1 citations · 2 across the 6 of their papers we have counts for
8 papers
Beyond Uniform Local Isometry and Topology: FactoMap for Disentangled Representations
Sohini Gupta, Bahareh Tolooshams, Michael Gao
Many disentanglement methods represent generative factors using Euclidean product coordinates, although the underlying factor spaces may wrap, collapse, or have position-dependent…
Mechanistic Interpretability with Sparse Autoencoder Neural Operators
Bahareh Tolooshams, Ailsa Shen, Anima Anandkumar
We introduce sparse autoencoder neural operators (SAE-NOs), a new class of sparse autoencoders that operate in function spaces rather than fixed-dimensional Euclidean representatio…
NOBLE -- Neural Operator with Biologically-informed Latent Embeddings to Capture Experimental Variability in Biological Neuron Models
Luca Ghafourpour, Valentin Duruisseaux, Bahareh Tolooshams +3
Characterizing the cellular properties of neurons is fundamental to understanding their function in the brain. In this quest, the generation of bio-realistic models is central towa…
EquiReg: Equivariance Regularized Diffusion for Inverse Problems
Bahareh Tolooshams, Aditi Chandrashekar, Rayhan Zirvi +4
Diffusion models represent the state-of-the-art for solving inverse problems such as image restoration tasks. Diffusion-based inverse solvers incorporate a likelihood term to guide…
Ultrasound Lung Aeration Map via Physics-Aware Neural Operators
Jiayun Wang, Oleksii Ostras, Masashi Sode +5
Lung ultrasound is a growing modality in clinics for diagnosing and monitoring acute and chronic lung diseases due to its low cost and accessibility. Lung ultrasound works by emitt…
A Unified Model for Compressed Sensing MRI Across Undersampling Patterns
Armeet Singh Jatyani, Jiayun Wang, Aditi Chandrashekar +4
Compressed Sensing MRI reconstructs images of the body's internal anatomy from undersampled measurements, thereby reducing scan time. Recently, deep learning has shown great potent…
Diffusion State-Guided Projected Gradient for Inverse Problems
Rayhan Zirvi, Bahareh Tolooshams, Anima Anandkumar
Recent advancements in diffusion models have been effective in learning data priors for solving inverse problems. They leverage diffusion sampling steps for inducing a data prior w…
Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems
Freya Shah, Taylor L. Patti, Julius Berner +3
Fourier Neural Operators (FNOs) excel on tasks using functional data, such as those originating from partial differential equations. Such characteristics render them an effective a…