activity
20192026
most citedPID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics

37 citations · 46 across the 17 of their papers we have counts for

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9 papers · 1 filter

cs.LG2026

LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data

Abhilash Neog, Sepideh Fatemi, Medha Sawhney +9

Understanding and forecasting lake dynamics is critical for monitoring water quality and ecosystem health across lakes and reservoirs. While machine learning methods have been rece…

cs.LG2025

Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling

Abhilash Neog, Arka Daw, Sepideh Fatemi Khorasgani +10

Modeling Irregularly-sampled and Multivariate Time Series (IMTS) is crucial across a variety of applications where different sets of variates may be missing at different time-steps…

cs.LG2024

Hiding-in-Plain-Sight (HiPS) Attack on CLIP for Targetted Object Removal from Images

Arka Daw, Megan Hong-Thanh Chung, Maria Mahbub +1

Machine learning models are known to be vulnerable to adversarial attacks, but traditional attacks have mostly focused on single-modalities. With the rise of large multi-modal mode…

cs.LG2024

A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations

Naveen Gupta, Medha Sawhney, Arka Daw +2

In subsurface imaging, learning the mapping from velocity maps to seismic waveforms (forward problem) and waveforms to velocity (inverse problem) is important for several applicati…

cs.LG2024

Learning the boundary-to-domain mapping using Lifting Product Fourier Neural Operators for partial differential equations

Aditya Kashi, Arka Daw, Muralikrishnan Gopalakrishnan Meena +1

Neural operators such as the Fourier Neural Operator (FNO) have been shown to provide resolution-independent deep learning models that can learn mappings between function spaces. F…

cs.LG20222 cited

Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring

Arka Daw, Kyongmin Yeo, Anuj Karpatne +1

Inferring the source information of greenhouse gases, such as methane, from spatially sparse sensor observations is an essential element in mitigating climate change. While it is w…