219 citations · 734 across the 29 of their papers we have counts for
33 papers
Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation
Simon Dirmeier, Carlo Albert, Fernando Perez-Cruz
Neural likelihood estimation methods for simulation-based inference can suffer from performance degradation when the modeled data is very high-dimensional or lies along a lower-dim…
Adaptive Annealed Importance Sampling with Constant Rate Progress
Shirin Goshtasbpour, Victor Cohen, Fernando Perez-Cruz
Annealed Importance Sampling (AIS) synthesizes weighted samples from an intractable distribution given its unnormalized density function. This algorithm relies on a sequence of int…
Optimization of Annealed Importance Sampling Hyperparameters
Shirin Goshtasbpour, Fernando Perez-Cruz
Annealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. Although AIS is guaranteed to provide unb…
Facilitated machine learning for image-based fruit quality assessment
Manuel Knott, Fernando Perez-Cruz, Thijs Defraeye
Image-based machine learning models can be used to make the sorting and grading of agricultural products more efficient. In many regions, implementing such systems can be difficult…
OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing
Firat Ozdemir, Berkan Lafci, Xosé Luís Deán-Ben +2
Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by subsequent detection of ultrasound waves generated via ligh…
What You See is What You Classify: Black Box Attributions
Steven Stalder, Nathanaël Perraudin, Radhakrishna Achanta +2
An important step towards explaining deep image classifiers lies in the identification of image regions that contribute to individual class scores in the model's output. However, d…
TATTOOED: A Robust Deep Neural Network Watermarking Scheme based on Spread-Spectrum Channel Coding
Giulio Pagnotta, Dorjan Hitaj, Briland Hitaj +2
Watermarking of deep neural networks (DNNs) has gained significant traction in recent years, with numerous (watermarking) strategies being proposed as mechanisms that can help veri…
Deep Reinforcement Learning for Random Access in Machine-Type Communication
Muhammad Awais Jadoon, Adriano Pastore, Monica Navarro +1
Random access (RA) schemes are a topic of high interest in machine-type communication (MTC). In RA protocols, backoff techniques such as exponential backoff (EB) are used to stabil…
Learning Summary Statistics for Bayesian Inference with Autoencoders
Carlo Albert, Simone Ulzega, Firat Ozdemir +2
For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of obser…
FedComm: Federated Learning as a Medium for Covert Communication
Dorjan Hitaj, Giulio Pagnotta, Briland Hitaj +2
Proposed as a solution to mitigate the privacy implications related to the adoption of deep learning, Federated Learning (FL) enables large numbers of participants to successfully…
Probabilistic modeling of lake surface water temperature using a Bayesian spatio-temporal graph convolutional neural network
Michael Stalder, Firat Ozdemir, Artur Safin +3
Accurate lake temperature estimation is essential for numerous problems tackled in both hydrological and ecological domains. Nowadays physical models are developed to estimate lake…
A data acquisition setup for data driven acoustic design
Romana Rust, Achilleas Xydis, Kurt Heutschi +8
In this paper, we present a novel interdisciplinary approach to study the relationship between diffusive surface structures and their acoustic performance. Using computational desi…
A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators
Sichen Li, Mélissa Zacharias, Jochem Snuverink +4
The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A nov…
Improved BiGAN training with marginal likelihood equalization
Pablo Sánchez-Martín, Pablo M. Olmos, Fernando Perez-Cruz
We propose a novel training procedure for improving the performance of generative adversarial networks (GANs), especially to bidirectional GANs. First, we enforce that the empirica…
Probabilistic Time of Arrival Localization
Fernando Perez-Cruz, Pablo M. Olmos, Michael Minyi Zhang +1
In this paper, we take a new approach for time of arrival geo-localization. We show that the main sources of error in metropolitan areas are due to environmental imperfections that…
Probabilistic MIMO Symbol Detection with Expectation Consistency Approximate Inference
Javier Cépedes, Pablo M. Olmos, Matilde Sánchez-Fernández +1
In this paper we explore low-complexity probabilistic algorithms for soft symbol detection in high-dimensional multiple-input multiple-output (MIMO) systems. We present a novel alg…
Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies
Stefan G. Stark, Stephanie L. Hyland, Melanie F. Pradier +5
The recent adoption of Electronic Health Records (EHRs) by health care providers has introduced an important source of data that provides detailed and highly specific insights into…
Out-of-Sample Testing for GANs
Pablo Sánchez-Martín, Pablo M. Olmos, Fernando Pérez-Cruz
We propose a new method to evaluate GANs, namely EvalGAN. EvalGAN relies on a test set to directly measure the reconstruction quality in the original sample space (no auxiliary net…
Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation
Francisco J. R. Ruiz, Isabel Valera, Lennart Svensson +1
New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the numb…
Sparse Three-parameter Restricted Indian Buffet Process for Understanding International Trade
Melanie F. Pradier, Viktor Stojkoski, Zoran Utkovski +2
This paper presents a Bayesian nonparametric latent feature model specially suitable for exploratory analysis of high-dimensional count data. We perform a non-negative doubly spars…
Economic Complexity Unfolded: Interpretable Model for the Productive Structure of Economies
Zoran Utkovski, Melanie F. Pradier, Viktor Stojkoski +2
Economic complexity reflects the amount of knowledge that is embedded in the productive structure of an economy. It resides on the premise of hidden capabilities - fundamental endo…
PassGAN: A Deep Learning Approach for Password Guessing
Briland Hitaj, Paolo Gasti, Giuseppe Ateniese +1
State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable users to check billions of passwords per second against password hashes. In addition to perfor…
Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models
Michael Minyi Zhang, Sinead A. Williamson, Fernando Perez-Cruz
Inference of latent feature models in the Bayesian nonparametric setting is generally difficult, especially in high dimensional settings, because it usually requires proposing feat…
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Briland Hitaj, Giuseppe Ateniese, Fernando Perez-Cruz
Deep Learning has recently become hugely popular in machine learning, providing significant improvements in classification accuracy in the presence of highly-structured and large d…