BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Date iCal//NONSGML kigkonsult.se iCalcreator 2.20.2//
METHOD:PUBLISH
X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:STANDARD
DTSTART:20171029T030000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20170326T020000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
RDATE:20180325T020000
TZNAME:CEST
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:calendar.12428.field_data.0@diag.uniroma1.it
DTSTAMP:20220118T105040Z
CREATED:20170801T175937Z
DESCRIPTION:Speaker: Tommaso ColomboTitle: Recurrent Neural Networks: why d
o LSTM networks perform so well in time series prediction?(Joint work with
: Alberto De Santis\, Stefano Lucidi)Abstract:Long Short-Term Memory (LSTM
) networks are a broadly-used and very well-known variant of Recurrent Neu
ral Networks\, heavily employed\, e.g.\, in time series forecasting and na
tural language processing. Deep learning has been revolutionary in many fi
elds for the last two decades (e.g.\, image recognition\, natural language
processing\, ...) thanks to the continuous increase in computing speed an
d capacity. To train deep learning machines\, one needs to solve complex\,
highly nonlinear and large-scale optimization problems\, but this notwith
standing few authors studied the theoretical properties of such problems.
Based on their structure\, Recurrent Neural Networks are naturally the mos
t suitable to solve a time series forecasting problem\, but their training
leads to one of the most difficult optimization problems in Deep Learning
. This difficulty is strictly tied to the vanishing gradients problem that
arises when trying to latch information for a long time period.There exis
t two main approaches to overcome these training issues: a structural one
(e.g.\, LSTM and other memory-based networks) and an algorithmic one (e.g.
\, gradient truncation\, …). The most effective\, recent approaches usuall
y employ a combination of the two\, i.e. a structure tailored to the probl
em and an optimization algorithm tailored to the structure at hand.-------
-------------------------------Speaker: Ludovica MaccarroneTitle: A new gr
ey-box approach to solve the workforce scheduling problem in complex manuf
acturing and logistic contexts(Joint work with Stefano Lucidi)Abstract:We
present a new approach to solve the workforce scheduling problem in comple
x applicative contexts such as manufacturing and logistic processes.We con
sider systems where one or more workloads require to be sequentially proce
ssed in different areas by different types of operators exclusively charac
terized by their skills. We assume the request of such skills is not fixed
and may be varied in order to match the time/cost objectives of the organ
ization. Furthermore\, due to the complexity of the considered processes\,
we suppose it is not possible to derive an analytic expression linking th
e number of resources of different types working on an activity to the tim
e to complete it. For this reason\, a set of ad hoc simulators can be empl
oyed and their outputs are parameters of our formulation.Typical issues ar
ising in workforce management applications are related to the need of mini
mizing the labor cost while meeting deadlines and industrial plans. These
resource/time trade-offs are even more complex under our assumptions due t
o the presence of simulators which natively split the problem into two seq
uential sub-problems. Our strategy addresses these difficulties through a
decomposition approach which allows to model the problem as a grey-box opt
imization problem combining a new scheduling formulation with the simulati
on of some complex manufacturing and logistic processes.
DTSTART;TZID=Europe/Paris:20170831T110000
DTEND;TZID=Europe/Paris:20170831T120000
LAST-MODIFIED:20200515T073055Z
LOCATION:Aula A2 - DIAG
SUMMARY:MORE@DIAG - Speakers: Tommaso Colombo\; Ludovica Maccarrone - Tomm
aso Colombo\, Ludovica Maccarrone
URL;TYPE=URI:https://diag.uniroma1.it/node/12428
END:VEVENT
END:VCALENDAR