Online Variational Filtering and Parameter Learning arxiv.org/abs/2110.13549 with Andrew Campbell Yuyang Shi & Tom Rainforth accepted as Oral #NeurIPS2021. We exploit forward Bellman recursions for the ELBO & its gradients in state-space models and RL ideas.

I have written a blog post describing our use of Reinforcement Learning to create an online objective for sequential Variational Inference 🌐 andrew-cr.github.io/posts/RLandVI/ 📰 arxiv.org/abs/2110.13549

How can we apply diffusion models to data with varying dimensionality? We use jump diffusions to simultaneously generate the size and state values for varying size data e.g. molecules arxiv.org/abs/2305.16261 w/ Will Harvey Christian Weilbach [email protected] Valentin De Bortoli Tom Rainforth Arnaud Doucet

Combining discrete and continuous data is an important capability for generative models. To address this for protein design, we introduce Multiflow, a generative model for structure and sequence generation. Preprint: arxiv.org/abs/2402.04997 Code: github.com/jasonkyuyim/mu… 1/8

Discrete generative models use denoisers for generation, but they can slip up. What if generation *isn’t only* about denoising?🤔 Introducing DDPD: Discrete Diffusion with Planned Denoising🤗🧵(1/11) w/ Juno Nam Andrew Campbell Hannes Stärk Yilun Xu Tommi Jaakkola RGB Lab @ MIT

Super excited to preprint our work on developing a Biomolecular Emulator (BioEmu): Scalable emulation of protein equilibrium ensembles with generative deep learning from Microsoft Research AI for Science. #ML #AI #NeuralNetworks #Biology #AI4Science biorxiv.org/content/10.110…