1,878 research outputs found
Cognitive apprenticeship : teaching the craft of reading, writing, and mathtematics
Includes bibliographical references (p. 25-27)This research was supported by the National Institute of Education under Contract no. US-NIE-C-400-81-0030 and the Office of Naval Research under Contract No. N00014-85-C-002
Knowledge Distillation and Continual Learning for Optimized Deep Neural Networks
Over the past few years, deep learning (DL) has been achieving state-of-theart performance on various human tasks such as speech generation, language translation, image segmentation, and object detection. While traditional machine learning models require hand-crafted features, deep learning algorithms can automatically extract discriminative features and learn complex knowledge from large datasets. This powerful learning ability makes deep learning models attractive to both academia and big corporations.
Despite their popularity, deep learning methods still have two main limitations: large memory consumption and catastrophic knowledge forgetting. First, DL algorithms use very deep neural networks (DNNs) with many billion parameters, which have a big model size and a slow inference speed. This restricts the application of DNNs in resource-constraint devices such as mobile phones and autonomous vehicles. Second, DNNs are known to suffer from catastrophic forgetting. When incrementally learning new tasks, the model performance on old tasks significantly drops. The ability to accommodate new knowledge while retaining previously learned knowledge is called continual learning. Since the realworld environments in which the model operates are always evolving, a robust neural network needs to have this continual learning ability for adapting to new changes
Aiming higher : the Plymouth and Peninsula Tri-Level Model (PPM) for school/HE links : putting the university into school and community
"This report outlines an innovative, effective model of school/higher education (HE) liaison, the Plymouth & Peninsula Model (PPM). The PPM is of major national and international importance.
The defining quality of PPM is that it is a genuine partnership, with parity of esteem between HEIs, schools and local authorities (LAs), supported by other major stakeholders.
The PPM is based upon firm research evidence, is highly cost effective and could be rolled out nationally to cover geographically all primary and secondary schools and college grouped in consortia" - page iii
Interactive Imitation Learning in Robotics: A Survey
Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL)
where human feedback is provided intermittently during robot execution allowing
an online improvement of the robot's behavior. In recent years, IIL has
increasingly started to carve out its own space as a promising data-driven
alternative for solving complex robotic tasks. The advantages of IIL are its
data-efficient, as the human feedback guides the robot directly towards an
improved behavior, and its robustness, as the distribution mismatch between the
teacher and learner trajectories is minimized by providing feedback directly
over the learner's trajectories. Nevertheless, despite the opportunities that
IIL presents, its terminology, structure, and applicability are not clear nor
unified in the literature, slowing down its development and, therefore, the
research of innovative formulations and discoveries. In this article, we
attempt to facilitate research in IIL and lower entry barriers for new
practitioners by providing a survey of the field that unifies and structures
it. In addition, we aim to raise awareness of its potential, what has been
accomplished and what are still open research questions. We organize the most
relevant works in IIL in terms of human-robot interaction (i.e., types of
feedback), interfaces (i.e., means of providing feedback), learning (i.e.,
models learned from feedback and function approximators), user experience
(i.e., human perception about the learning process), applications, and
benchmarks. Furthermore, we analyze similarities and differences between IIL
and RL, providing a discussion on how the concepts offline, online, off-policy
and on-policy learning should be transferred to IIL from the RL literature. We
particularly focus on robotic applications in the real world and discuss their
implications, limitations, and promising future areas of research
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How to design for persistence and retention in MOOCs?
Design of educational interventions is typically carried out following a design cycle involving phases of investigation, conceptualization, prototyping, implementation, execution and evaluation. This cycle can be applied at different levels of granularity e.g. learning activity, module, course or programme.
In this paper we consider an aspect of learner behavior that can be critical to the success of many MOOCs i.e. their persistence to study, and the related theme of learner retention. We reflect on the impact that consideration of these can have on design decisions at different stages in the design cycle with the aim of en-hancing MOOC design in relation to learner persistence and retention, with particular attention to the European context
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