint varies the neural activities all vary even though the 
knowledge is viewpoint-invariant. The ``capsules'' that implement the 
lowest-level parts in the shape hierarchy need to extract explicit 
pose parameters from pixel intensities and these pose parameters need 
to have the right form to allow coordinate transformations to be 
implemented by matrix multiplies. These capsules are quite easy to 
learn from pairs of transformed images if the neural net has direct, 
non-visual access to the transformations, as it would if it controlled 
them. (Joint work with Sida Wang and Alex Krizhevsky)