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PRODID:-//Columba Systems Ltd//NONSGML CPNG/SpringViewer/ICal Output/3.3-
 M3//EN
VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20260309T150042Z
DTSTART:20260311T140000Z
DTEND:20260311T150000Z
SUMMARY:Departmental Seminar – “Inherent Weight Normalization in Stochast
 ic Neural Networks” by Dr Georgios Is. Detorakis
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}m1te-mmjb54
 9l-79y5j0
DESCRIPTION:Neural Sampling Machines (NSM) is a class of neural networks 
 with binary threshold neurons that rely almost exclusively on multiplica
 tive noise as a resource for inference and learning. The probability of 
 activation of the NSM exhibits a self-normalizing property that mirrors 
 Weight Normalization\, a previously studied mechanism that fulfills many
  of the features of batch normalization in an online fashion. The always
 -on stochasticity of the NSM can leverage the stochasticity inherent to 
 a physical substrate\, such as analog non-volatile memories for in-memor
 y computing\, and is well-suited for Monte Carlo sampling\, while requir
 ing almost exclusively addition and comparison operations.
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Kilburn_TH 1.3\, Kilburn Building\, Manchester
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