18 citations · 36 across the 12 of their papers we have counts for
14 papers
Learning to Evict from Key-Value Cache
Luca Moschella, Laura Manduchi, Ozan Sener
The growing size of Large Language Models (LLMs) makes efficient inference challenging, primarily due to the memory demands of the autoregressive Key-Value (KV) cache. Existing evi…
Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers
Laura Manduchi, Antoine Wehenkel, Jens Behrmann +6
Whole-body hemodynamics simulators, which model blood flow and pressure waveforms as functions of physiological parameters, are now essential tools for studying cardiovascular syst…
TreeDiffusion: Hierarchical Generative Clustering for Conditional Diffusion
Jorge da Silva Gonçalves, Laura Manduchi, Moritz Vandenhirtz +1
Generative modeling and clustering are conventionally distinct tasks in machine learning. Variational Autoencoders (VAEs) have been widely explored for their ability to integrate b…
Structured Generations: Using Hierarchical Clusters to guide Diffusion Models
Jorge da Silva Goncalves, Laura Manduchi, Moritz Vandenhirtz +1
This paper introduces Diffuse-TreeVAE, a deep generative model that integrates hierarchical clustering into the framework of Denoising Diffusion Probabilistic Models (DDPMs). The p…
scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data
Moritz Vandenhirtz, Florian Barkmann, Laura Manduchi +2
We propose a novel method, scTree, for single-cell Tree Variational Autoencoders, extending a hierarchical clustering approach to single-cell RNA sequencing data. scTree corrects f…
On the Challenges and Opportunities in Generative AI
Laura Manduchi, Clara Meister, Kushagra Pandey +23
The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervi…