Fetching the paper…
Reading the bibliography…
In real-world scenarios, texts in a graph are often linked by multiple semantic relations (e.g., papers in an academic graph are referenced by other publications, written by the same author, or published in the same venue), where text documents and their relations form a multiplex text-attributed graph.
Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 1907
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N. and Gurevych, I · 1908
Earlier work this paper cites.
Distributional structure
Harris, Z. S · 1954
Earlier work this paper cites.
Visualizing data using t-sne
Van der Maaten, L. and Hinton, G · 2008
Earlier work this paper cites.
Pathsim: Meta path-based top-k similarity search in heterogeneous information networks
Sun, Y., Han, J., Yan, X., Yu, P. S., and Wu, T · 2011
Earlier work this paper cites.
Multidimensional networks: foundations of structural analysis
Berlingerio, M., Coscia, M., Giannotti, F., Monreale, A., and Pedreschi, D · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J · 2013
Earlier work this paper cites.
Distributed representations of sentences and documents
Le, Q. and Mikolov, T · 2014
Earlier work this paper cites.
An overview of microsoft academic service (mas) and applications
Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B.-J., and Wang, K · 2015
Earlier work this paper cites.
Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
He, R. and McAuley, J · 2016
Earlier work this paper cites.
metapath2vec: Scalable representation learning for heterogeneous networks
Dong, Y., Chawla, N. V., and Swami, A · 2017
Earlier work this paper cites.
Representation learning on graphs: Methods and applications
Hamilton, W. L., Ying, R., and Leskovec, J · 2017
Earlier work this paper cites.
An attention-based collaboration framework for multi-view network representation learning
Qu, M., Tang, J., Shang, J., Ren, X., Zhang, M., and Han, J · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Multi-dimensional network embedding with hierarchical structure
Ma, Y., Ren, Z., Jiang, Z., Tang, J., and Yin, D · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
Cited alongside, same era.
mvn2vec: Preservation and collaboration in multi-view network embedding
Shi, Y., Han, F., He, X., He, X., Yang, C., Luo, J., and Han, J · 2018
Cited alongside, same era.
Scalable multiplex network embedding
Zhang, H., Qiu, L., Yi, L., and Song, Y · 2018
Cited alongside, same era.
Scibert: A pretrained language model for scientific text
Beltagy, I., Lo, K., and Cohan, A · 2019
Cited alongside, same era.
Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2020
Later among the works it cites.
Dense passage retrieval for open-domain question answering
Karpukhin, V., Oğuz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., and Yih, W.-t · 2020
Later among the works it cites.
Unsupervised attributed multiplex network embedding
Park, C., Kim, D., Han, J., and Yu, H · 2020
Later among the works it cites.
Mpnet: Masked and permuted pre-training for language understanding
Song, K., Tan, X., Qin, T., Lu, J., and Liu, T.-Y · 2020
Later among the works it cites.
Persistent anti-muslim bias in large language models
Abid, A., Farooqi, M., and Zou, J · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Cited alongside, same era.
Heterogeneous graph attention network
Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., and Yu, P. S · 2019
Cited alongside, same era.
Heterogeneous graph neural network
Zhang, C., Song, D., Huang, C., Swami, A., and Chawla, N. V · 2019
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
Electra: Pre-training text encoders as discriminators rather than generators
Clark, K., Luong, M.-T., Le, Q. V., and Manning, C. D · 2020
Cited alongside, same era.
Specter: Document-level representation learning using citation-informed transformers
Cohan, A., Feldman, S., Beltagy, I., Downey, D., and Weld, D. S · 2020
Cited alongside, same era.
Heterogeneous network representation learning
Dong, Y., Hu, Z., Wang, K., Sun, Y., and Tang, J · 2020
Cited alongside, same era.
Hdmi: High-order deep multiplex infomax
Jing, B., Park, C., and Tong, H · 2021
Later among the works it cites.
The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
Later among the works it cites.
Towards understanding and mitigating social biases in language models
Liang, P. P., Wu, C., Morency, L.-P., and Salakhutdinov, R · 2021
Later among the works it cites.
Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., and Tang, J · 2021
Later among the works it cites.
Learning how to ask: Querying lms with mixtures of soft prompts
Qin, G. and Eisner, J · 2021
Later among the works it cites.
Graphformers: Gnn-nested transformers for representation learning on textual graph
Yang, J., Liu, Z., Xiao, S., Li, C., Lian, D., Agrawal, S., Singh, A., Sun, G., and Xie, X · 2021
Later among the works it cites.
Neighborhood contrastive learning for scientific document representations with citation embeddings
Ostendorff, M., Rethmeier, N., Augenstein, I., Gipp, B., and Rehm, G · 2022
Later among the works it cites.
Linkbert: Pretraining language models with document links
Yasunaga, M., Leskovec, J., and Liang, P · 2022
Later among the works it cites.
The effect of metadata on scientific literature tagging: A cross-field cross-model study
Zhang, Y., Jin, B., Zhu, Q., Meng, Y., and Han, J · 2023
Closest in time.