5 citations · 8 across the 17 of their papers we have counts for
17 papers
Recursive Flow Matching
Jiahe Huang, Sihan Xu, Sharvaree Vadgama +1
Generative models have emerged as a powerful paradigm for solving physics systems and modeling complex spatiotemporal dynamics. However, achieving high physical accuracy without in…
ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery
Andrew Y. Zhou, Sharvaree Vadgama, Sumanth Varambally +3
Advances in large language models (LLMs) have recently opened new and promising avenues for small-molecule drug discovery. Yet existing LLM-based approaches for molecular generatio…
Think like a Scientist: Physics-guided LLM Agent for Equation Discovery
Jianke Yang, Ohm Venkatachalam, Mohammad Kianezhad +2
Explaining observed phenomena through symbolic, interpretable formulas is a fundamental goal of science. Recently, large language models (LLMs) have emerged as promising tools for…
Longitudinal Flow Matching for Trajectory Modeling
Mohammad Mohaiminul Islam, Thijs P. Kuipers, Sharvaree Vadgama +4
Generative models for sequential data often struggle with sparsely sampled and high-dimensional trajectories, typically reducing the learning of dynamics to pairwise transitions. W…
Platonic Transformers: A Solid Choice For Equivariance
Mohammad Mohaiminul Islam, Rishabh Anand, David R. Wessels +7
While widespread, Transformers lack inductive biases for geometric symmetries common in science and computer vision. Existing equivariant methods often sacrifice the efficiency and…
Controlled Generation with Equivariant Variational Flow Matching
Floor Eijkelboom, Heiko Zimmermann, Sharvaree Vadgama +4
We derive a controlled generation objective within the framework of Variational Flow Matching (VFM), which casts flow matching as a variational inference problem. We demonstrate th…
Clifford Group Equivariant Diffusion Models for 3D Molecular Generation
Cong Liu, Sharvaree Vadgama, David Ruhe +2
This paper explores leveraging the Clifford algebra's expressive power for $\E(n)$-equivariant diffusion models. We utilize the geometric products between Clifford multivectors and…
On the Importance of Embedding Norms in Self-Supervised Learning
Andrew Draganov, Sharvaree Vadgama, Sebastian Damrich +4
Self-supervised learning (SSL) allows training data representations without a supervised signal and has become an important paradigm in machine learning. Most SSL methods employ th…
Learning Symmetries via Weight-Sharing with Doubly Stochastic Tensors
Putri A. van der Linden, Alejandro García-Castellanos, Sharvaree Vadgama +2
Group equivariance has emerged as a valuable inductive bias in deep learning, enhancing generalization, data efficiency, and robustness. Classically, group equivariant methods requ…
Probing Equivariance and Symmetry Breaking in Convolutional Networks
Sharvaree Vadgama, Mohammad Mohaiminul Islam, Domas Buracas +3
In this work, we explore the trade-offs of explicit structural priors, particularly group equivariance. We address this through theoretical analysis and a comprehensive empirical s…
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…
The Hidden Pitfalls of the Cosine Similarity Loss
Andrew Draganov, Sharvaree Vadgama, Erik J. Bekkers
We show that the gradient of the cosine similarity between two points goes to zero in two under-explored settings: (1) if a point has large magnitude or (2) if the points are on op…
Grounding Continuous Representations in Geometry: Equivariant Neural Fields
David R Wessels, David M Knigge, Samuele Papa +4
Conditional Neural Fields (CNFs) are increasingly being leveraged as continuous signal representations, by associating each data-sample with a latent variable that conditions a sha…
Fast, Expressive SE Equivariant Networks through Weight-Sharing in Position-Orientation Space
Erik J Bekkers, Sharvaree Vadgama, Rob D Hesselink +2
Based on the theory of homogeneous spaces we derive geometrically optimal edge attributes to be used within the flexible message-passing framework. We formalize the notion of weigh…
Modeling Barrett's Esophagus Progression using Geometric Variational Autoencoders
Vivien van Veldhuizen, Sharvaree Vadgama, Onno J. de Boer +2
Early detection of Barrett's Esophagus (BE), the only known precursor to Esophageal adenocarcinoma (EAC), is crucial for effectively preventing and treating esophageal cancer. In t…
Interpretable ECG classification via a query-based latent space traversal (qLST)
Melle B. Vessies, Sharvaree P. Vadgama, Rutger R. van de Leur +4
Electrocardiography (ECG) is an effective and non-invasive diagnostic tool that measures the electrical activity of the heart. Interpretation of ECG signals to detect various abnor…
A Geometric Approach to Rotor Failure Tolerant Trajectory Tracking Control Design for a Quadrotor
Ashutosh Simha, Sharvaree Vadgama, Soumyendu Raha
This paper addresses the problem of designing a trajectory tracking control law for a quadrotor UAV, subsequent to complete failure of a single rotor. The control design problem co…