9 papers
A Function-Space Dichotomy for Compositional Learning: Exponential Sub-Optimality of the Neural Tangent Kernel
Arkaprabha Ganguli, Emil Constantinescu
A persistent empirical observation is that trained neural networks outperform their neural tangent kernel (NTK) limit on tasks with compositional structure, yet a quantitative acco…
High-Resolution Flood Mapping With Sentinel-1 and Sentinel-2 via Misalignment-Robust Cross-Sensor Learning and Generative Despeckling
David Ma, Jeremy Feinstein, Shreya Pandit +2
Reliable high-resolution flood extent mapping from satellite imagery remains constrained by limited data fidelity and sensor-specific artifacts. Multispectral optical imagery is de…
Event-Level QCD Inference Framework for Quark-Gluon Imaging
Patrick Barry, Pi-Yueh Chuang, Ian Cloët +3
We introduce and demonstrate an event-level analysis framework for quark-gluon imaging. For a first application we use it for the inference of parton distribution functions from sy…
Disentangled Deep Priors for Bayesian Inverse Problems
Arkaprabha Ganguli, Emil Constantinescu
We propose a structured prior for high-dimensional Bayesian inverse problems based on a disentangled deep generative model whose latent space is partitioned into auxiliary variable…
Uncovering Physical Drivers of Dark Matter Halo Structures with Auxiliary-Variable-Guided Generative Models
Arkaprabha Ganguli, Anirban Samaddar, Florian Kéruzoré +4
Deep generative models (DGMs) compress high-dimensional data but often entangle distinct physical factors in their latent spaces. We present an auxiliary-variable-guided framework…
Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets
Arkaprabha Ganguli, Nesar Ramachandra, Julie Bessac +1
This study addresses the challenge of statistically extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings. We investigat…
Error Controlled Feature Selection for Ultrahigh Dimensional and Highly Correlated Feature Space Using Deep Learning
Arkaprabha Ganguli, David Todem, Tapabrata Maiti
In recent years, deep learning has been at the center of analytics due to its impressive empirical success in analyzing complex data objects. Despite this success, most of the exis…
Nonparametric Scanning For Nonrandom Missing Data With Continuous Instrumental Variables
Arkaprabha Ganguli, David Todem
This article introduces a new instrumental variable approach for estimating unknown population parameters with data having nonrandom missing values. With coarse and discrete instru…
ENNS: Variable Selection, Regression, Classification and Deep Neural Network for High-Dimensional Data
Kaixu Yang, Tapabrata Maiti
High-dimensional, low sample-size (HDLSS) data problems have been a topic of immense importance for the last couple of decades. There is a vast literature that proposed a wide vari…