Neural Message Passing for Multi-label Classification

Lanchantin, Jack; Sekhon, Arshdeep; Qi, Yanjun

doi:10.1007/978-3-030-46147-8_9

Cited by 28 publications

(37 citation statements)

References 25 publications

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“…The attention mechanism is the crux behind many state-of-theart sequence-to-sequence models used in machine translation and language processing 40 and it has recently shown good results on multi-label classification. 41 While the attention mechanism has also been recently adopted to perform learning of relationships among elements in material property prediction, 34,35 our model additionally uses the attention mechanism to perform learning of relationships among multiple material properties by acting on the output of the multivariate Gaussian model as opposed to the composition itself.…”

Section: Discussionmentioning

confidence: 99%

“…Higher-order property correlation learning proceeds via an attention graph neural network, whose description can be found in prior literature. 34,35,40,41 We use five attention layers, namely, the message-passing operations are executed five times. Each attention layer also includes an element-wise feed-forward MLP which has two layers of 128 neurons each.…”

Section: H-clmp Modelmentioning

confidence: 99%

“…Soft-attention builds upon this concept by allowing the function that produces the attention coefficients to be learned directly from the data. 40,41 Roost and CrabNet use graph attention networks (GAT) wherein the nodes are elements, enabling learning of interactions among elements. Additionally, H-CLMP uses GAT where the nodes are multi-property embeddings to learn relationships among multiple materials properties.…”

Section: Introductionmentioning

confidence: 99%

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Materials Representation and Transfer Learning for Multi-Property Prediction

Kong¹,

Guevarra²,

Gomes³

et al. 2021

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

Section: H-clmp Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Materials Representation and Transfer Learning for Multi-Property Prediction

Kong¹,

Guevarra²,

Gomes³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…where t p is true positive, f p is false positive and f n is false negative. High F 1 -ma usually indicate high performance on less frequent labels [45].…”

Section: Performance Metricsmentioning

confidence: 99%

Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Faustine

Pereira²

2020

Energies

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The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.

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“…Inspired by [21,32], we propose a PMP module to encode the state by taking into account the relation between an EHR and the hierarchical ICD structure, parent-child relations, and sibling relations of ICD codes, as shown in Figure 3. Formally, is defined as:…”

Section: Path Message Passingmentioning

confidence: 99%

Coding Electronic Health Records with Adversarial Reinforcement Path Generation

Wang

Ren

Chen

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Electronic Health Record (EHR) coding is the task of assigning one or more International Classification of Diseases (ICD) codes to every EHR. Most previous work either ignores the hierarchical nature of the ICD codes or only focuses on parent-child relations. Moreover, existing EHR coding methods predict ICD codes from the leaf level with the greatest ICD number and the most fine-grained categories, which makes it difficult for models to make correct decisions. In order to address these problems, we model EHR coding as a path generation task. For this approach, we need to address two main challenges: (1) How to model relations between EHR and ICD codes, and relations between ICD codes? (2) How to evaluate the quality of generated ICD paths in order to obtain a signal that can be used to supervise the learning? We propose a coarse-tofine ICD path generation framework, named Reinforcement Path Generation Network (RPGNet), that implements EHR coding with a Path Generator (PG) and a Path Discriminator (PD). We address challenge (1) by introducing a Path Message Passing (PMP) module in the PG to encode three types of relation: between EHRs and ICD codes, between parent-child ICD codes, and between sibling ICD codes. To address challenge (2), we propose a PD component that estimates the reward for each ICD code in a generated path. RPGNet is trained with Reinforcement Learning (RL) in an adversarial manner. Experiments on the MIMIC-III benchmark dataset show that RPGNet significantly outperforms state-of-the-art methods in terms of micro-averaged F1 and micro-averaged AUC. CCS CONCEPTS• Information systems → Content analysis and feature selection; • Applied computing → Health care information systems; • Computing methodologies → Adversarial learning; Reinforcement learning.

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Neural Message Passing for Multi-label Classification

Cited by 28 publications

References 25 publications

Materials Representation and Transfer Learning for Multi-Property Prediction

Materials Representation and Transfer Learning for Multi-Property Prediction

Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Coding Electronic Health Records with Adversarial Reinforcement Path Generation

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