Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation

Kong, Shufeng; Bai, Junwen; Lee, Jae Hee; Chen, Di; Allyn, Andrew; Stuart, Michelle; Pinsky, Malin L.; Mills, Katherine E.; Gomes, Carla P.

doi:10.24963/ijcai.2020/603

“…Here, we worked around the problem by converting the abundances to binned abundance classes, which reduces the impact of zeros. Multivariate hurdle approaches have been proposed (24) but this remains an active area of research. One could diminish the impact of true zeros by augmenting training data for a model by providing access to naïve predictions (e.g.…”

Section: Discussionmentioning

confidence: 99%

Predicting and prioritizing community assembly: learning outcomes via experiments

Blonder

¹

,

Godoy

²

2022

Preprint

View full text Add to dashboard Cite

Predicting species coexistence can be difficult often because underlying assembly processes are unknown and data are limited. However, accurate predictions are needed for design and forecasting problems in biodiversity conservation, climate change, invasion ecology, restoration ecology, and synthetic ecology. Here we describe an approach (Learning Outcomes Via Experiments; LOVE) where a limited set of experiments are conducted and multiple community outcomes measured (richness, composition, and abundance), from which a model is trained to predict outcomes for arbitrary experiments. Across seven taxonomically diverse datasets, LOVE predicts test outcomes with low error when trained on ~100 randomly-selected experiments. LOVE can then prioritize experiments for tasks like maximizing outcome richness or total abundance, or minimizing abundances of unwanted species. LOVE complements existing mechanism-first approaches to prediction and shows that rapid screening of communities for desirable properties may become possible.

show abstract

“…The integration of AI techniques in the present work builds upon the foundation of our prior work developing general multi-label classification and multi-target regression approaches that were initially motivated by ecology applications. These prior works demonstrated the utility of multivariate Gaussian used for pairwise correlation learning, 36 model alignment with a VAE during training, 37,39 and high-order correlation learning via an attention graph neural network. 38 Our careful crafting of model architecture is particularly motivated by the need to make predictions in new composition spaces.…”

Section: Discussionmentioning

confidence: 99%

“…We tackle this challenge with correlation learning, which has been demonstrated to enhance multi-label classification and multi-target regression. [36][37][38][39] Since the multiple properties being predicted may not be explicitly correlated, we developed a framework to learn correlations in latent embeddings of the multiple properties.…”

Section: Introductionmentioning

confidence: 99%

Materials Representation and Transfer Learning for Multi-Property Prediction

Kong¹,

Guevarra²,

Gomes³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material’s composition, (ii) learning and exploitation of correlations among target properties in multitarget regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences.

show abstract

“…The integration of AI techniques in the present work build upon the foundation of our prior work developing general multi-label classification and and multi-target regression approaches that were initially motivated by ecology applications. These prior works demonstrated the utility of multivariate Gaussian used for pairwise correlation learning, 35 model alignment with a VAE during training, 36,38 and high-order correlation learning via an attention graph neural network. 37 Our careful crafting of model architecture is particularly motivated by the need to make predictions in new composition spaces.…”

Section: Discussionmentioning

confidence: 99%

“…We tackle this challenge with correlation learning, which has been demonstrated to enhance multilabel classification and multi-target regression. [35][36][37][38] Since the multiple properties being predicted may not be explicitly correlated, we developed a framework to learn correlations in latent embeddings of the multiple properties.…”

Section: Introductionmentioning

confidence: 99%

Materials Representation and Transfer Learning for Multi-Property Prediction

Kong¹,

Guevarra²,

Gomes³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material’s composition, (ii) learning and exploitation of correlations among target properties in multitarget regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences.

show abstract

Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation

Cited by 13 publications

References 5 publications

Predicting and prioritizing community assembly: learning outcomes via experiments

Predicting and prioritizing community assembly: learning outcomes via experiments

Materials Representation and Transfer Learning for Multi-Property Prediction

Materials Representation and Transfer Learning for Multi-Property Prediction

Contact Info

Product

Resources

About