2 citations · 5 across the 10 of their papers we have counts for
10 papers
Flexible Flows for Biological Sequence Design
Yogesh Verma, Dani Korpela, Harri Lähdesmäki +1
Designing functional biological sequences requires navigating vast discrete spaces under strict evolutionary and biophysical constraints. Discrete Flow Matching (DFM) offers a gene…
Let Physics Guide Your Protein Flows: Topology-aware Unfolding and Generation
Yogesh Verma, Markus Heinonen, Vikas Garg
Protein structure prediction and folding are fundamental to understanding biology, with recent deep learning advances reshaping the field. Diffusion-based generative models have re…
Positional Encoding meets Persistent Homology on Graphs
Yogesh Verma, Amauri H. Souza, Vikas Garg
The local inductive bias of message-passing graph neural networks (GNNs) hampers their ability to exploit key structural information (e.g., connectivity and cycles). Positional enc…
Robust Simulation-Based Inference under Missing Data via Neural Processes
Yogesh Verma, Ayush Bharti, Vikas Garg
Simulation-based inference (SBI) methods typically require fully observed data to infer parameters of models with intractable likelihood functions. However, datasets often contain…
Diffusion Twigs with Loop Guidance for Conditional Graph Generation
Giangiacomo Mercatali, Yogesh Verma, Andre Freitas +1
We introduce a novel score-based diffusion framework named Twigs that incorporates multiple co-evolving flows for enriching conditional generation tasks. Specifically, a central or…
Topological Neural Networks go Persistent, Equivariant, and Continuous
Yogesh Verma, Amauri H Souza, Vikas Garg
Topological Neural Networks (TNNs) incorporate higher-order relational information beyond pairwise interactions, enabling richer representations than Graph Neural Networks (GNNs).…
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs
Yogesh Verma, Markus Heinonen, Vikas Garg
Climate and weather prediction traditionally relies on complex numerical simulations of atmospheric physics. Deep learning approaches, such as transformers, have recently challenge…
E(3)-equivariant models cannot learn chirality: Field-based molecular generation
Alexandru Dumitrescu, Dani Korpela, Markus Heinonen +4
Obtaining the desired effect of drugs is highly dependent on their molecular geometries. Thus, the current prevailing paradigm focuses on 3D point-cloud atom representations, utili…
AbODE: Ab Initio Antibody Design using Conjoined ODEs
Yogesh Verma, Markus Heinonen, Vikas Garg
Antibodies are Y-shaped proteins that neutralize pathogens and constitute the core of our adaptive immune system. De novo generation of new antibodies that target specific antigens…
Modular Flows: Differential Molecular Generation
Yogesh Verma, Samuel Kaski, Markus Heinonen +1
Generating new molecules is fundamental to advancing critical applications such as drug discovery and material synthesis. Flows can generate molecules effectively by inverting the…