216 citations · 328 across the 8 of their papers we have counts for
8 papers
Multimodal Foundation Models for Material Property Prediction and Discovery
Viggo Moro, Charlotte Loh, Rumen Dangovski +7
Artificial intelligence is transforming computational materials science, improving the prediction of material properties, and accelerating the discovery of novel materials. Recentl…
Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare
Won Joon Yun, Samuel Kim, Joongheon Kim
The prodigious growth of digital health data has precipitated a mounting interest in harnessing machine learning methodologies, such as natural language processing (NLP), to scruti…
Deep Learning and Symbolic Regression for Discovering Parametric Equations
Michael Zhang, Samuel Kim, Peter Y. Lu +1
Symbolic regression is a machine learning technique that can learn the governing formulas of data and thus has the potential to transform scientific discovery. However, symbolic re…
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science
Charlotte Loh, Thomas Christensen, Rumen Dangovski +2
Deep learning techniques have been increasingly applied to the natural sciences, e.g., for property prediction and optimization or material discovery. A fundamental ingredient of s…
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
Samuel Kim, Peter Y. Lu, Charlotte Loh +3
Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-b…
OccamNet: A Fast Neural Model for Symbolic Regression at Scale
Owen Dugan, Rumen Dangovski, Allan Costa +4
Neural networks' expressiveness comes at the cost of complex, black-box models that often extrapolate poorly beyond the domain of the training dataset, conflicting with the goal of…
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
Samuel Kim, Peter Y. Lu, Srijon Mukherjee +4
Symbolic regression is a powerful technique that can discover analytical equations that describe data, which can lead to explainable models and generalizability outside of the trai…
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning
Peter Y. Lu, Samuel Kim, Marin Soljačić
Experimental data is often affected by uncontrolled variables that make analysis and interpretation difficult. For spatiotemporal systems, this problem is further exacerbated by th…