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Robust Sampling in Deep Learning
Deep learning requires regularization mechanisms to reduce overfitting a...
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Improved BiGAN training with marginal likelihood equalization
We propose a novel training procedure for improving the performance of g...
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Probabilistic Time of Arrival Localization
In this paper, we take a new approach for time of arrival geo-localizati...
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Probabilistic MIMO Symbol Detection with Expectation Consistency Approximate Inference
In this paper we explore low-complexity probabilistic algorithms for sof...
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Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies
The recent adoption of Electronic Health Records (EHRs) by health care p...
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Out-of-Sample Testing for GANs
We propose a new method to evaluate GANs, namely EvalGAN. EvalGAN relies...
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Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation
New communication standards need to deal with machine-to-machine communi...
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Sparse Three-parameter Restricted Indian Buffet Process for Understanding International Trade
This paper presents a Bayesian nonparametric latent feature model specia...
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PassGAN: A Deep Learning Approach for Password Guessing
State-of-the-art password guessing tools, such as HashCat and John the R...
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Accelerated Inference for Latent Variable Models
Inference of latent feature models in the Bayesian nonparametric setting...
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Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Deep Learning has recently become hugely popular in machine learning, pr...
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Complex-Valued Kernel Methods for Regression
Usually, complex-valued RKHS are presented as an straightforward applica...
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Deep Learning for Multi-label Classification
In multi-label classification, the main focus has been to develop ways o...
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Bayesian Nonparametric Crowdsourcing
Crowdsourcing has been proven to be an effective and efficient tool to a...
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Bayesian nonparametric comorbidity analysis of psychiatric disorders
The analysis of comorbidity is an open and complex research field in the...
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Gaussian Processes for Nonlinear Signal Processing
Gaussian processes (GPs) are versatile tools that have been successfully...
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