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RankML: a Meta Learning-Based Approach for Pre-Ranking Machine Learning Pipelines [article]

Doron Laadan, Roman Vainshtein, Yarden Curiel, Gilad Katz, Lior Rokach
2019 arXiv   pre-print
In this study, we propose RankML, a meta-learning based approach for predicting the performance of whole machine learning pipelines.  ...  Given a previously-unseen dataset, a performance metric, and a set of candidate pipelines, RankML immediately produces a ranked list of all pipelines based on their predicted performance.  ...  Our contributions in this paper are as follows: • We present RankML, a meta learning-based approach for the ranking of ML pipeline based on their predicted performance.  ... 
arXiv:1911.00108v2 fatcat:rmdy57pcgnhhtia3xfcqxjw7sa

Learning to rank: a ROC-based graph-theoretic approach

Willem Waegeman
2009 4OR  
neural network.  ...  Many machine learning algorithms like kernel methods and neural networks employ this kind of regularizer to prevent overfitting.  ...  This triplet of AUCs is T M -transitive since If we try to construct a graph for this triplet of AUCs, then we must have 11 edges departing from V 3 and arriving in V 1 and we must have 1 edge departing  ... 
doi:10.1007/s10288-009-0095-y fatcat:itepcyi7fzamxkftlnrkksd2xu

A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases

Muhammad Attique Khan, Tallha Akram, Muhammad Sharif, Majed Alhaisoni, Tanzila Saba, Nadia Nawaz
2021 EURASIP Journal on Image and Video Processing  
Then the most appropriate features are selected using a novel framework of entropy and rank-based correlation (EaRbC).  ...  The proposed approach incorporates five primary steps including preprocessing,Standard instruction requires city and country for affiliations.  ...  The authors are grateful for this financial support. We also like to thank the Plant Village community for developing this large dataset and VC, HITEC University, Taxila Pakistan.  ... 
doi:10.1186/s13640-021-00558-2 fatcat:g2fge3ho5nabzlybvwby2zpg4e

Polychromatic neural networks

Francis T.S. Yu, Xiangyang Yang, Don A. Gregory
1992 Optics Communications  
Instead of training individual networks with different width configurations, we train a shared network with switchable batch normalization.  ...  We present a simple and general method to train a single neural network executable at different widths 1 , permitting instant and adaptive accuracy-efficiency trade-offs at runtime.  ...  To reduce computation of a neural network, some works propose to adaptively construct the computation graph of a neural network.  ... 
doi:10.1016/0030-4018(92)90489-e fatcat:6yyv3zvp7jfk7mogt6oeah3why

Slimmable Neural Networks [article]

Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, Thomas Huang
2018 arXiv   pre-print
We present a simple and general method to train a single neural network executable at different widths (number of channels in a layer), permitting instant and adaptive accuracy-efficiency trade-offs at  ...  Instead of training individual networks with different width configurations, we train a shared network with switchable batch normalization.  ...  To reduce computation of a neural network, some works propose to adaptively construct the computation graph of a neural network.  ... 
arXiv:1812.08928v1 fatcat:wrojie66gzfwlfvizv5olagosy

NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification [article]

Julian Busch, Anton Kocheturov, Volker Tresp, Thomas Seidl
2021 arXiv   pre-print
Our approach first extracts flow graphs and subsequently classifies them using a novel graph neural network model.  ...  Experiments on four different prediction tasks consistently demonstrate the advantages of our approach and show that our graph neural network model can boost detection performance by a significant margin  ...  Instead of classifying individual network flows, as the original work proposes, we follow our graph-based approach and construct a flow graph for each execution of a candidate application.  ... 
arXiv:2103.03939v2 fatcat:wjgxdcqvybbtbiqraariaixa6u

Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising [article]

Siheng Chen and Yonina C. Eldar and Lingxiao Zhao
2020 arXiv   pre-print
neural networks.  ...  For denoising a single smooth graph signal, the normalized mean square error of the proposed networks is around 40% and 60% lower than that of graph Laplacian denoising and graph wavelets, respectively  ...  To make a fair comparison, we use the same network setting and training paradigm for graph unrolling networks to train other networks. Baselines.  ... 
arXiv:2006.01301v1 fatcat:eggvqz7uerdbtpbxkh52s2u3bi

Few-shot Network Anomaly Detection via Cross-network Meta-learning [article]

Kaize Ding, Qinghai Zhou, Hanghang Tong, Huan Liu
2021 arXiv   pre-print
Taking advantage of this potential, in this work, we tackle the problem of few-shot network anomaly detection by (1) proposing a new family of graph neural networks -- Graph Deviation Networks (GDN) that  ...  can leverage a small number of labeled anomalies for enforcing statistically significant deviations between abnormal and normal nodes on a network; and (2) equipping the proposed GDN with a new cross-network  ...  Graph Neural Networks Graph neural networks [4, 12, 14, 35] have achieved groundbreaking success in transforming the information of a graph into lowdimensional latent representations.  ... 
arXiv:2102.11165v1 fatcat:2fv55uoue5dn5cvnipgtm3du74

Design Space for Graph Neural Networks [article]

Jiaxuan You, Rex Ying, Jure Leskovec
2021 arXiv   pre-print
The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new architectures as well as novel applications.  ...  Overall, our work offers a principled and scalable approach to transition from studying individual GNN designs for specific tasks, to systematically studying the GNN design space and the task space.  ...  Acknowledgments We thank Jonathan Gomes-Selman, Hongyu Ren, Serina Chang, and Camilo Ruiz for discussions and for providing feedback on our manuscript.  ... 
arXiv:2011.08843v2 fatcat:3fs36ip6yjbg5pv5nh7yttuwlq

Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network [article]

Mohammadreza Radmanesh, Hossein Ghorbanzadeh, Ahmad Asgharian Rezaei, Mahdi Jalili, Xinghuo Yu
2022 arXiv   pre-print
Here, we propose a novel deep asymmetric attributed network embedding model based on convolutional graph neural network, called AAGCN.  ...  We test the performance of AAGCN on three real-world networks for network reconstruction, link prediction, node classification and visualization tasks.  ...  Here we incorporate the network topology and node features to represent non-linear relations between network nodes.  ... 
arXiv:2202.06307v1 fatcat:5s4nptehkrgjrel2rlwbjyv5iy

Network-Based Document Clustering Using External Ranking Loss for Network Embedding

Yeo Chan Yoon, Hyung Kuen Gee, Heuiseok Lim
2019 IEEE Access  
Furthermore, a novel neural-network-based network embedding learning method was devised that considers the significance of a document based on its rankings with external measures, such as the download  ...  In this study, we defined a probabilistic network graph for fine-grained document clustering and developed a probabilistic generative model and calculation method.  ...  NET2VEC: NEURAL NE WITH EXTERNAL RANKING LOSS This section describes a novel network-embedding method with external ranking loss, which is the main contribution of our work.  ... 
doi:10.1109/access.2019.2948662 fatcat:jf4urfqezjc75ip5w6glkyboge

Latent Network Summarization: Bridging Network Embedding and Summarization [article]

Di Jin, Ryan Rossi, Danai Koutra, Eunyee Koh, Sungchul Kim, Anup Rao
2019 arXiv   pre-print
We propose Multi-LENS, an inductive multi-level latent network summarization approach that leverages a set of relational operators and relational functions (compositions of operators) to capture the structure  ...  Motivated by the computational and storage challenges that dense embeddings pose, we introduce the problem of latent network summarization that aims to learn a compact, latent representation of the graph  ...  To efficiently solve the proposed latent network summarization problem, we propose Multi-LENS, a multi-level inductive approach based on graph function compositions.  ... 
arXiv:1811.04461v2 fatcat:sp6rmuslwvcvxlyuhbbi2oy3s4

Trustworthy Graph Neural Networks: Aspects, Methods and Trends [article]

He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, Jian Pei
2022 arXiv   pre-print
Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering  ...  Finally, we present a thorough overview of trending directions for facilitating the research and industrialisation of trustworthy GNNs.  ...  [2] review several different GNN architectures, including recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks.  ... 
arXiv:2205.07424v1 fatcat:f3iul7soqvgzbgaeqw7nhypbju

Modeling polypharmacy side effects with graph convolutional networks [article]

Marinka Zitnik, Monica Agrawal, Jure Leskovec
2018 bioRxiv   pre-print
Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks.  ...  Here, we present Decagon, an approach for modeling polypharmacy side effects.  ...  Neural networks on graphs.  ... 
doi:10.1101/258814 fatcat:lrnwzjodhzbwbjm752wqdbs2la

Modeling polypharmacy side effects with graph convolutional networks

Marinka Zitnik, Monica Agrawal, Jure Leskovec
2018 Bioinformatics  
Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks.  ...  Here, we present Decagon, an approach for modeling polypharmacy side effects.  ...  Neural networks on graphs.  ... 
doi:10.1093/bioinformatics/bty294 pmid:29949996 pmcid:PMC6022705 fatcat:lbn6h5oycjck3ctgw4xffkt5fu
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