Vincent Fortuin

Academic Template

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👋 Welcome to the Academic Template

The Wowchemy Academic Resumé Template for Hugo empowers you to create your job-winning online resumé and showcase your academic publications.

Check out the latest demo of what you’ll get in less than 10 minutes, or view the showcase.

Wowchemy makes it easy to create a beautiful website for free. Edit your site in Markdown, Jupyter, or RStudio (via Blogdown), generate it with Hugo, and deploy with GitHub or Netlify. Customize anything on your site with widgets, themes, and language packs.

👉 Get Started
📚 View the documentation
💬 Chat with the Wowchemy community or Hugo community
🐦 Twitter: @wowchemy @GeorgeCushen #MadeWithWowchemy
💡 Request a feature or report a bug for Wowchemy
⬆️ Updating Wowchemy? View the Update Guide and Release Notes

Crowd-funded open-source software

To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.

❤️ Click here to unlock rewards with sponsorship

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Backgrounds can be applied to any section. Here, the background option is set give a color gradient.

To remove this section, delete content/home/demo.md.

Get inspired

Check out the Markdown files which power the Academic Demo, or view the showcase.

Skills

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Experience

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Accomplishments

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Projects

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Recent & Upcoming Talks

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Selected Publications

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All Publications

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Quickly discover relevant content by filtering publications.

Example Talk

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Click on the Slides button above to view the built-in slides feature.

Slides can be added in a few ways:

Create slides using Wowchemy’s Slides feature and link using slides parameter in the front matter of the talk file
Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file
Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes.

Further event details, including page elements such as image galleries, can be added to the body of this page.

Hodge-Aware Contrastive Learning

Mon, 01 Jan 2024 00:00:00 +0000

On the Challenges and Opportunities in Generative AI

Mon, 01 Jan 2024 00:00:00 +0000

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI

Mon, 01 Jan 2024 00:00:00 +0000

Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood

Mon, 01 Jan 2024 00:00:00 +0000

A primer on Bayesian neural networks: review and debates

Sun, 01 Jan 2023 00:00:00 +0000

Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072)

Sun, 01 Jan 2023 00:00:00 +0000

Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo

Sun, 01 Jan 2023 00:00:00 +0000

Improving Neural Additive Models with Bayesian Principles

Sun, 01 Jan 2023 00:00:00 +0000

Incorporating Unlabelled Data into Bayesian Neural Networks

Sun, 01 Jan 2023 00:00:00 +0000

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization

Sun, 01 Jan 2023 00:00:00 +0000

Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice

Sun, 01 Jan 2023 00:00:00 +0000

Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks

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Understanding pathologies of deep heteroskedastic regression

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Bayesian neural network priors revisited

Sat, 01 Jan 2022 00:00:00 +0000

Data augmentation in Bayesian neural networks and the cold posterior effect

Sat, 01 Jan 2022 00:00:00 +0000

Deep classifiers with label noise modeling and distance awareness

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Invariance learning in deep neural networks with differentiable Laplace approximations

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Meta-learning richer priors for VAEs

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Neural Variational Gradient Descent

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On Disentanglement in Gaussian Process Variational Autoencoders

Sat, 01 Jan 2022 00:00:00 +0000

On Interpretable Reranking-Based Dependency Parsing Systems

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Pathologies in Priors and Inference for Bayesian Transformers

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Priors in Bayesian deep learning: A review

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Probing as quantifying inductive bias

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Quantum Bayesian Neural Networks

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Sparse MoEs meet Efficient Ensembles

Sat, 01 Jan 2022 00:00:00 +0000

A Bayesian Approach to Invariant Deep Neural Networks