no code implementations • 29 Jan 2014 • Francisco J. R. Ruiz, Isabel Valera, Carlos Blanco, Fernando Perez-Cruz
To this end, we use the large amount of information collected in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) database and propose to model these data using a nonparametric latent model based on the Indian Buffet Process (IBP).
no code implementations • 18 Jul 2014 • Pablo G. Moreno, Yee Whye Teh, Fernando Perez-Cruz, Antonio Artés-Rodríguez
Crowdsourcing has been proven to be an effective and efficient tool to annotate large datasets.
no code implementations • 17 Dec 2014 • Jesse Read, Fernando Perez-Cruz
In multi-label classification, the main focus has been to develop ways of learning the underlying dependencies between labels, and to take advantage of this at classification time.
1 code implementation • NeurIPS 2015 • Isabel Valera, Francisco Ruiz, Lennart Svensson, Fernando Perez-Cruz
We propose the infinite factorial dynamic model (iFDM), a general Bayesian nonparametric model for source separation.
1 code implementation • 24 Feb 2017 • Briland Hitaj, Giuseppe Ateniese, Fernando Perez-Cruz
Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper.
no code implementations • 19 May 2017 • Michael Minyi Zhang, Sinead A. Williamson, Fernando Perez-Cruz
First, we introduce an accelerated feature proposal mechanism that we show is a valid MCMC algorithm for posterior inference.
3 code implementations • 1 Sep 2017 • Briland Hitaj, Paolo Gasti, Giuseppe Ateniese, Fernando Perez-Cruz
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.
no code implementations • ICLR 2018 • Pablo M. Olmos, Briland Hitaj, Paolo Gasti, Giuseppe Ateniese, Fernando Perez-Cruz
In this paper, we noticed that even though GANs might not be able to generate samples from the underlying distribution (or we cannot tell at least), they are capturing some structure of the data in that high dimensional space.
no code implementations • 29 Jun 2018 • Melanie F. Pradier, Viktor Stojkoski, Zoran Utkovski, Ljupco Kocarev, Fernando Perez-Cruz
This paper presents a Bayesian nonparametric latent feature model specially suitable for exploratory analysis of high-dimensional count data.
no code implementations • 18 Oct 2018 • Francisco J. R. Ruiz, Isabel Valera, Lennart Svensson, Fernando Perez-Cruz
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.
no code implementations • 15 Oct 2019 • Fernando Perez-Cruz, Pablo M. Olmos, Michael Minyi Zhang, Howard Huang
In this paper, we take a new approach for time of arrival geo-localization.
1 code implementation • 4 Nov 2019 • 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.
no code implementations • 1 Feb 2021 • Sichen Li, Mélissa Zacharias, Jochem Snuverink, Jaime Coello de Portugal, Fernando Perez-Cruz, Davide Reggiani, Andreas Adelmann
The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time.
no code implementations • 24 Sep 2021 • Romana Rust, Achilleas Xydis, Kurt Heutschi, Nathanaël Perraudin, Gonzalo Casas, Chaoyu Du, Jürgen Strauss, Kurt Eggenschwiler, Fernando Perez-Cruz, Fabio Gramazio, Matthias Kohler
In this paper, we present the automated data acquisition setup, the data processing and the computational generation of diffusive surface structures.
1 code implementation • 27 Sep 2021 • Michael Stalder, Firat Ozdemir, Artur Safin, Jonas Sukys, Damien Bouffard, Fernando Perez-Cruz
Nowadays physical models are developed to estimate lake dynamics; however, computations needed for accurate estimation of lake surface temperature can get prohibitively expensive.
no code implementations • 21 Jan 2022 • Dorjan Hitaj, Giulio Pagnotta, Briland Hitaj, Fernando Perez-Cruz, Luigi V. Mancini
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 train deep neural networks without having to reveal the actual private training data.
1 code implementation • 28 Jan 2022 • Carlo Albert, Simone Ulzega, Firat Ozdemir, Fernando Perez-Cruz, Antonietta Mira
For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of observations with simulated model outputs in terms of a small set of summary statistics.
no code implementations • 12 Feb 2022 • Giulio Pagnotta, Dorjan Hitaj, Briland Hitaj, Fernando Perez-Cruz, Luigi V. Mancini
Being trained on proprietary information, these models provide a competitive edge for the owner company.
1 code implementation • 23 May 2022 • Steven Stalder, Nathanaël Perraudin, Radhakrishna Achanta, Fernando Perez-Cruz, Michele Volpi
These attributions are provided in the form of masks that only show the classifier-relevant parts of an image, masking out the rest.
1 code implementation • 17 Jun 2022 • Firat Ozdemir, Berkan Lafci, Xosé Luís Deán-Ben, Daniel Razansky, Fernando Perez-Cruz
However, no standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
1 code implementation • 10 Jul 2022 • 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.
1 code implementation • 27 Sep 2022 • Shirin Goshtasbpour, Fernando Perez-Cruz
Annealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models.
no code implementations • 18 Oct 2022 • Yanan Xin, Natasa Tagasovska, Fernando Perez-Cruz, Martin Raubal
Particularly, the transportation sector would benefit from the progress in AI and advance the development of intelligent transportation systems.
no code implementations • 29 Nov 2022 • Vera M. Balmer, Sophia V. Kuhn, Rafael Bischof, Luis Salamanca, Walter Kaufmann, Fernando Perez-Cruz, Michael A. Kraus
For conceptual design, engineers rely on conventional iterative (often manual) techniques.
no code implementations • 20 Feb 2023 • Stefania Russo, Nathanaël Perraudin, Steven Stalder, Fernando Perez-Cruz, Joao Paulo Leitao, Guillaume Obozinski, Jan Dirk Wegner
In this technical report we compare different deep learning models for prediction of water depth rasters at high spatial resolution.
no code implementations • 15 Mar 2023 • Sichen Li, Jochem Snuverink, Fernando Perez-Cruz, Andreas Adelmann
Unforeseen particle accelerator interruptions, also known as interlocks, lead to abrupt operational changes despite being necessary safety measures.
1 code implementation • 2 Jun 2023 • Javier Rando, Fernando Perez-Cruz, Briland Hitaj
Large language models (LLMs) successfully model natural language from vast amounts of text without the need for explicit supervision.
1 code implementation • 27 Jun 2023 • Shirin Goshtasbpour, Victor Cohen, Fernando Perez-Cruz
This algorithm relies on a sequence of interpolating distributions bridging the target to an initial tractable distribution such as the well-known geometric mean path of unnormalized distributions which is assumed to be suboptimal in general.
1 code implementation • 2 Aug 2023 • Simon Dirmeier, Carlo Albert, Fernando Perez-Cruz
We present Surjective Sequential Neural Likelihood (SSNL) estimation, a novel method for simulation-based inference in models where the evaluation of the likelihood function is not tractable and only a simulator that can generate synthetic data is available.
1 code implementation • 1 Nov 2023 • Simon Dirmeier, Fernando Perez-Cruz
We propose Diffusion Model Variational Inference (DMVI), a novel method for automated approximate inference in probabilistic programming languages (PPLs).
1 code implementation • 1 Nov 2023 • Simon Dirmeier, Ye Hong, Yanan Xin, Fernando Perez-Cruz
Reliable quantification of epistemic and aleatoric uncertainty is of crucial importance in applications where models are trained in one environment but applied to multiple different environments, often seen in real-world applications for example, in climate science or mobility analysis.
no code implementations • 20 Nov 2023 • Ye Hong, Yanan Xin, Simon Dirmeier, Fernando Perez-Cruz, Martin Raubal
Deep neural networks are increasingly utilized in mobility prediction tasks, yet their intricate internal workings pose challenges for interpretability, especially in comprehending how various aspects of mobility behavior affect predictions.
1 code implementation • 19 Feb 2024 • Simon Dirmeier, Ye Hong, Fernando Perez-Cruz
Diffusion probabilistic models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data, for instance, for computer vision, audio, natural language processing, or biomolecule generation.
no code implementations • 6 Mar 2024 • Dorjan Hitaj, Giulio Pagnotta, Fabio De Gaspari, Sediola Ruko, Briland Hitaj, Luigi V. Mancini, Fernando Perez-Cruz
We introduce MaleficNet 2. 0, a novel technique to embed self-extracting, self-executing malware in neural networks.