The most recent list can be found on on google scholar.
Brofos, James A; Shu, Rui; Lederman, Roy R
A Bias-Variance Decomposition for Bayesian Deep Learning Inproceedings
In: pp. 14, 2019.
Abstract | BibTeX | Tags: Bayesian Deep Learning, Bayesian Inference, Deep Learning
@inproceedings{brofos_bias-variance_2019,
title = {A Bias-Variance Decomposition for Bayesian Deep Learning},
author = {James A Brofos and Rui Shu and Roy R Lederman},
year = {2019},
date = {2019-12-01},
pages = {14},
abstract = {We exhibit a decomposition of the Kullback-Leibler divergence into terms corresponding to bias, variance, and irreducible error. Our particular focus in this work is Bayesian deep learning and in this domain we illustrate the application of this decomposition to adversarial example identification, to image segmentation, and to malware detection. We empirically demonstrate qualitative similarities between the variance decomposition and mutual information.},
keywords = {Bayesian Deep Learning, Bayesian Inference, Deep Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
We exhibit a decomposition of the Kullback-Leibler divergence into terms corresponding to bias, variance, and irreducible error. Our particular focus in this work is Bayesian deep learning and in this domain we illustrate the application of this decomposition to adversarial example identification, to image segmentation, and to malware detection. We empirically demonstrate qualitative similarities between the variance decomposition and mutual information.
Shaham, Uri; Lederman, Roy R
Learning by coincidence: Siamese networks and common variable learning Journal Article
In: Pattern Recognition, vol. 74, pp. 52–63, 2018, ISSN: 00313203.
Links | BibTeX | Tags: Common variable, Deep Learning, Multi-view, multimodal, Siamese networks
@article{shaham_learning_2018,
title = {Learning by coincidence: Siamese networks and common variable learning},
author = {Uri Shaham and Roy R Lederman},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0031320317303588},
doi = {10.1016/j.patcog.2017.09.015},
issn = {00313203},
year = {2018},
date = {2018-01-01},
urldate = {2020-08-13},
journal = {Pattern Recognition},
volume = {74},
pages = {52--63},
keywords = {Common variable, Deep Learning, Multi-view, multimodal, Siamese networks},
pubstate = {published},
tppubtype = {article}
}
Shaham, Uri; Lederman, Roy R
Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks Technical Report
2015.
Abstract | Links | BibTeX | Tags: Common variable, Deep Learning, Multi-view
@techreport{shaham_common_2015,
title = {Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks},
author = {Uri Shaham and Roy R Lederman},
url = {https://arxiv.org/abs/1512.08806v3},
year = {2015},
date = {2015-12-01},
urldate = {2020-08-13},
abstract = {We consider the statistical problem of learning common source of variability
in data which are synchronously captured by multiple sensors, and demonstrate
that Siamese neural networks can be naturally applied to this problem. This
approach is useful in particular in exploratory, data-driven applications,
where neither a model nor label information is available. In recent years, many
researchers have successfully applied Siamese neural networks to obtain an
embedding of data which corresponds to a "semantic similarity". We present an
interpretation of this "semantic similarity" as learning of equivalence
classes. We discuss properties of the embedding obtained by Siamese networks
and provide empirical results that demonstrate the ability of Siamese networks
to learn common variability.},
keywords = {Common variable, Deep Learning, Multi-view},
pubstate = {published},
tppubtype = {techreport}
}
We consider the statistical problem of learning common source of variability
in data which are synchronously captured by multiple sensors, and demonstrate
that Siamese neural networks can be naturally applied to this problem. This
approach is useful in particular in exploratory, data-driven applications,
where neither a model nor label information is available. In recent years, many
researchers have successfully applied Siamese neural networks to obtain an
embedding of data which corresponds to a "semantic similarity". We present an
interpretation of this "semantic similarity" as learning of equivalence
classes. We discuss properties of the embedding obtained by Siamese networks
and provide empirical results that demonstrate the ability of Siamese networks
to learn common variability.
in data which are synchronously captured by multiple sensors, and demonstrate
that Siamese neural networks can be naturally applied to this problem. This
approach is useful in particular in exploratory, data-driven applications,
where neither a model nor label information is available. In recent years, many
researchers have successfully applied Siamese neural networks to obtain an
embedding of data which corresponds to a "semantic similarity". We present an
interpretation of this "semantic similarity" as learning of equivalence
classes. We discuss properties of the embedding obtained by Siamese networks
and provide empirical results that demonstrate the ability of Siamese networks
to learn common variability.