Augment and Reduce: Stochastic Inference for Large Categorical Distributions. 02/12/2018 ∙ by Francisco J. R. Ruiz, et al. ∙ University of Cambridge ∙ Columbia University ∙ 0 ∙ share

For example, to reproduce the results on the EURLex-4K dataset: omikuji train eurlex_train.txt --model_path ./model omikuji test ./model eurlex_test.txt --out_path predictions.txt Python Binding. A simple Python binding is also available for training and prediction. It can be install via pip: pip install omikuji

It … EURLex-4K 15,539 3,809 3,993 25.73 5.31 Wiki10-31k 14,146 6,616 30,938 8.52 18.64 AmazonCat-13K 1,186,239 306,782 13,330 448.57 5.04 conducted on the impact of the operations. Finally, we describe the XMCNAS discovered architecture, and the results we achieve with this architecture. 3.1 Datasets and evaluation metrics The objective in extreme multi-label classification is to learn feature architectures and classifiers that can automatically tag a data point with the most relevant subset of labels from an extremely large label set. EURLex-4K AmazonCat-13K N train N test covariates classes 60 ,000 10 000 784 10 4,880 2,413 1,836 148 25,968 6,492 784 1,623 15,539 3,809 5,000 896 1,186,239 306,782 203,882 2,919 minibatch (obs.) minibatch (classes) iterations 500 1 35 000 488 20 5,000 541 50 45,000 279 50 100,000 1,987 60 5,970 Table 2.Average time per epoch for each method For example, to reproduce the results on the EURLex-4K dataset: omikuji train eurlex_train.txt --model_path ./model omikuji test ./model eurlex_test.txt --out_path predictions.txt Python Binding. A simple Python binding is also available for training and prediction. It … EURLex-4K [N = 15K,D = 5K,L = 4K] Algorithm Revealed Label Percentages 20% 40% 60% 80% PSP1 PSP3 PSP5 PSP1 PSP3 PSP5 PSP1 PSP3 PSP5 PSP1 PSP3 PSP5 WRMF 8.87 9.80 11.05 12.44 13.69 16.58 13.59 15.50 19.77 13.21 18.10 22.85 SVD++ 0.17 0.31 0.41 0.17 0.29 0.51 0.18 0.34 0.61 0.14 0.29 0.60 BPR 1.17 1.23 1.13 1.18 0.89 1.01 1.06 0.72 0.86 1.09 1.65 For datasets with small labels like Eurlex-4k, Amazoncat-13k and Wiki10-31k, each label clusters contain only one label and we can get each label scores in label recalling part. For ensemble, we use three different transformer models for Eurlex-4K, Amazoncat-13K and Wiki10-31K, and use three different label clusters with BERT Devlin et al.

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Categorical distributions are fundamental to many areas of machine learning. Examples include classification (Gupta et al., 2014), language models (Bengio et al., 2006), recommendation systems (Marlin & Zemel, 2004), reinforcement learning (Sutton & Barto, 1998), and neural attention models (Bahdanau et al., 2015).They also play an important role in discrete choice models (McFadden, 1978). 2018-12-01 7 in Parabel for the benchmark EURLex-4K dataset, and 3 versus 13 for WikiLSHTC-325K dataset 1. The shallow architecture reduces the adverse impact of er-ror propagation during prediction. Secondly and more signi cantly, allowing large number of partitions with … Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification Mohammadreza Qaraei1, Sujay Khandagale2 and Rohit Babbar1 1- … EURLex-4K 15539 5000 3993 3809 236.8 5.31 AmazonCat-13K 1186239 203882 13330 306782 71.2 5.04 Wiki10-31K 14146 101938 30938 6616 673.4 18.64 Delicious-200K 196606 782585 205443 100095 301.2 75.54 WikiLSHTC-325K 1778351 1617899 325056 587084 42.1 3.19 Wikipedia-500K 1813391 2381304 501070 783743 385.3 4.77 Amazon-670K 490449 135909 670091 153025 Eurlex-4K, AmazonCat-13K or the Wikipedia-500K, all of them available in the Extreme Classi cation Repository [15]. More recently, a newer version of X-BERT has been released, renamed X-Transformer2[16].

labels for EUR-Lex dataset. Line 4 is for smaller datasets, MediaMill, Bibtex, and EUR-Lex and it was fixed to 0.1 for all bigger datasets.

As shown in this Table, on all datasets except Delicious-200K and EURLex-4K our method matches or outperforms all previous work in terms of precision@k3. Even on the Delicious-200K dataset, our method\u2019s performance is close to that of the state-of-the-art, which belongs to another embedding-based method SLEEC [6].

Especificaban una resolución de 2048 x 1080, denominada 2k (4096 x 2160, o 4k, para las pantallas de más de 15 m) y JPEG 2000 como  eur-lex.europa.eu. L'article 7 de la directive 93/104/CE du Conseil, du 23 novembre 1993, concernant certains aspects de l'aménagement du temps de travail,  eur-lex.europa.eu.

Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II [1st ed. 2019] 978-3-030-30483-6, 978-3-030-30484-3

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, 2015 ), and AmazonCat-13K ( McAuley & Leskovec , 2013 ). 5 T able 1 gives information We will use Eurlex-4K as an example. In the ./datasets/Eurlex-4K folder, we assume the following files are provided: X.trn.npz: the instance TF-IDF feature matrix for the train set. The data type is scipy.sparse.csr_matrix of size (N_trn, D_tfidf), where N_trn is the number of train instances and D_tfidf is the number of features. · Analyzed extreme multi-label classification (EXML) on EURLex-4K dataset using state-of-the-art algorithms. Responsible for literature review on EXML problems, specifically for embedding methods Paper Reading:《Taming Pretrained Transformers for Extreme Multi-label Text Classification 》@time：2020-11-30github codearxiv paperSIGKDD 2020 Applied Data Track1. 主要工作针对极端多标签文本分类（Extreme Multi-label Classification, XMC）问题，即给定输入文本，则从大型标签集中返回最相关 … 为了验证本文提出的Deep AE-MF和Deep AE-MF+neg方法的性能，选取了6个多标签数据集进行实验测试，分别为enron、ohsumed、movieLens、Delicious、EURLex-4K和TJ，其中前5个是英文类型的多标签数据集，最后一个则是中文类型数据集。实验结果如表1到表5所示。 2100 Machine Learning (2020) 109:2099–2119 1 3 2015),annotatingweb-scaleencyclopedia(Partalasetal.2015),andimage-classi-cation(Krizhevskyetal.2012;Dengetal.2010).Ithasbeendemonstratedthat,the 现有的一些多标签分类算法，因多标签数据含有高维的特征或标签信息而变得不可行.为了解决这一问题，提出基于去噪自编码器和矩阵分解的联合嵌入多标签分类算法Deep AE-MF.该算法包括两部分：特征嵌入部分使用去噪自编码器对特征空间学习得到非线性表示，标签嵌入部分则是利用矩阵分解直接 이 논문은 XMC를 BERT를 이용하여 푸는 모델에 대한 논문이다.
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, 2015 ), and AmazonCat-13K ( McAuley & Leskovec , 2013 ). 5 T able 1 gives information Augment and Reduce: Stochastic Inference for Large Categorical Distributions. 02/12/2018 ∙ by Francisco J. R. Ruiz, et al. ∙ University of Cambridge ∙ Columbia University ∙ 0 ∙ share EURLex-4K 15539 5000 3993 3809 236.8 5.31 AmazonCat-13K 1186239 203882 13330 306782 71.2 5.04 Wiki10-31K 14146 101938 30938 6616 673.4 18.64 Delicious-200K 196606 782585 205443 100095 301.2 75.54 WikiLSHTC-325K 1778351 1617899 325056 587084 42.1 3.19 Wikipedia-500K 1813391 2381304 501070 783743 385.3 4.77 Amazon-670K 490449 135909 670091 153025 Regression Oracle. As in (Foster et al.,2018;Simchi-Levi and Xu,2020), we will rely on the availability of an optimization oracle regression-oracle for the class Fthat can perform least- As shown in this Table, on all datasets except Delicious-200K and EURLex-4K our method matches or outperforms all previous work in terms of precision@k3.

To run Slice on the EURLex-4K dataset, execute "bash sample_run.sh" (Linux) or "sample_run" (Windows) in the Slice folder.
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· Analyzed extreme multi-label classification (EXML) on EURLex-4K dataset using state-of-the-art algorithms. Responsible for literature review on EXML problems, specifically for embedding methods

Close3  eur-lex.europa.eu.

Introduction. The EUR-Lex text collection is a collection of documents about European Union law. It contains many different types of documents, including treaties, legislation, case-law and legislative proposals, which are indexed according to several orthogonal categorization schemes to allow for multiple search facilities.

precision at 3 is 69.48. precision at 5 is 57.94. ndcg at 1 is 82.51. ndcg at 3 is 72.89. ndcg Introduction. The EUR-Lex text collection is a collection of documents about European Union law.

Line 4 is for smaller datasets, MediaMill, Bibtex, and EUR-Lex and it was fixed to 0.1 for all bigger datasets. EURLex-4K. N@1. eur-lex.europa.eu. (b) sodium benzoate as a product market separate from sorbates while leaving open whether potassium benzoate and calcium benzoate are  podrán autorizar el envasado al vacío de los cortes de los códigos INT 12, 13, 14 , 15, 16, 17 y 19, en vez del envoltorio individual contemplado en el punto 1. eur-   holdings in the capital of the Banca d'Italia, the choice of [] placing them in the foundation was not even available.