no code implementations • 24 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.
no code implementations • 29 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.
no code implementations • 7 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.
no code implementations • 3 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).
no code implementations • 2 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.
no code implementations • 28 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.
no code implementations • 19 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.
no code implementations • 18 Aug 2023 • Jared D. Willard, Charuleka Varadharajan, Xiaowei Jia, Vipin Kumar
Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science.
1 code implementation • 20 Jul 2023 • Reyhaneh Rahimi, Praveen Ravirathinam, Ardeshir Ebtehaj, Ali Behrangi, Jackson Tan, Vipin Kumar
This paper presents a deep learning architecture for nowcasting of precipitation almost globally every 30 min with a 4-hour lead time.
no code implementations • 17 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.
no code implementations • 9 Jul 2023 • Hector Zenil, Jesper Tegnér, Felipe S. Abrahão, Alexander Lavin, Vipin Kumar, Jeremy G. Frey, Adrian Weller, Larisa Soldatova, Alan R. Bundy, Nicholas R. Jennings, Koichi Takahashi, Lawrence Hunter, Saso Dzeroski, Andrew Briggs, Frederick D. Gregory, Carla P. Gomes, Jon Rowe, James Evans, Hiroaki Kitano, Ross King
Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
no code implementations • 26 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.
no code implementations • 16 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.
no code implementations • 1 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.
1 code implementation • 15 Nov 2022 • Jia Li, Xiang Li, Xiaowei Jia, Michael Steinbach, Vipin Kumar
Causal DAGs(Directed Acyclic Graphs) are usually considered in a 2D plane.
1 code implementation • 15 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.
no code implementations • 14 Oct 2022 • Praveen Ravirathinam, Rahul Ghosh, Ke Wang, Keyang Xuan, Ankush Khandelwal, Hilary Dugan, Paul Hanson, Vipin Kumar
Using this large unlabelled dataset, we first show how a spatiotemporal representation is better compared to just spatial or temporal representation.
no code implementations • 12 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.
no code implementations • 10 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.
no code implementations • 14 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.
no code implementations • 16 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.
no code implementations • 16 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.
no code implementations • 26 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.
no code implementations • 9 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.
no code implementations • 2 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.
no code implementations • 3 Mar 2021 • Rahul Ghosh, Xiaowei Jia, Vipin Kumar
Land cover mapping is essential for monitoring global environmental change and managing natural resources.
no code implementations • 2 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.
no code implementations • COLING 2020 • Kshitij Tayal, Nikhil Rao, Saurabh Agarwal, Xiaowei Jia, Karthik Subbian, Vipin Kumar
The lack of structure in short text sequences limits the success of popular NLP methods based on deep learning.
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.
no code implementations • 11 Nov 2020 • Jia Li, HaoYu Yang, Xiaowei Jia, Vipin Kumar, Michael Steinbach, Gyorgy Simon
Electronic Health Records (EHR) data analysis plays a crucial role in healthcare system quality.
1 code implementation • 10 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.
no code implementations • 23 Oct 2020 • Kshitij Tayal, Chieh-Hsin Lai, Raunak Manekar, Zhong Zhuang, Vipin Kumar, Ju Sun
In many physical systems, inputs related by intrinsic system symmetries generate the same output.
no code implementations • 23 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.
no code implementations • 26 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.
no code implementations • 20 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.
no code implementations • 10 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.
no code implementations • 28 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.
no code implementations • 3 Jan 2020 • Guruprasad Nayak, Rahul Ghosh, Xiaowei Jia, Varun Mithal, Vipin Kumar
Many real-world phenomena are observed at multiple resolutions.
no code implementations • 3 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.
no code implementations • 8 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.
no code implementations • 31 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.
1 code implementation • 6 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.
no code implementations • 5 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.
no code implementations • 16 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.
no code implementations • 19 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.
no code implementations • 15 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.
no code implementations • 13 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.
1 code implementation • 13 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.
2 code implementations • 31 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.
no code implementations • 9 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.
no code implementations • 27 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.
no code implementations • 15 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).