Alexia Jolicoeur-Martineau, Ph.D.

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Principal Researcher @ Microsoft 🐱‍💻

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Any-Property-Conditional Molecule 🧪 Generation with Self-Criticism 👩‍🏫 using Spanning Trees (STGG+)
Jul 15, 2024 · original
Update 2025-03-12: We have since improved STGG+ and added active learning ( STGG+AL ). It beats RL method at generating molecules with complex properties. The molecules we get are much nicer than the ones from the original paper. Molecule synthesizability can be improved simply by adding constraints such as max-ring-size ≤ 6 and removing too large molecules (since STGG+ already takes care of ensuring proper valency rules). See below for an example of a molecule made by STGG+AL. ——————————————————————————————————– Paper / Code Twitter (sorry 𝕏 ) is obsessed with Large Language Models (LLMs) lately, so we hear very little about other cool applications of generative AI. Molecule generation is an exciting area of generative AI since it can serve to generate new drugs or materials (such as Organic-LED; the material used in the screen of your smartphone and even newer TVs ). In this work, we
Fashion repeats itself: Generating tabular data via Diffusion and XGBoost 🌲
Sep 19, 2023 · original
Paper / Code Since AlexNet showed the world the power of deep learning, the field of AI has rapidly switched to almost exclusively focus on deep learning. Some of the main justifications are that 1) neural networks are Universal Function Approximation (UFA, not UFO ), 2) deep learning generally works the best, and 3) it is highly scalable through SGD and GPUs. However, when you look a bit further down from the surface, you see that 1) simple methods such as Decision Trees are also UFAs , 2) fancy tree-based methods such as Gradient-Boosted Trees (GBTs) actually work better than deep learning on tabular data, and 3) tabular data tend to be small, but GBTs can optionally be trained with GPUs and iterated over small data chunks for scalability to large datasets. At least for the tabular data case, deep learning is not all you need . In this joint collaboration with Kilian Fatras and Tal Kac
Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation
May 22, 2022 · original
In this joint work with Vikram Voleti and Christopher Pal , we show that a single diffusion model can solve many video tasks: 1) interpolation, 2) forward/reverse prediction, and 3) unconditional generation through a well-designed masking scheme . See our website, which contains many videos: https://mask-cond-video-diffusion.github.io . The paper can be found here . The code is available here: https://github.com/voletiv/mcvd-pytorch . A lot of the existing video models have poor quality (especially on long videos), require enormous amounts of GPUs/TPUs, and can only solve one specific task at a time (only prediction, only generation, or only interpolation). We aimed to improve on all these problems. We do so through a Masked Conditional Video Diffusion (MCVD) approach. Using score-based diffusion , we get very high quality and diverse results that retain their quality better over time (a
Alternative losses for Relativistic GANs
Oct 1, 2018 · original
Further investigation needs to be done, but I suspect some variants of Relativistic average GANs (RaGANs) might be more sensible than the ones I proposed in my paper . If you are using Relativistic GANs, you might be interested in trying out also variant 3 which is the most promising. For simplicity, let’s assume we use the non-saturating loss and that we have symmetry, i.e., f1(-y)=f2(y) . (This is true in HingeGAN, LSGAN with -1/1 labels, Standard GAN with sigmoid activation). 1) This is the RaGAN formula proposed in the paper. 2) This variant works as well as the original RaGAN. I know this because I used it by mistake before and it made no difference in the results. The generator loss doesn’t make much sense, but as discussed in GANs beyond divergence minimization , the generator can minimize pretty much anything related to the divergence estimated (the loss function of the discrimin

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