219 citations · 734 across the 29 of their papers we have counts for
8 papers · 1 filter
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…