26,723 research outputs found
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks
Diversity of environments is a key challenge that causes learned robotic
controllers to fail due to the discrepancies between the training and
evaluation conditions. Training from demonstrations in various conditions can
mitigate---but not completely prevent---such failures. Learned controllers such
as neural networks typically do not have a notion of uncertainty that allows to
diagnose an offset between training and testing conditions, and potentially
intervene. In this work, we propose to use Bayesian Neural Networks, which have
such a notion of uncertainty. We show that uncertainty can be leveraged to
consistently detect situations in high-dimensional simulated and real robotic
domains in which the performance of the learned controller would be sub-par.
Also, we show that such an uncertainty based solution allows making an informed
decision about when to invoke a fallback strategy. One fallback strategy is to
request more data. We empirically show that providing data only when requested
results in increased data-efficiency.Comment: Copyright 20XX IEEE. Personal use of this material is permitted.
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A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
Hierarchical Subquery Evaluation for Active Learning on a Graph
To train good supervised and semi-supervised object classifiers, it is
critical that we not waste the time of the human experts who are providing the
training labels. Existing active learning strategies can have uneven
performance, being efficient on some datasets but wasteful on others, or
inconsistent just between runs on the same dataset. We propose perplexity based
graph construction and a new hierarchical subquery evaluation algorithm to
combat this variability, and to release the potential of Expected Error
Reduction.
Under some specific circumstances, Expected Error Reduction has been one of
the strongest-performing informativeness criteria for active learning. Until
now, it has also been prohibitively costly to compute for sizeable datasets. We
demonstrate our highly practical algorithm, comparing it to other active
learning measures on classification datasets that vary in sparsity,
dimensionality, and size. Our algorithm is consistent over multiple runs and
achieves high accuracy, while querying the human expert for labels at a
frequency that matches their desired time budget.Comment: CVPR 201
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