BGADAM: Boosting based Genetic-Evolutionary ADAM for Neural Network Optimization

Bai, Jiyang; Ren, Yuxiang; Zhang, Jiawei

doi:10.1109/ijcnn52387.2021.9533744

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Another graph neural network (GNN) based approach is to train the GNN using subgraphs as mini-batches instead of training a GNN on the full original graph. This method achieved robust performance with less training time and memory resource requirements [29].…”

Section: Literature Reviewmentioning

confidence: 99%

An Ensemble Learning Approach to perform Link Prediction on Large Scale Biomedical Knowledge Graphs for Drug Repurposing and Discovery

Prabhakar¹,

Vu²,

Crawford³

et al. 2023

Preprint

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Generating knowledge graph embeddings (KGEs) to represent entities (nodes) and relations (edges) in large scale knowledge graph datasets has been a challenging problem in representation learning. This is primarily because the embeddings / vector representations that are required to encode the full scope of data in a large heterogeneous graph needs to have a high dimensionality. The orientation of a large number of vectors requires a lot of space which is achieved by projecting the embeddings to higher dimensions. This is not a scalable solution especially when we expect the knowledge graph to grow in size in order to incorporate more data. Any efforts to constrain the embeddings to lower number of dimensions could be problematic as insufficient space to spatially orient the large number of embeddings / vector representations within limited number of dimensions could lead to poor inferencing on downstream tasks such as link prediction which leverage these embeddings to predict the likelihood of existence of a link between two or more entities in a knowledge graph. This is especially the case with large biomedical knowledge graphs which relate several diverse entities such as genes, diseases, signaling pathways, biological functions etc. that are clinically relevant for the application of KGs to drug discovery. The size of the biomedical knowledge graphs are therefore much larger compared to typical benchmark knowledge graph datasets. This poses a huge challenge in generating embeddings / vector representations of good quality to represent the latent semantic structure of the graph. Attempts to circumvent this challenge by increasing the dimensionality of the embeddings often render hardware limitations as generating high dimensional embeddings is computationally expensive and often times infeasible. To practically deal with representing the latent structure of such large scale knowledge graphs (KGs), our work proposes an ensemble learning model in which the full knowledge graph is sampled into several smaller subgraphs and KGE models generate embeddings for each individual subgraph. The results of link prediction from the KGE models trained on each subgraph are then aggregated to generate a consolidated set of link predictions across the full knowledge graph. The experimental results demonstrated significant improvement in rank-based evaluation metrics on task specific link predictions as well as general link predictions on four open-sourced biomedical knowledge graph datasets.

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Section: Literature Reviewmentioning

confidence: 99%

An Ensemble Learning Approach to perform Link Prediction on Large Scale Biomedical Knowledge Graphs for Drug Repurposing and Discovery

Prabhakar¹,

Vu²,

Crawford³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…These methods are referred as Adam-type algorithms since the adaptive learning rates are employed. Further, Adam has attained the most wide application in many deep learning training tasks, such as optimization of convolutional neural networks and recurrent neural networks [ 19 , 20 ]. Despite its popularity, Adam incurs the convergence issue.…”

Section: Introductionmentioning

confidence: 99%

Online Learning for DNN Training: A Stochastic Block Adaptive Gradient Algorithm

Liu

Zhou

et al. 2022

Computational Intelligence and Neuroscience

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Adaptive algorithms are widely used because of their fast convergence rate for training deep neural networks (DNNs). However, the training cost becomes prohibitively expensive due to the computation of the full gradient when training complicated DNN. To reduce the computational cost, we present a stochastic block adaptive gradient online training algorithm in this study, called SBAG. In this algorithm, stochastic block coordinate descent and the adaptive learning rate are utilized at each iteration. We also prove that the regret bound of O T can be achieved via SBAG, in which T is a time horizon. In addition, we use SBAG to train ResNet-34 and DenseNet-121 on CIFAR-10, respectively. The results demonstrate that SBAG has better training speed and generalized ability than other existing training methods.

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BGADAM: Boosting based Genetic-Evolutionary ADAM for Neural Network Optimization

Cited by 2 publications

References 12 publications

An Ensemble Learning Approach to perform Link Prediction on Large Scale Biomedical Knowledge Graphs for Drug Repurposing and Discovery

An Ensemble Learning Approach to perform Link Prediction on Large Scale Biomedical Knowledge Graphs for Drug Repurposing and Discovery

Online Learning for DNN Training: A Stochastic Block Adaptive Gradient Algorithm

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