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Found 44 papers, 5 papers with code
When are Foundation Models Effective? Understanding the Suitability for Pixel-Level Classification Using Multispectral Imagery
This work aims to enhance the understanding of the status and suitability of foundation models for pixel-level classification using multispectral imagery at moderate resolution, through comparisons with traditional machine learning (ML) and regular-size deep learning models.
LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models
To address these issues, we propose LITE -- a multimodal large language model for environmental ecosystems modeling.
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.
Improving Explainable Object-induced Model through Uncertainty for Automated Vehicles
The rapid evolution of automated vehicles (AVs) has the potential to provide safer, more efficient, and comfortable travel options.
Referee-Meta-Learning for Fast Adaptation of Locational Fairness
When dealing with data from distinct locations, machine learning algorithms tend to demonstrate an implicit preference of some locations over the others, which constitutes biases that sabotage the spatial fairness of the algorithm.
Nature-Guided Cognitive Evolution for Predicting Dissolved Oxygen Concentrations in North Temperate Lakes
Using these simulated labels, we implement a multi-population cognitive evolutionary search, where models, mirroring natural organisms, adaptively evolve to select relevant feature interactions within populations for different lake types and tasks.
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.
SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models
Fairness-awareness has emerged as an essential building block for the responsible use of artificial intelligence in real applications.
Phenotyping calcification in vascular tissues using artificial intelligence
Fundamental studies are needed to determine how risk is influenced by the diverse calcification phenotypes.
FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems
This raises a fundamental question in advancing the modeling of environmental ecosystems: how to build a general framework for modeling the complex relationships amongst various environmental data over space and time?
From molecules to scaffolds to functional groups: building context-dependent molecular representation via multi-channel learning
Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks.
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.
HOSSnet: an Efficient Physics-Guided Neural Network for Simulating Crack Propagation
Hybrid Optimization Software Suite (HOSS), which is a combined finite-discrete element method (FDEM), is one of the advanced approaches to simulating high-fidelity fracture and fragmentation processes but the application of pure HOSS simulation is computationally expensive.
Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement
The results on two different types of turbulent flow data confirm the superiority of the proposed method in reconstructing the high-resolution DNS data and preserving the physical characteristics of flow transport.
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.
STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19
While deep learning approaches outperform conventional estimation techniques on tasks with abundant training data, the continuously evolving pandemic poses a significant challenge to solving this problem due to data nonstationarity, limited observations, and complex social contexts.
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.
Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability
One major advantage of our proposed method is that it improves the model adaptation to a large number of heterogeneous tasks.
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.
Graph-Augmented Cyclic Learning Framework for Similarity Estimation of Medical Clinical Notes
Semantic textual similarity (STS) in the clinical domain helps improve diagnostic efficiency and produce concise texts for downstream data mining tasks.
Modeling Reservoir Release Using Pseudo-Prospective Learning and Physical Simulations to Predict Water Temperature
However, the information of released water flow is often not available for many reservoirs, which makes it difficult for data-driven models to capture the impact to downstream river segments.
Heterogeneous Stream-reservoir Graph Networks with Data Assimilation
In this paper, we propose a heterogeneous recurrent graph model to represent these interacting processes that underlie stream-reservoir networks and improve the prediction of water temperature in all river segments within a network.
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.
Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks
Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers.
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.
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.
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.
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks
In this paper, we develop a real-time active learning method that uses the spatial and temporal contextual information to select representative query samples in a reinforcement learning framework.
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.
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.
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.
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.
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.
