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Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space.
Sentence-bert: Sentence embeddings using siamese bert-networks
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Visualization of search results: a comparative evaluation of text, 2d, and 3d interfaces
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Prototypical contrastive learning of unsupervised representations
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Declutr: Deep contrastive learning for unsupervised textual representations
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Demystifying contrastive self-supervised learning: Invariances, augmentations and dataset biases
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Learning to classify short and sparse text & web with hidden topics from large-scale data collections
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Learning a parametric embedding by preserving local structure
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Representation learning with contrastive predictive coding
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Deep contextualized word representations
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Improving language understanding by generative pre-training
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin. 2018 · 2018
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Learning representations by maximizing mutual information across views
Philip Bachman, R Devon Hjelm, and William Buchwalter. 2019 · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
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A self-training approach for short text clustering
Amir Hadifar, Lucas Sterckx, Thomas Demeester, and Chris Develder. 2019 · 2019
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Nlp augmentation
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019a · 2019
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Generating natural language adversarial examples through probability weighted word saliency
Shuhuai Ren, Yihe Deng, Kun He, and Wanxiang Che. 2019 · 2019
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Mixture models for diverse machine translation: Tricks of the trade
Tianxiao Shen, Myle Ott, Michael Auli, and Marc’Aurelio Ranzato. 2019 · 2019
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020
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Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp
John X. Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi. 2020 · 2020
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Coco-lm: Correcting and contrasting text sequences for language model pretraining
Yu Meng, Chenyan Xiong, Payal Bajaj, Saurabh Tiwary, Paul Bennett, Jiawei Han, and Xia Song. 2021 · 2021
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