16 citations · 16 across the 3 of their papers we have counts for
6 papers · 1 filter
Decafs: Disentangled Conditional adversarial Flows
Anirudh jain, Sakshi Varshney, Samuel Kaski +1
Flow-based models have established state-of-the-art performance in generative modeling across domains, but are hard to interpret due to their complex latent embeddings. In particul…
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…
Provably expressive temporal graph networks
Amauri H. Souza, Diego Mesquita, Samuel Kaski +1
Temporal graph networks (TGNs) have gained prominence as models for embedding dynamic interactions, but little is known about their theoretical underpinnings. We establish fundamen…
Why GANs are overkill for NLP
David Alvarez-Melis, Vikas Garg, Adam Tauman Kalai
This work offers a novel theoretical perspective on why, despite numerous attempts, adversarial approaches to generative modeling (e.g., GANs) have not been as popular for certain…
Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms
Vikas K. Garg, Tamar Pichkhadze
We resolve the fundamental problem of online decoding with general order ergodic Markov chain models. Specifically, we provide deterministic and randomized algorithms whos…