3,171 research outputs found
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
A macro-realism inequality for opto-electro-mechanical systems
We show how to apply the Leggett-Garg inequality to opto-electro-mechanical
systems near their quantum ground state. We find that by using a dichotomic
quantum non-demolition measurement (via, e.g., an additional circuit-QED
measurement device) either on the cavity or on the nanomechanical system
itself, the Leggett-Garg inequality is violated. We argue that only
measurements on the mechanical system itself give a truly unambigous violation
of the Leggett-Garg inequality for the mechanical system. In this case, a
violation of the Leggett-Garg inequality indicates physics beyond that of
"macroscopic realism" is occurring in the mechanical system. Finally, we
discuss the difficulties in using unbound non-dichotomic observables with the
Leggett-Garg inequality.Comment: 9 pages, 2 figures. Added additional figure (2b), and associated
conten
Likelihood-based Out-of-Distribution Detection with Denoising Diffusion Probabilistic Models
Out-of-Distribution detection between dataset pairs has been extensively
explored with generative models. We show that likelihood-based
Out-of-Distribution detection can be extended to diffusion models by leveraging
the fact that they, like other likelihood-based generative models, are
dramatically affected by the input sample complexity. Currently, all
Out-of-Distribution detection methods with Diffusion Models are
reconstruction-based. We propose a new likelihood ratio for Out-of-Distribution
detection with Deep Denoising Diffusion Models, which we call the Complexity
Corrected Likelihood Ratio. Our likelihood ratio is constructed using Evidence
Lower-Bound evaluations from an individual model at various noising levels. We
present results that are comparable to state-of-the-art Out-of-Distribution
detection methods with generative models.Comment: 9 pages (main paper), 3 pages (acknowledgements & references), 3
figures, 2 tables, 1 algorithm, work accepted for BMVC 202
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