311 citations · 393 across the 20 of their papers we have counts for
20 papers
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…
Open World Scene Graph Generation using Vision Language Models
Amartya Dutta, Kazi Sajeed Mehrab, Medha Sawhney +8
Scene-Graph Generation (SGG) seeks to recognize objects in an image and distill their salient pairwise relationships. Most methods depend on dataset-specific supervision to learn t…
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…
AI-generated Image Detection: Passive or Watermark?
Moyang Guo, Yuepeng Hu, Zhengyuan Jiang +4
While text-to-image models offer numerous benefits, they also pose significant societal risks. Detecting AI-generated images is crucial for mitigating these risks. Detection method…
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…
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…
What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits
Harish Babu Manogaran, M. Maruf, Arka Daw +12
A grand challenge in biology is to discover evolutionary traits - features of organisms common to a group of species with a shared ancestor in the tree of life (also referred to as…
VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images
M. Maruf, Arka Daw, Kazi Sajeed Mehrab +19
Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of orga…
Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution
Mridul Khurana, Arka Daw, M. Maruf +12
A central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species acr…
Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images
Kazi Sajeed Mehrab, M. Maruf, Arka Daw +16
We introduce Fish-Visual Trait Analysis (Fish-Vista), the first organismal image dataset designed for the analysis of visual traits of aquatic species directly from images using pr…
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…
MEMTRACK: A Deep Learning-Based Approach to Microrobot Tracking in Dense and Low-Contrast Environments
Medha Sawhney, Bhas Karmarkar, Eric J. Leaman +3
Tracking microrobots is challenging, considering their minute size and high speed. As the field progresses towards developing microrobots for biomedical applications and conducting…
Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation
M. Maruf, Arka Daw, Amartya Dutta +2
In recent years, several Weakly Supervised Semantic Segmentation (WS3) methods have been proposed that use class activation maps (CAMs) generated by a classifier to produce pseudo-…
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…
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling
Arka Daw, Jie Bu, Sifan Wang +2
Despite the success of physics-informed neural networks (PINNs) in approximating partial differential equations (PDEs), PINNs can sometimes fail to converge to the correct solution…
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)
Jie Bu, Arka Daw, M. Maruf +1
A central goal in deep learning is to learn compact representations of features at every layer of a neural network, which is useful for both unsupervised representation learning an…
PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics
Arka Daw, M. Maruf, Anuj Karpatne
As applications of deep learning (DL) continue to seep into critical scientific use-cases, the importance of performing uncertainty quantification (UQ) with DL has become more pres…
Beyond Observed Connections : Link Injection
Jie Bu, M. Maruf, Arka Daw
In this paper, we proposed the \textit{link injection}, a novel method that helps any differentiable graph machine learning models to go beyond observed connections from the input…
Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling
Arka Daw, R. Quinn Thomas, Cayelan C. Carey +3
To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific know…
Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
Arka Daw, Anuj Karpatne, William Watkins +2
This paper introduces a framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery. This framework, termed physics-gu…