📘 Publications

🧠 > Conference papers & pre-prints

• Photonic Differential Privacy with Direct Feedback Alignment. Ruben Ohana*, Hamlet J. Medina Ruiz*, Julien Launay*, Alessandro Cappelli, Iacopo Poli, Liva Ralaivola, Alain Rakotomamonjy. NeurIPS (poster), 2021.
• ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients. Alessandro Cappeli, Julien Launay, Laurent Meunier, Ruben Ohana, Iacopo Poli. arXiv pre-print. 2021.
• PAGnol: an Extra-Large French Generative Model. Julien Launay*, E.L. Tommasone*, Baptiste Pannier, François Boniface, Amélie Chatelain, Iacopo Poli, Djamé Seddah. arXiv pre-print. 2021.
• Adversarial Robustness by Design through Analog Computing and Synthetic Gradients. Alessandro Cappeli*, Ruben Ohana*, Julien Launay, Laurent Meunier, Iacopo Poli, Florent Krzakala. arXiv pre-print, 2021.
• Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures. Julien Launay, Iacopo Poli, François Boniface, Florent Krzakala. NeurIPS (poster), 2020. GitHub, Twitter.
• Principled Training of Neural Networks with Direct Feedback Alignment. Julien Launay, Iacopo Poli, Florent Krzakala. arXiv pre-print, 2019. GitHub.
• Analysis of Factors Affecting the Performance of BIPV Panels. Julien Launay, Eric W.M. Lee, Rachid Bennacer, Richard K.K. Yuen. ICOME (oral), and European Physical Journal Applied Physics, 2018.

📝 > Workshops and short papers

• Is the Number of Trainable Parameters All That Actually Matters? Amélie Chatelain, Amine Djeghri, Daniel Hesslow, Julien Launay†, Iacopo Poli† (co-advised). NeurIPS I Can't Believe It's Not Better (spotlight), 2021.
• LightOn Optical Processing Unit: Scaling-up AI and HPC with a Non von Neumann co-processor. Charles Brossollet, Alessandro Cappelli, Igor Carron, Charidimos Chaintoutis, Amélie Chatelain, Laurent Daudet, Sylvain Gigan, Daniel Hesslow, Florent Krzakala, Julien Launay, Safa Mokaadi, Fabien Moreau, Kilian Müller, Ruben Ohana, Gustave Pariente, Iacopo Poli, and Giuseppe L. Tommasone. Hot Chips (poster), 2021.
• ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients. Alessandro Cappeli, Julien Launay, Laurent Meunier, Ruben Ohana, Iacopo Poli. ICML Workshop on Adversarial Machine Learning (poster), 2021.
• Hardware Beyond Backpropagation: a Photonic Co-Processor for Direct Feedback Alignment. Julien Launay, Iacopo Poli, Kilian Müller, Gustave Pariente, Igor Carron, Laurent Daudet, Florent Krzakala, Sylvain Gigan. NeurIPS Beyond Backpropagation (oral), 2020.
• Ignorance is Bliss: Adversarial Robustness by Design through Analog Computing and Synaptic Asymmetry. Alessandro Cappelli, Ruben Ohana, Julien Launay, Iacopo Poli, Florent Krzakala. NeurIPS Beyond Backpropagation (poster), 2020.
• Light-in-the-loop: Using a Photonics Co-Processor for Scalable Training of Neural Networks. Julien Launay, Iacopo Poli, Killian Müller, Igor Carron, Laurent Daudet, Florent Krzakala, Sylvain Gigan. Hot Chips (poster), 2020.
• Computer Vision and Feedback Alignment Methods. Julien Launay, Iacopo Poli, Florent Krzakala. NAISys (poster), 2020.

⚖️ > Patents

• Method and System for Distributed Training Using Synthetic Gradients. Julien Launay, Iacopo Poli, Kilian Müller, Gustave Pariente, Igor Carron, Laurent Daudet. US Patent 17/117,925, 2020 (pending).
• Method and System for Machine Learning Using Optical Data. Iacopo Poli, Julien Launay, Kilian Müller, Gustave Pariente, Igor Carron, Laurent Daudet, Ruben Ohana, Daniel Hesslow. US Patent Publication US2021/0287079 A1.

🔗 > Blogposts

• This site, 0 blogposts to date.
• LightOn's Summer Series #1, #2, and #3, on LightOn's Medium blog.

🎓 > Educational resources

• Les glissements de terrain, modélisation et prévision. (Landslides: modeling and prediction). Éduscol, 2017.

📖 > Books

• Aventure, survie et création: le Guide Minecraft. (Adventure, Survival, and Creation: a Minecraft Guide). Sold 20,000 copies. Pearson, 2015.