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Found 52 papers, 7 papers with code
Knowledge-guided Machine Learning: Current Trends and Future Prospects
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
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.
Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Heterogeneous Systems
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.
Uncertainty Quantification in Inverse Models in Hydrology
Our inverse model offers 3\% improvement in R$^2$ for the inverse model (basin characteristic estimation) and 6\% for the forward model (streamflow prediction).
Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management
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.
Message Propagation Through Time: An Algorithm for Sequence Dependency Retention in Time Series Modeling
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.
Koopman Invertible Autoencoder: Leveraging Forward and Backward Dynamics for Temporal Modeling
Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes.
Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources
Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science.
Global Precipitation Nowcasting of Integrated Multi-satellitE Retrievals for GPM: A U-Net Convolutional LSTM Architecture
This paper presents a deep learning architecture for nowcasting of precipitation almost globally every 30 min with a 4-hour lead time.
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
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.
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
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.
Bayesian Federated Learning: A Survey
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.
Entity Aware Modelling: A Survey
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.
Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin
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.
Realization of Causal Representation Learning to Adjust Confounding Bias in Latent Space
Causal DAGs(Directed Acyclic Graphs) are usually considered in a 2D plane.
Mini-Batch Learning Strategies for modeling long term temporal dependencies: A study in environmental applications
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.
Spatiotemporal Classification with limited labels using Constrained Clustering for large datasets
Using this large unlabelled dataset, we first show how a spatiotemporal representation is better compared to just spatial or temporal representation.
Probabilistic Inverse Modeling: An Application in Hydrology
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
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
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.
Weakly Supervised Classification Using Group-Level Labels
In many applications, finding adequate labeled data to train predictive models is a major challenge.
Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping
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.
CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels
Mapping and monitoring crops is a key step towards sustainable intensification of agriculture and addressing global food security.
Phase Retrieval using Single-Instance Deep Generative Prior
Several deep learning methods for phase retrieval exist, but most of them fail on realistic data without precise support information.
Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping
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
Land cover mapping is essential for monitoring global environmental change and managing natural resources.
Physics Guided Machine Learning Methods for Hydrology
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.
Model-agnostic Methods for Text Classification with Inherent Noise
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.
Regularized Graph Convolutional Networks for Short Text Classification
The lack of structure in short text sequences limits the success of popular NLP methods based on deep learning.
Incorporating Causal Effects into Deep Learning Predictions on EHR Data
Electronic Health Records (EHR) data analysis plays a crucial role in healthcare system quality.
Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning
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.
Deep Learning Initialized Phase Retrieval
Phase retrieval (PR) consists of estimating 2D or 3D objects from their Fourier magnitudes and takes a central place in scientific imaging.
Unlocking Inverse Problems Using Deep Learning: Breaking Symmetries in Phase Retrieval
In many physical systems, inputs related by intrinsic system symmetries generate the same output.
Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks
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.
Inverse Problems, Deep Learning, and Symmetry Breaking
In many physical systems, inputs related by intrinsic system symmetries are mapped to the same output.
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
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.
Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles
Specifically, we show that a PGRNN can improve prediction accuracy over that of physics-based models, while generating outputs consistent with physical laws.
Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data
Many real-world phenomena are observed at multiple resolutions.
A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series
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.
Automated Monitoring Cropland Using Remote Sensing Data: Challenges and Opportunities for Machine Learning
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.
Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles
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
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.
Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes
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
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.
Discovery of Shifting Patterns in Sequence Classification
The experiments demonstrate the superiority of our proposed method in sequence classification performance and in detecting discriminative shifting patterns.
ORBIT: Ordering Based Information Transfer Across Space and Time for Global Surface Water Monitoring
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.
Machine Learning for the Geosciences: Challenges and Opportunities
Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet.
Spatio-Temporal Data Mining: A Survey of Problems and Methods
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.
Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
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
In this manuscript, we provide a structured and comprehensive overview of data mining techniques for modeling EHR data.
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
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
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).
