Search Results for author: Vipin Kumar

Found 53 papers, 8 papers with code

An Autoencoder Architecture for L-band Passive Microwave Retrieval of Landscape Freeze-Thaw Cycle

1 code implementation4 Jul 2024 Divya Kumawat, Ardeshir Ebtehaj, Xiaolan Xu, Andreas Colliander, Vipin Kumar

Estimating the landscape and soil freeze-thaw (FT) dynamics in the Northern Hemisphere is crucial for understanding permafrost response to global warming and changes in regional and global carbon budgets.

Anomaly Detection Retrieval +2

Knowledge-guided Machine Learning: Current Trends and Future Prospects

no code implementations24 Mar 2024 Anuj Karpatne, Xiaowei Jia, Vipin Kumar

We discuss different facets of KGML research in terms of the type of scientific knowledge used, the form of knowledge-ML integration explored, and the method for incorporating scientific knowledge in ML.

Combining Satellite and Weather Data for Crop Type Mapping: An Inverse Modelling Approach

no code implementations29 Jan 2024 Praveen Ravirathinam, Rahul Ghosh, Ankush Khandelwal, Xiaowei Jia, David Mulla, Vipin Kumar

We finally discuss the impact of weather by correlating our results with crop phenology to show that WSTATT is able to capture physical properties of crop growth.

Crop Type Mapping

Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Heterogeneous Systems

no code implementations7 Oct 2023 Arvind Renganathan, Rahul Ghosh, Ankush Khandelwal, Vipin Kumar

We present a Task-aware modulation using Representation Learning (TAM-RL) framework that enhances personalized predictions in few-shot settings for heterogeneous systems when individual task characteristics are not known.

Few-Shot Learning Representation Learning

Uncertainty Quantification in Inverse Models in Hydrology

no code implementations3 Oct 2023 Somya Sharma Chatterjee, Rahul Ghosh, Arvind Renganathan, Xiang Li, Snigdhansu Chatterjee, John Nieber, Christopher Duffy, Vipin Kumar

Our inverse model offers 3\% improvement in R$^2$ for the inverse model (basin characteristic estimation) and 6\% for the forward model (streamflow prediction).

Uncertainty Quantification

Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management

no code implementations2 Oct 2023 Somya Sharma Chatterjee, Kelly Lindsay, Neel Chatterjee, Rohan Patil, Ilkay Altintas De Callafon, Michael Steinbach, Daniel Giron, Mai H. Nguyen, Vipin Kumar

Traditional ML methods used for fire modeling offer computational speedup but struggle with physically inconsistent predictions, biased predictions due to class imbalance, biased estimates for fire spread metrics (e. g., burned area, rate of spread), and generalizability in out-of-distribution wind conditions.

Decision Making Management

Message Propagation Through Time: An Algorithm for Sequence Dependency Retention in Time Series Modeling

no code implementations28 Sep 2023 Shaoming Xu, Ankush Khandelwal, Arvind Renganathan, Vipin Kumar

Time series modeling, a crucial area in science, often encounters challenges when training Machine Learning (ML) models like Recurrent Neural Networks (RNNs) using the conventional mini-batch training strategy that assumes independent and identically distributed (IID) samples and initializes RNNs with zero hidden states.

Time Series

Koopman Invertible Autoencoder: Leveraging Forward and Backward Dynamics for Temporal Modeling

no code implementations19 Sep 2023 Kshitij Tayal, Arvind Renganathan, Rahul Ghosh, Xiaowei Jia, Vipin Kumar

Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes.

Decision Making

Reflections from the Workshop on AI-Assisted Decision Making for Conservation

no code implementations17 Jul 2023 Lily Xu, Esther Rolf, Sara Beery, Joseph R. Bennett, Tanya Berger-Wolf, Tanya Birch, Elizabeth Bondi-Kelly, Justin Brashares, Melissa Chapman, Anthony Corso, Andrew Davies, Nikhil Garg, Angela Gaylard, Robert Heilmayr, Hannah Kerner, Konstantin Klemmer, Vipin Kumar, Lester Mackey, Claire Monteleoni, Paul Moorcroft, Jonathan Palmer, Andrew Perrault, David Thau, Milind Tambe

In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022.

Decision Making

Bayesian Federated Learning: A Survey

no code implementations26 Apr 2023 Longbing Cao, Hui Chen, Xuhui Fan, Joao Gama, Yew-Soon Ong, Vipin Kumar

This survey presents a critical overview of BFL, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of BFL from both Bayesian and federated perspectives.

Federated Learning Privacy Preserving

Entity Aware Modelling: A Survey

no code implementations16 Feb 2023 Rahul Ghosh, HaoYu Yang, Ankush Khandelwal, Erhu He, Arvind Renganathan, Somya Sharma, Xiaowei Jia, Vipin Kumar

However, these entity characteristics are not readily available in many real-world scenarios, and different ML methods have been proposed to infer these characteristics from the data.

Fairness Uncertainty Quantification

Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin

no code implementations1 Jan 2023 Leikun Yin, Rahul Ghosh, Chenxi Lin, David Hale, Christoph Weigl, James Obarowski, Junxiong Zhou, Jessica Till, Xiaowei Jia, Troy Mao, Vipin Kumar, Zhenong Jin

In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season.

Clustering Decision Making

Mini-Batch Learning Strategies for modeling long term temporal dependencies: A study in environmental applications

1 code implementation15 Oct 2022 Shaoming Xu, Ankush Khandelwal, Xiang Li, Xiaowei Jia, Licheng Liu, Jared Willard, Rahul Ghosh, Kelly Cutler, Michael Steinbach, Christopher Duffy, John Nieber, Vipin Kumar

To address this issue, we further propose a new strategy which augments a training segment with an initial value of the target variable from the timestep right before the starting of the training segment.

Probabilistic Inverse Modeling: An Application in Hydrology

no code implementations12 Oct 2022 Somya Sharma, Rahul Ghosh, Arvind Renganathan, Xiang Li, Snigdhansu Chatterjee, John Nieber, Christopher Duffy, Vipin Kumar

We propose uncertainty based learning method that offers 6\% improvement in $R^2$ for streamflow prediction (forward modeling) from inverse model inferred basin characteristic estimates, 17\% reduction in uncertainty (40\% in presence of noise) and 4\% higher coverage rate for basin characteristics.

Quantification of Pollen Viability in Lantana camara By Digital Holographic Microscopy

no code implementations10 Oct 2022 Vipin Kumar, Nishant Goyal, Abhishek Prasad, Suresh Babu, Kedar Khare, Gitanjali Yadav

Pollen grains represent the male gametes of seed plants and their viability is critical for efficient sexual reproduction in the plant life cycle.

Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology

no code implementations14 Sep 2021 Rahul Ghosh, Arvind Renganathan, Kshitij Tayal, Xiang Li, Ankush Khandelwal, Xiaowei Jia, Chris Duffy, John Neiber, Vipin Kumar

Furthermore, we show that KGSSL is relatively more robust to distortion than baseline methods, and outperforms the baseline model by 35\% when plugging in KGSSL inferred characteristics.

Self-Supervised Learning

Weakly Supervised Classification Using Group-Level Labels

no code implementations16 Aug 2021 Guruprasad Nayak, Rahul Ghosh, Xiaowei Jia, Vipin Kumar

In many applications, finding adequate labeled data to train predictive models is a major challenge.

Binary Classification Classification +1

Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping

no code implementations16 Aug 2021 Rahul Ghosh, Xiaowei Jia, Chenxi Lin, Zhenong Jin, Vipin Kumar

Common techniques of addressing this issue, based on the underlying idea of pre-training the Deep Neural Networks (DNN) on freely available large datasets, cannot be used for Remote Sensing due to the unavailability of such large-scale labeled datasets and the heterogeneity of data sources caused by the varying spatial and spectral resolution of different sensors.

Clustering Representation Learning +1

CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels

no code implementations26 Jul 2021 Rahul Ghosh, Praveen Ravirathinam, Xiaowei Jia, Ankush Khandelwal, David Mulla, Vipin Kumar

Mapping and monitoring crops is a key step towards sustainable intensification of agriculture and addressing global food security.

Semantic Segmentation

Phase Retrieval using Single-Instance Deep Generative Prior

no code implementations9 Jun 2021 Kshitij Tayal, Raunak Manekar, Zhong Zhuang, David Yang, Vipin Kumar, Felix Hofmann, Ju Sun

Several deep learning methods for phase retrieval exist, but most of them fail on realistic data without precise support information.

Retrieval

Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping

no code implementations2 May 2021 Rahul Ghosh, Praveen Ravirathinam, Xiaowei Jia, Chenxi Lin, Zhenong Jin, Vipin Kumar

The availability of massive earth observing satellite data provide huge opportunities for land use and land cover mapping.

Land Cover Mapping in Limited Labels Scenario: A Survey

no code implementations3 Mar 2021 Rahul Ghosh, Xiaowei Jia, Vipin Kumar

Land cover mapping is essential for monitoring global environmental change and managing natural resources.

BIG-bench Machine Learning

Physics Guided Machine Learning Methods for Hydrology

no code implementations2 Dec 2020 Ankush Khandelwal, Shaoming Xu, Xiang Li, Xiaowei Jia, Michael Stienbach, Christopher Duffy, John Nieber, Vipin Kumar

The goal of this work is to incorporate our understanding of physical processes and constraints in hydrology into machine learning algorithms, and thus bridge the performance gap while reducing the need for large amounts of data compared to traditional data-driven approaches.

BIG-bench Machine Learning

Model-agnostic Methods for Text Classification with Inherent Noise

no code implementations COLING 2020 Kshitij Tayal, Rahul Ghosh, Vipin Kumar

To our knowledge, this is the first time such a comprehensive study in text classification encircling popular models and model-agnostic loss methods has been conducted.

BIG-bench Machine Learning text-classification +1

Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning

1 code implementation10 Nov 2020 Jared D. Willard, Jordan S. Read, Alison P. Appling, Samantha K. Oliver, Xiaowei Jia, Vipin Kumar

This method, Meta Transfer Learning (MTL), builds a meta-learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance.

Meta-Learning Transfer Learning

Deep Learning Initialized Phase Retrieval

no code implementations23 Oct 2020 Raunak Manekar, Zhong Zhuang, Kshitij Tayal, Vipin Kumar, Ju Sun

Phase retrieval (PR) consists of estimating 2D or 3D objects from their Fourier magnitudes and takes a central place in scientific imaging.

Retrieval

Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks

no code implementations26 Sep 2020 Xiaowei Jia, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver, Steven Markstrom, Jared Willard, Shaoming Xu, Michael Steinbach, Jordan Read, Vipin Kumar

This paper proposes a physics-guided machine learning approach that combines advanced machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks.

BIG-bench Machine Learning

Inverse Problems, Deep Learning, and Symmetry Breaking

no code implementations20 Mar 2020 Kshitij Tayal, Chieh-Hsin Lai, Vipin Kumar, Ju Sun

In many physical systems, inputs related by intrinsic system symmetries are mapped to the same output.

Retrieval

Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

no code implementations10 Mar 2020 Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, Vipin Kumar

There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with state-of-the-art machine learning (ML) techniques.

BIG-bench Machine Learning

Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles

no code implementations28 Jan 2020 Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan S. Read, Jacob A Zwart, Michael Steinbach, Vipin Kumar

Specifically, we show that a PGRNN can improve prediction accuracy over that of physics-based models, while generating outputs consistent with physical laws.

BIG-bench Machine Learning

A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series

no code implementations3 Jun 2019 Saurabh Agrawal, Saurabh Verma, Anuj Karpatne, Stefan Liess, Snigdhansu Chatterjee, Vipin Kumar

Traditional approaches focus on finding relationships between two entire time series, however, many interesting relationships exist in small sub-intervals of time and remain feeble during other sub-intervals.

Time Series Time Series Analysis

Automated Monitoring Cropland Using Remote Sensing Data: Challenges and Opportunities for Machine Learning

no code implementations8 Apr 2019 Xiaowei Jia, Ankush Khandelwal, Vipin Kumar

This paper provides an overview of how recent advances in machine learning and the availability of data from earth observing satellites can dramatically improve our ability to automatically map croplands over long period and over large regions.

BIG-bench Machine Learning

Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles

no code implementations31 Oct 2018 Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Zwart, Michael Steinbach, Vipin Kumar

This paper proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improve the modeling of physical processes.

Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks

1 code implementation6 Oct 2018 Saurabh Agrawal, Michael Steinbach, Daniel Boley, Snigdhansu Chatterjee, Gowtham Atluri, Anh The Dang, Stefan Liess, Vipin Kumar

In many domains, there is significant interest in capturing novel relationships between time series that represent activities recorded at different nodes of a highly complex system.

Time Series Time Series Analysis

Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes

no code implementations5 Oct 2018 Xiaowei Jia, Anuj Karpatne, Jared Willard, Michael Steinbach, Jordan Read, Paul C Hanson, Hilary A Dugan, Vipin Kumar

In this paper, we introduce a novel framework for combining scientific knowledge within physics-based models and recurrent neural networks to advance scientific discovery in many dynamical systems.

Mining Sub-Interval Relationships In Time Series Data

no code implementations16 Feb 2018 Saurabh Agrawal, Saurabh Verma, Gowtham Atluri, Anuj Karpatne, Stefan Liess, Angus Macdonald III, Snigdhansu Chatterjee, Vipin Kumar

In this paper, we define the notion of a sub-interval relationship (SIR) to capture inter- actions between two time series that are prominent only in certain sub-intervals of time.

Computational Efficiency Time Series +1

Discovery of Shifting Patterns in Sequence Classification

no code implementations19 Dec 2017 Xiaowei Jia, Ankush Khandelwal, Anuj Karpatne, Vipin Kumar

The experiments demonstrate the superiority of our proposed method in sequence classification performance and in detecting discriminative shifting patterns.

Classification General Classification

ORBIT: Ordering Based Information Transfer Across Space and Time for Global Surface Water Monitoring

no code implementations15 Nov 2017 Ankush Khandelwal, Anuj Karpatne, Vipin Kumar

Various data fusion methods have been proposed in the literature that mainly rely on individual timesteps when both datasets are available to learn a mapping between features values at different resolutions using local relationships between pixels.

Earth Observation

Spatio-Temporal Data Mining: A Survey of Problems and Methods

1 code implementation13 Nov 2017 Gowtham Atluri, Anuj Karpatne, Vipin Kumar

Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains including, climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences.

Anomaly Detection Change Detection +2

Machine Learning for the Geosciences: Challenges and Opportunities

no code implementations13 Nov 2017 Anuj Karpatne, Imme Ebert-Uphoff, Sai Ravela, Hassan Ali Babaie, Vipin Kumar

Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet.

BIG-bench Machine Learning

Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling

2 code implementations31 Oct 2017 Arka Daw, Anuj Karpatne, William Watkins, Jordan Read, Vipin Kumar

This paper introduces a framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery.

Mining Electronic Health Records: A Survey

no code implementations9 Feb 2017 Pranjul Yadav, Michael Steinbach, Vipin Kumar, Gyorgy Simon

In this manuscript, we provide a structured and comprehensive overview of data mining techniques for modeling EHR data.

Management

Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data

no code implementations27 Dec 2016 Anuj Karpatne, Gowtham Atluri, James Faghmous, Michael Steinbach, Arindam Banerjee, Auroop Ganguly, Shashi Shekhar, Nagiza Samatova, Vipin Kumar

Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery.

Causal Inference in Observational Data

no code implementations15 Nov 2016 Pranjul Yadav, Lisiane Prunelli, Alexander Hoff, Michael Steinbach, Bonnie Westra, Vipin Kumar, Gyorgy Simon

We also evaluated our causal rule mining framework on the Electronic Health Records (EHR) data of a large cohort of patients from Mayo Clinic and showed that the patterns we extracted are sufficiently rich to explain the controversial findings in the medical literature regarding the effect of a class of cholesterol drugs on Type-II Diabetes Mellitus (T2DM).

Causal Inference

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