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08/21/2023
Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation
In recent years, several Weakly Supervised Semantic Segmentation (WS3) m...
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06/05/2023
Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks
Discovering evolutionary traits that are heritable across species on the...
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05/24/2023
Let There Be Order: Rethinking Ordering in Autoregressive Graph Generation
Conditional graph generation tasks involve training a model to generate ...
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11/02/2022
Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring
Inferring the source information of greenhouse gases, such as methane, f...
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07/05/2022
Rethinking the Importance of Sampling in Physics-informed Neural Networks
Physics-informed neural networks (PINNs) have emerged as a powerful tool...
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02/12/2022
Physics-Guided Problem Decomposition for Scaling Deep Learning of High-dimensional Eigen-Solvers: The Case of Schrödinger's Equation
Given their ability to effectively learn non-linear mappings and perform...
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11/16/2021
Adjoint-Matching Neural Network Surrogates for Fast 4D-Var Data Assimilation
The data assimilation procedures used in many operational numerical weat...
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10/01/2021
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)
A central goal in deep learning is to learn compact representations of f...
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06/06/2021
PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics
As applications of deep learning (DL) continue to seep into critical sci...
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04/09/2021
A Data-Driven Approach to Full-Field Damage and Failure Pattern Prediction in Microstructure-Dependent Composites using Deep Learning
An image-based deep learning framework is developed in this paper to pre...
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01/20/2021
Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs
We propose quadratic residual networks (QRes) as a new type of parameter...
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09/30/2020
GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization
This paper proposes a novel method for online Multi-Object Tracking (MOT...
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07/02/2020
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling Approach
The objective of unsupervised graph representation learning (GRL) is to ...
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07/02/2020
Learning Neural Networks with Competing Physics Objectives: An Application in Quantum Mechanics
Physics-guided Machine Learning (PGML) is an emerging field of research ...
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01/28/2020
Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles
Physics-based models of dynamical systems are often used to study engine...
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11/06/2019
Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling
To simultaneously address the rising need of expressing uncertainties in...
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11/06/2019
Physics-guided Design and Learning of Neural Networks for Predicting Drag Force on Particle Suspensions in Moving Fluids
Physics-based simulations are often used to model and understand complex...
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06/03/2019
A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series
Traditional approaches focus on finding relationships between two entire...
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10/31/2018
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 (PGR...
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10/05/2018
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 k...
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02/16/2018
Mining Sub-Interval Relationships In Time Series Data
Time-series data is being increasingly collected and stud- ied in severa...
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12/19/2017
Discovery of Shifting Patterns in Sequence Classification
In this paper, we investigate the multi-variate sequence classification ...
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11/13/2017
Spatio-Temporal Data Mining: A Survey of Problems and Methods
Large volumes of spatio-temporal data are increasingly collected and stu...
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11/13/2017
Machine Learning for the Geosciences: Challenges and Opportunities
Geosciences is a field of great societal relevance that requires solutio...
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10/31/2017
Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
This paper introduces a novel framework for learning data science models...
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12/27/2016