Publications

Exploring trust development in families of children towards surgical and emergency care providers: A scoping review of the literature.
Olivia Serhan
Alexander Moise
Elena Guadagno
A. Issa
Dan Poenaru
2023-01-01
Journal of Pediatric Surgery (publié)
doi.org
Exploring validation metrics for offline model-based optimisation
Christopher Beckham
Alexandre Piché
David Vazquez
Chris Pal
In offline model-based optimisation (MBO) we are interested in using machine learning to de-sign candidates that maximise some measure of d… (voir plus)esirability through an expensive but real-world scoring process. Offline MBO tries to approximate this expensive scoring function and use that to evaluate generated designs, however evaluation is non-exact because one approximation is being evaluated with another. Instead, we ask ourselves: if we did have the real world scoring function at hand, what cheap-to-compute validation metrics would correlate best with this? Since the real-world scoring function is available for simulated MBO datasets, insights obtained from this can be transferred over to real-world offline MBO tasks where the real-world scoring function is expensive to compute. To address this, we propose a conceptual evaluation framework that is amenable to measuring extrapolation, and apply this to conditional denoising diffusion models. Empirically, we find that two validation metrics – agreement and Frechet distance – correlate quite well with the ground truth. When there is high variability in conditional generation, feedback is required in the form of an approximated version of the real-world scoring function. Furthermore, we find that generating high-scoring samples may require heavily weighting the generative model in favour of sample quality, potentially at the cost of sample diversity.
2023-01-01
(publié)
www.semanticscholar.org
Family risk communication preferences in pediatric surgery: A scoping review.
Arthega Selvarajan
Brandon Arulanandam
Elena Guadagno
Dan Poenaru
2023-01-01
Journal of Pediatric Surgery (publié)
doi.org
Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples
Marco Jiralerspong
Joey Bose
Ian Gemp
Chongli Qin
Yoram Bachrach
Gauthier Gidel
openreview.net
Feature Likelihood Score: Evaluating Generalization of Generative Models Using Samples
Marco Jiralerspong
Avishek Joey Bose
Gauthier Gidel
Deep generative models have demonstrated the ability to generate complex, high-dimensional, and photo-realistic data. However, a unified fr… (voir plus)amework for evaluating different generative modeling families remains a challenge. Indeed, likelihood-based metrics do not apply in many cases while pure sample-based metrics such as FID fail to capture known failure modes such as overfitting on training data. In this work, we introduce the Feature Likelihood Score (FLS), a parametric sample-based score that uses density estimation to quantitatively measure the quality/diversity of generated samples while taking into account overfitting. We empirically demonstrate the ability of FLS to identify specific overfitting problem cases, even when previously proposed metrics fail. We further perform an extensive experimental evaluation on various image datasets and model classes. Our results indicate that FLS matches intuitions of previous metrics, such as FID, while providing a more holistic evaluation of generative models that highlights models whose generalization abilities are under or overappreciated. Code for computing FLS is provided at https://github.com/marcojira/fls.
2023-01-01
arXiv.org (prépublication)
doi.org
Filtering Pixel Latent Variables for Unmixing Volumetric Images
Catherine Bouchard
Vincent Boulanger
Flavie Lavoie-Cardinal
Christian Gagné
Measurements of different overlapping components require robust unmixing algorithms to convert the raw multi-dimensional measurements to use… (voir plus)ful unmixed images. Such algorithms perform reliable separation of the components when the raw signal is fully resolved and contains enough information to fit curves on the raw distributions. In experimental physics, measurements are often noisy, undersam-pled, or unresolved spatially or spectrally. We propose a novel method where bandpass filters are applied to the latent space of a multi-dimensional convolutional neural network to separate the overlapping signal components and extract each of their relative contributions. Simultaneously processing all dimensions with multi-dimensional convolution kernels empowers the network to combine the information from adjacent pixels and time-or spectral-bins, facilitating component separation in instances where individual pixels lack well-resolved information. We demonstrate the applicability of the method to real experimental physics problems using fluorescence lifetime microscopy and mode decomposition in optical fibers as test cases. The successful application of our approach to these two distinct experimental cases, characterized by different measured distributions, highlights the versatility of our approach in addressing a wide array of imaging tasks.
2023-01-01
arXiv.org (preprint)
doi.org
Filtering Pixel Latent Variables for Unmixing Volumetric Images
Catherine Bouchard
Vincent Boulanger
Flavie Lavoie-Cardinal
Christian Gagné
Measurements of different overlapping components require robust unmixing algorithms to convert the raw multi-dimensional measurements to use… (voir plus)ful unmixed images. Such algorithms perform reliable separation of the components when the raw signal is fully resolved and contains enough information to fit curves on the raw distributions. In experimental physics, measurements are often noisy, undersam-pled, or unresolved spatially or spectrally. We propose a novel method where bandpass filters are applied to the latent space of a multi-dimensional convolutional neural network to separate the overlapping signal components and extract each of their relative contributions. Simultaneously processing all dimensions with multi-dimensional convolution kernels empowers the network to combine the information from adjacent pixels and time-or spectral-bins, facilitating component separation in instances where individual pixels lack well-resolved information. We demonstrate the applicability of the method to real experimental physics problems using fluorescence lifetime microscopy and mode decomposition in optical fibers as test cases. The successful application of our approach to these two distinct experimental cases, characterized by different measured distributions, highlights the versatility of our approach in addressing a wide array of imaging tasks.
2023-01-01
arXiv.org (prépublication)
doi.org
Findings of the 1st Shared Task on Multi-lingual Multi-task Information Retrieval at MRL 2023
Francesco Tinner
David Ifeoluwa Adelani
Chris Emezue
Mammad Hajili
Omer Goldman
Muhammad Farid Adilazuarda
Muhammad Dehan Al Kautsar
Aziza Mirsaidova
Müge Kural
Dylan Massey
Chiamaka Ijeoma Chukwuneke
CHINEDU EMMANUEL MBONU
Damilola Oluwaseun Oloyede
Kayode Olaleye
Jonathan Atala
Benjamin A. Ajibade
Saksham Bassi
Rahul Aralikatte
Najoung Kim
Duygu Ataman
Large language models (LLMs) excel in language understanding and generation, especially in English which has ample public benchmarks for var… (voir plus)ious natural language processing (NLP) tasks. Nevertheless, their reliability across different languages and domains remains uncertain. Our new shared task introduces a novel benchmark to assess the ability of multilingual LLMs to comprehend and produce language under sparse settings, particularly in scenarios with under-resourced languages, with an emphasis on the ability to capture logical, factual, or causal relationships within lengthy text contexts. The shared task consists of two sub-tasks crucial to information retrieval: Named Entity Recognition (NER) and Reading Comprehension (RC), in 7 data-scarce languages: Azerbaijani, Igbo, Indonesian, Swiss German, Turkish, Uzbek and Yorùbá, which previously lacked annotated resources in information retrieval tasks. Our evaluation of leading LLMs reveals that, despite their competitive performance, they still have notable weaknesses such as producing output in the non-target language or providing counterfactual information that cannot be inferred from the context. As more advanced models emerge, the benchmark will remain essential for supporting fairness and applicability in information retrieval systems.
2023-01-01
MRL (publié)
doi.org
Formal and Empirical Studies of Counting Behaviour in ReLU RNNs.
Nadine El-Naggar
Andrew Ryzhikov
Laure Daviaud
Pranava Madhyastha
Tillman Weyde
François Coste
Faissal Ouardi
Guillaume Rabusseau
2023-01-01
International Conference on Graphics and Interaction (publié)
dblp.uni-trier.de
Formalizing locality for normative synaptic plasticity models
Colin Bredenberg
Ezekiel Williams
Cristina Savin
Blake Richards
Guillaume Lajoie
openreview.net
Formation of Giant Plasma Membrane Vesicles for Biological and Medical Applications: A Review
Yang Li
Songyang Liu
Wanyu Xu
Kemin Wang
Fengjiao He
Jianbo Liu
Plasma membrane vesicles (PMVs) are micron-sized biomembrane vesicles that are isolated directly from living cells. They retain the lipid an… (voir plus)d protein complexity of the plasma membrane of the parent cell...
2023-01-01
Sensors & Diagnostics (publié)
doi.org
Formation of Giant Plasma Membrane Vesicles for Biological and Medical Applications: A Review
Yangping Li
Songyang Liu
Wanyu Xu
Kemin Wang
Feng-jiang He
Jianbo Liu
Plasma membrane vesicles (PMVs) are micron-sized biomembrane vesicles that are isolated directly from living cells. They retain the lipid an… (voir plus)d protein complexity of the plasma membrane of the parent cell...
2023-01-01
Sensors & Diagnostics (published)
doi.org