Search Results for author: Luis Oala

Found 12 papers, 6 papers with code

Interval Neural Networks: Uncertainty Scores

1 code implementation25 Mar 2020 Luis Oala, Cosmas Heiß, Jan Macdonald, Maximilian März, Wojciech Samek, Gitta Kutyniok

We propose a fast, non-Bayesian method for producing uncertainty scores in the output of pre-trained deep neural networks (DNNs) using a data-driven interval propagating network.

Image Reconstruction Uncertainty Quantification

Interval Neural Networks as Instability Detectors for Image Reconstructions

1 code implementation27 Mar 2020 Jan Macdonald, Maximilian März, Luis Oala, Wojciech Samek

This work investigates the detection of instabilities that may occur when utilizing deep learning models for image reconstruction tasks.

Image Reconstruction Uncertainty Quantification

Post-Hoc Domain Adaptation via Guided Data Homogenization

1 code implementation8 Apr 2021 Kurt Willis, Luis Oala

Addressing shifts in data distributions is an important prerequisite for the deployment of deep learning models to real-world settings.

Domain Adaptation Transfer Learning

Data Models for Dataset Drift Controls in Machine Learning With Optical Images

1 code implementation4 Nov 2022 Luis Oala, Marco Aversa, Gabriel Nobis, Kurt Willis, Yoan Neuenschwander, Michèle Buck, Christian Matek, Jerome Extermann, Enrico Pomarico, Wojciech Samek, Roderick Murray-Smith, Christoph Clausen, Bruno Sanguinetti

This limits our ability to study and understand the relationship between data generation and downstream machine learning model performance in a physically accurate manner.

Model Selection

Machine Learning for Health symposium 2022 -- Extended Abstract track

no code implementations28 Nov 2022 Antonio Parziale, Monica Agrawal, Shalmali Joshi, Irene Y. Chen, Shengpu Tang, Luis Oala, Adarsh Subbaswamy

A collection of the extended abstracts that were presented at the 2nd Machine Learning for Health symposium (ML4H 2022), which was held both virtually and in person on November 28, 2022, in New Orleans, Louisiana, USA.

Localized Data Work as a Precondition for Data-Centric ML: A Case Study of Full Lifecycle Crop Disease Identification in Ghana

no code implementations4 Jul 2023 Darlington Akogo, Issah Samori, Cyril Akafia, Harriet Fiagbor, Andrews Kangah, Donald Kwame Asiedu, Kwabena Fuachie, Luis Oala

The Ghana Cashew Disease Identification with Artificial Intelligence (CADI AI) project demonstrates the importance of sound data work as a precondition for the delivery of useful, localized datacentric solutions for public good tasks such as agricultural productivity and food security.

Generative Fractional Diffusion Models

no code implementations26 Oct 2023 Gabriel Nobis, Marco Aversa, Maximilian Springenberg, Michael Detzel, Stefano Ermon, Shinichi Nakajima, Roderick Murray-Smith, Sebastian Lapuschkin, Christoph Knochenhauer, Luis Oala, Wojciech Samek

We generalize the continuous time framework for score-based generative models from an underlying Brownian motion (BM) to an approximation of fractional Brownian motion (FBM).

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