176,364 research outputs found
Meta-Learning Dynamics Forecasting Using Task Inference
Current deep learning models for dynamics forecasting struggle with
generalization. They can only forecast in a specific domain and fail when
applied to systems with different parameters, external forces, or boundary
conditions. We propose a model-based meta-learning method called DyAd which can
generalize across heterogeneous domains by partitioning them into different
tasks. DyAd has two parts: an encoder which infers the time-invariant hidden
features of the task with weak supervision, and a forecaster which learns the
shared dynamics of the entire domain. The encoder adapts and controls the
forecaster during inference using adaptive instance normalization and adaptive
padding. Theoretically, we prove that the generalization error of such
procedure is related to the task relatedness in the source domain, as well as
the domain differences between source and target. Experimentally, we
demonstrate that our model outperforms state-of-the-art approaches on both
turbulent flow and real-world ocean data forecasting tasks
Meta Reinforcement Learning with Latent Variable Gaussian Processes
Learning from small data sets is critical in many practical applications
where data collection is time consuming or expensive, e.g., robotics, animal
experiments or drug design. Meta learning is one way to increase the data
efficiency of learning algorithms by generalizing learned concepts from a set
of training tasks to unseen, but related, tasks. Often, this relationship
between tasks is hard coded or relies in some other way on human expertise. In
this paper, we frame meta learning as a hierarchical latent variable model and
infer the relationship between tasks automatically from data. We apply our
framework in a model-based reinforcement learning setting and show that our
meta-learning model effectively generalizes to novel tasks by identifying how
new tasks relate to prior ones from minimal data. This results in up to a 60%
reduction in the average interaction time needed to solve tasks compared to
strong baselines.Comment: 11 pages, 7 figure
Team Learning: A Theoretical Integration and Review
With the increasing emphasis on work teams as the primary architecture of organizational structure, scholars have begun to focus attention on team learning, the processes that support it, and the important outcomes that depend on it. Although the literature addressing learning in teams is broad, it is also messy and fraught with conceptual confusion. This chapter presents a theoretical integration and review. The goal is to organize theory and research on team learning, identify actionable frameworks and findings, and emphasize promising targets for future research. We emphasize three theoretical foci in our examination of team learning, treating it as multilevel (individual and team, not individual or team), dynamic (iterative and progressive; a process not an outcome), and emergent (outcomes of team learning can manifest in different ways over time). The integrative theoretical heuristic distinguishes team learning process theories, supporting emergent states, team knowledge representations, and respective influences on team performance and effectiveness. Promising directions for theory development and research are discussed
Alternative approaches for studying shared and distributed leadership
Scholars hold different perspectives about leadership which are not limited to a
formally appointed leader. Of the abundance of terms used to describe this
phenomenon, shared and distributed are the most prevalent. These terms are often
used interchangeably, resulting in confusion in the way that shared and
distributed leadership is conceptualized and investigated. This paper provides a
historical development of this field, challenges existing conceptions and
reveals inconsistencies and contradictions that are seldom acknowledged. Four
distinct approaches to the study of shared and distributed leadership are
identified in the literature, each embracing different ontological views and
leadership epistemologies. Individually, the four approaches offer valuable -
yet partial - understanding. Comparing and contrasting the assumptions and
insights from the four approaches raises fundamental issues about how we think
about leadership in terms of research, practice and development
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