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Embeddings have become a pivotal means to represent complex, multi-faceted information about entities, concepts, and relationships in a condensed and useful format.
A neural probabilistic language model
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Probabilistic matrix factorization
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A unified architecture for natural language processing: Deep neural networks with multitask learning
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Collaborative filtering for implicit feedback datasets
Yifan Hu, Yehuda Koren, and Chris Volinsky · 2008
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Factorization machines
Steffen Rendle · 2010
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Advances in collaborative filtering
Yehuda Koren, Steffen Rendle, and Robert Bell · 2011
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio · 2015
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The movielens datasets: History and context
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Deep unordered composition rivals syntactic methods for text classification
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Integrating and evaluating neural word embeddings in information retrieval
Guido Zuccon, Bevan Koopman, Peter Bruza, and Leif Azzopardi · 2015
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres · 2018
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Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Sampling-bias-corrected neural modeling for large corpus item recommendations
Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, and Ed Chi · 2019
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C Lawrence Zitnick, Jerry Ma, et al · 2021
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Multimodal few-shot learning with frozen language models
Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill · 2021
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Discovering personalized semantics for soft attributes in recommender systems using concept activation vectors
Christina Göpfert, Yinlam Chow, Chih-wei Hsu, Ivan Vendrov, Tyler Lu, Deepak Ramachandran, and Craig Boutilier · 2022
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Ptr: Prompt tuning with rules for text classification
Xu Han, Weilin Zhao, Ning Ding, Zhiyuan Liu, and Maosong Sun · 2022
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Sentence-T5: Scalable sentence encoders from pre-trained Text-to-Text models
Jianmo Ni, Gustavo Hernández Ábrego, Noah Constant, Ji Ma, Keith B. Hall, Daniel Cer, and Yinfei Yang · 2022
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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The impossibility of low-rank representations for triangle-rich complex networks
C Seshadhri, Aneesh Sharma, Andrew Stolman, and Ashish Goel · 2020
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Mixed negative sampling for learning two-tower neural networks in recommendations
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On interpretation and measurement of soft attributes for recommendation
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Unifying vision-and-language tasks via text generation
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Large dual encoders are generalizable retrievers
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Scaling autoregressive models for content-rich text-to-image generation
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Language models can explain neurons in language models
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