34,396 research outputs found
Project knowledge into project practice: generational issues in the knowledge management process
This paper considers Learning and Knowledge Transfer within the project domain. Knowledge can be a tenuous and elusive concept, and is challenging to transfer within organizations and projects. This challenge is compounded when we consider generational differences in the project and the workplace. This paper looks at learning, and the transfer of that generated knowledge. A number of tools and frameworks have been considered, together with accumulated extant literature. These issues have been deliberated through the lens of different generational types, focusing on the issues and differences in knowledge engagement and absorption between Baby Boomers, Generation X, and Generation Y/Millennials. Generation Z/Centennials have also been included where appropriate. This is a significant issue in modern project and organizational structures. Some recommendations are offered to assist in effective knowledge transfer across generational types.Accepted manuscrip
Expert Gate: Lifelong Learning with a Network of Experts
In this paper we introduce a model of lifelong learning, based on a Network
of Experts. New tasks / experts are learned and added to the model
sequentially, building on what was learned before. To ensure scalability of
this process,data from previous tasks cannot be stored and hence is not
available when learning a new task. A critical issue in such context, not
addressed in the literature so far, relates to the decision which expert to
deploy at test time. We introduce a set of gating autoencoders that learn a
representation for the task at hand, and, at test time, automatically forward
the test sample to the relevant expert. This also brings memory efficiency as
only one expert network has to be loaded into memory at any given time.
Further, the autoencoders inherently capture the relatedness of one task to
another, based on which the most relevant prior model to be used for training a
new expert, with finetuning or learning without-forgetting, can be selected. We
evaluate our method on image classification and video prediction problems.Comment: CVPR 2017 pape
Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People
This paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authorsâ experience, a framework proposal for creating valuable and aggregated knowledge is depicted.This research was partially funded by FundaciĂłn Tecnalia Research & Innovation, and J.O.-M. also wants
to recognise the support obtained from the EU RFCS program through project number 793505 â4.0 Lean system
integrating workers and processes (WISEST)â and from the grant PRX18/00036 given by the Spanish SecretarĂa
de Estado de Universidades, InvestigaciĂłn, Desarrollo e InnovaciĂłn del Ministerio de Ciencia, InnovaciĂłn
y Universidades
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
In the NIPS 2017 Learning to Run challenge, participants were tasked with
building a controller for a musculoskeletal model to make it run as fast as
possible through an obstacle course. Top participants were invited to describe
their algorithms. In this work, we present eight solutions that used deep
reinforcement learning approaches, based on algorithms such as Deep
Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region
Policy Optimization. Many solutions use similar relaxations and heuristics,
such as reward shaping, frame skipping, discretization of the action space,
symmetry, and policy blending. However, each of the eight teams implemented
different modifications of the known algorithms.Comment: 27 pages, 17 figure
Transnational Philanthropy, Policy Transfer Networks and the Open Society Institute
This paper avoids assumptions that civil society is an entirely separate and distinguishable domain from states and emergent forms of transnational authority. Focusing on the 'soft' ideational and normative policy transfer undermines notions of clear cut boundaries between an independent philanthropic body in civil society and highlights the intermeshing and mutual engagement that comes with networks, coalitions, joint funding, partnerships and common policy dialogues
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