Fetching the paper…
Reading the bibliography…
Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities.
R. C. T. Lee, Y. H. Chin, and S. C. Chang, “Application of principal component analysis to multikey searching,” IEEE Trans. Software Eng
1976
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” nature
1986
Earlier work this paper cites.
J. L. Elman, “Finding structure in time,” Cognitive science
1990
Earlier work this paper cites.
S. Robinson and R. Bennett, “A typology of deviant workplace behaviors: A multidimensional scaling study,” Academy of Management Journal
1995
Earlier work this paper cites.
R. Caruana, “Multitask learning,” Machine learning
1997
Earlier work this paper cites.
M. I. Jordan, “Serial order: A parallel distributed processing approach,” in Advances in psychology
1997
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation
1997
Earlier work this paper cites.
D. Jurafsky and E. Shriberg, “Switchboard swbd-damsl shallow-discourse-function annotation coders manual,” 1997
1997
Earlier work this paper cites.
R. E. Schapire and Y. Singer, “Improved boosting algorithms using confidence-rated predictions,” Mach. Learn
1999
Earlier work this paper cites.
S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science
2000
Earlier work this paper cites.
G. G. Chowdhury, “Natural language processing,” Annual review of information science and technology
2003
Earlier work this paper cites.
Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin, “A neural probabilistic language model,” J. Mach. Learn. Res
2003
Earlier work this paper cites.
M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation,” Neural Comput
2003
Earlier work this paper cites.
C.-Y. Lin and E. Hovy, “Automatic evaluation of summaries using n-gram co-occurrence statistics,” in HLT-NAACL
2003
Earlier work this paper cites.
J. Ye, R. Janardan, and Q. Li, “Two-dimensional linear discriminant analysis,” in Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, NIPS 2004, December 13-18, 2004, Vancouver, British Columbia, Canada]
2004
Earlier work this paper cites.
B. Dolan, C. Quirk, and C. Brockett, “Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources,” in COLING
2004
Earlier work this paper cites.
“AG Corpus.” http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html , 2004
2004
Earlier work this paper cites.
J. Ang, Y. Liu, and E. Shriberg, “Automatic dialog act segmentation and classification in multiparty meetings,” in 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP ’05, Philadelphia, Pennsylvania, USA, March 18-23, 2005
2005
Earlier work this paper cites.
J. Wiebe, T. Wilson, and C. Cardie, “Annotating expressions of opinions and emotions in language,” Language Resources and Evaluation
2005
Earlier work this paper cites.
“MPQA Corpus.” http://www.cs.pitt.edu/mpqa/ , 2005
2005
Earlier work this paper cites.
W. B. Dolan and C. Brockett, “Automatically constructing a corpus of sentential paraphrases,” in IWP
2005
Earlier work this paper cites.
O. Samko, A. D. Marshall, and P. L. Rosin, “Selection of the optimal parameter value for the isomap algorithm,” Pattern Recognit. Lett
2006
Earlier work this paper cites.
“20NG Corpus.” http://ana.cachopo.org/datasets-for-single-label-text-categorization , 2007
2007
Earlier work this paper cites.
“Reuters Corpus.” https://www.cs.umb.edu/~smimarog/textmining/datasets/ , 2007
2007
Earlier work this paper cites.
A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, and J. Schmidhuber, “A novel connectionist system for unconstrained handwriting recognition,” IEEE transactions on pattern analysis and machine intelligence
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition
2009
Earlier work this paper cites.
D. Erhan, P.-A. Manzagol, Y. Bengio, S. Bengio, and P. Vincent, “The difficulty of training deep architectures and the effect of unsupervised pre-training,” in Artificial Intelligence and Statistics
2009
Earlier work this paper cites.
I. Hendrickx, S. N. Kim, Z. Kozareva, P. Nakov, D. Ó. Séaghdha, S. Padó, M. Pennacchiotti, L. Romano, and S. Szpakowicz, “Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals,” in Proc. NAACL, 2009
2009
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International journal of computer vision
2010
Earlier work this paper cites.
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba, “Sun database: Large-scale scene recognition from abbey to zoo,” in 2010 IEEE computer society conference on computer vision and pattern recognition
2010
Earlier work this paper cites.
R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. P. Kuksa, “Natural language processing (almost) from scratch,” J. Mach. Learn. Res
2011
Earlier work this paper cites.
N. Shervashidze, P. Schweitzer, E. J. van Leeuwen, K. Mehlhorn, and K. M. Borgwardt, “Weisfeiler-lehman graph kernels,” J. Mach. Learn. Res
2011
Earlier work this paper cites.
A. Coates, A. Ng, and H. Lee, “An analysis of single-layer networks in unsupervised feature learning,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics
2011
Earlier work this paper cites.
H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre, “HMDB: a large video database for human motion recognition,” in ICCV
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems
2012
Earlier work this paper cites.
M. Sundermeyer, R. Schlüter, and H. Ney, “Lstm neural networks for language modeling,” in INTERSPEECH
2012
Earlier work this paper cites.
H. Levesque, E. Davis, and L. Morgenstern, “The winograd schema challenge,” in Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning
2012
Earlier work this paper cites.
T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in Proc. ICLR, 2013
2013
Earlier work this paper cites.
T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” arXiv
2013
Earlier work this paper cites.
M. Lin, Q. Chen, and S. Yan, “Network in network,” arXiv
2013
Earlier work this paper cites.
A. Graves, “Generating sequences with recurrent neural networks,” arXiv
2013
Earlier work this paper cites.
“SST Corpus.” http://nlp.stanford.edu/sentiment , 2013
2013
Earlier work this paper cites.
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi, “Fine-grained visual classification of aircraft,” tech. rep., 2013
2013
Earlier work this paper cites.
A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox, “Discriminative unsupervised feature learning with convolutional neural networks,” Advances in neural information processing systems
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv
2014
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected crfs,” arXiv
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” arXiv
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” arXiv
2014
Earlier work this paper cites.
M. Marelli, L. Bentivogli, M. Baroni, R. Bernardi, S. Menini, and R. Zamparelli, “Semeval-2014 task 1: Evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment,” in SemEval@COLING
2014
Earlier work this paper cites.
L. Bossard, M. Guillaumin, and L. Van Gool, “Food-101–mining discriminative components with random forests,” in European conference on computer vision
2014
Earlier work this paper cites.
T. Berg, J. Liu, S. Woo Lee, M. L. Alexander, D. W. Jacobs, and P. N. Belhumeur, “Birdsnap: Large-scale fine-grained visual categorization of birds,” in CVPR
2014
Earlier work this paper cites.
R. Sennrich, B. Haddow, and A. Birch, “Neural machine translation of rare words with subword units,” arXiv
2015
Earlier work this paper cites.
A. M. Dai and Q. V. Le, “Semi-supervised sequence learning,” arXiv
2015
Earlier work this paper cites.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” arXiv
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” arXiv
2015
Earlier work this paper cites.
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes challenge: A retrospective,” International journal of computer vision
2015
Earlier work this paper cites.
E. Denton, S. Chintala, A. Szlam, and R. Fergus, “Deep generative image models using a laplacian pyramid of adversarial networks,” arXiv
2015
Earlier work this paper cites.
M. Sugiyama and K. M. Borgwardt, “Halting in random walk kernels,” in NIPS
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
X. Zhang, J. J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” in NIPS
2015
Earlier work this paper cites.
J. Lehmann, R. Isele, M. Jakob, A. Jentzsch, D. Kontokostas, P. N. Mendes, S. Hellmann, M. Morsey, P. van Kleef, S. Auer, and C. Bizer, “Dbpedia - A large-scale, multilingual knowledge base extracted from wikipedia,” Semantic Web
2015
Earlier work this paper cites.
“Ohsumed Corpus.” http://davis.wpi.edu/xmdv/datasets/ohsumed.html , 2015
2015
Earlier work this paper cites.
M. Cordts, M. Omran, S. Ramos, T. Scharwächter, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset,” in CVPR Workshop on The Future of Datasets in Vision
2015
Earlier work this paper cites.
N. Srivastava, E. Mansimov, and R. Salakhudinov, “Unsupervised learning of video representations using lstms,” in International conference on machine learning
2015
Earlier work this paper cites.
J. Donahue, P. Krähenbühl, and T. Darrell, “Adversarial feature learning,” arXiv
2016
Earlier work this paper cites.
Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey, et al
2016
Earlier work this paper cites.
P. Liu, X. Qiu, and X. Huang, “Recurrent neural network for text classification with multi-task learning,” arXiv
2016
Earlier work this paper cites.
A. Dosovitskiy, P. Fischer, J. T. Springenberg, M. Riedmiller, and T. Brox, “Discriminative unsupervised feature learning with exemplar convolutional neural networks,” TPAMI
2016
Earlier work this paper cites.
R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in ECCV
2016
Earlier work this paper cites.
V. Dumoulin, I. Belghazi, B. Poole, O. Mastropietro, A. Lamb, M. Arjovsky, and A. Courville, “Adversarially learned inference,” arXiv
2016
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Variational graph auto-encoders,” CoRR
2016
Earlier work this paper cites.
I. Misra, C. L. Zitnick, and M. Hebert, “Shuffle and learn: unsupervised learning using temporal order verification,” in ECCV
2016
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in European conference on computer vision
2016
Earlier work this paper cites.
C. Li and M. Wand, “Precomputed real-time texture synthesis with markovian generative adversarial networks,” in European conference on computer vision
2016
Earlier work this paper cites.
A. Van Oord, N. Kalchbrenner, and K. Kavukcuoglu, “Pixel recurrent neural networks,” in International Conference on Machine Learning
2016
Earlier work this paper cites.
G. Perarnau, J. Van De Weijer, B. Raducanu, and J. M. Álvarez, “Invertible conditional gans for image editing,” arXiv
2016
Earlier work this paper cites.
C. Vondrick, H. Pirsiavash, and A. Torralba, “Generating videos with scene dynamics,” arXiv
2016
Earlier work this paper cites.
M. Jaderberg, V. Mnih, W. M. Czarnecki, T. Schaul, J. Z. Leibo, D. Silver, and K. Kavukcuoglu, “Reinforcement Learning with Unsupervised Auxiliary Tasks,” Nov. 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Z. Yang, D. Yang, C. Dyer, X. He, A. J. Smola, and E. H. Hovy, “Hierarchical attention networks for document classification,” in NAACL-HLT
2016
Earlier work this paper cites.
C. N. dos Santos, M. Tan, B. Xiang, and B. Zhou, “Attentive pooling networks,” CoRR
2016
Earlier work this paper cites.
J. Y. Lee and F. Dernoncourt, “Sequential short-text classification with recurrent and convolutional neural networks,” in NAACL-HLT
2016
Earlier work this paper cites.
S. Kim, L. F. D’Haro, R. E. Banchs, J. D. Williams, and M. Henderson, “The fourth dialog state tracking challenge,” in Dialogues with Social Robots - Enablements, Analyses, and Evaluation, Seventh International Workshop on Spoken Dialogue Systems, IWSDS 2016, Saariselkä, Finland, January 13-16, 2016
2016
Earlier work this paper cites.
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang, “Squad: 100,000+ questions for machine comprehension of text,” arXiv
2016
Earlier work this paper cites.
J. Xiao, K. A. Ehinger, J. Hays, A. Torralba, and A. Oliva, “Sun database: Exploring a large collection of scene categories,” International Journal of Computer Vision
2016
Earlier work this paper cites.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Earlier work this paper cites.
B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li, “Yfcc100m: The new data in multimedia research,” Communications of the ACM
2016
Earlier work this paper cites.
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei, “Deep reinforcement learning from human preferences,” Advances in neural information processing systems
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” arXiv
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, “Enriching word vectors with subword information,” TACL
2017
Earlier work this paper cites.
B. McCann, J. Bradbury, C. Xiong, and R. Socher, “Learned in translation: Contextualized word vectors,” arXiv
2017
Earlier work this paper cites.
S. J. Rennie, E. Marcheret, Y. Mroueh, J. Ross, and V. Goel, “Self-critical sequence training for image captioning,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2017
Earlier work this paper cites.
P. Bojanowski and A. Joulin, “Unsupervised learning by predicting noise,” in ICML
2017
Earlier work this paper cites.
W. L. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in NIPS
2017
Earlier work this paper cites.
D. U. Hui, X. U. Xueke, W. U. Dayong, Y. Liu, Y. U. Zhihua, and X. Cheng, “A sentiment classification method based on sentiment-specific word embedding,” Journal of Chinese Information Processing
2017
Earlier work this paper cites.
Y. Liu, C. Ma, and Y. Zhang, “Hierarchical machine translation model based on deep recursive neural network,” Chin. J. Comput
2017
Earlier work this paper cites.
V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE transactions on pattern analysis and machine intelligence
2017
Earlier work this paper cites.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in CVPR
2017
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE transactions on pattern analysis and machine intelligence
2017
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam, “Rethinking atrous convolution for semantic image segmentation,” arXiv
2017
Earlier work this paper cites.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in CVPR
2017
Earlier work this paper cites.
T. Kim, M. Cha, H. Kim, J. K. Lee, and J. Kim, “Learning to discover cross-domain relations with generative adversarial networks,” in International Conference on Machine Learning
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell, “Curiosity-driven exploration by self-supervised prediction,” in International conference on machine learning
2017
Earlier work this paper cites.
H. Tang, R. Houthooft, D. Foote, A. Stooke, O. Xi Chen, Y. Duan, J. Schulman, F. DeTurck, and P. Abbeel, “# exploration: A study of count-based exploration for deep reinforcement learning,” Advances in neural information processing systems
2017
Earlier work this paper cites.
T. Miyato, A. M. Dai, and I. J. Goodfellow, “Adversarial training methods for semi-supervised text classification,” in ICLR
2017
Earlier work this paper cites.
D. Cer, M. Diab, E. Agirre, I. Lopez-Gazpio, and L. Specia, “Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation,” arXiv
2017
Earlier work this paper cites.
“Reuters Corpus.” https://martin-thoma.com/nlp-reuters , 2017
2017
Earlier work this paper cites.
A. Williams, N. Nangia, and S. R. Bowman, “A broad-coverage challenge corpus for sentence understanding through inference,” arXiv
2017
Earlier work this paper cites.
G. Lai, Q. Xie, H. Liu, Y. Yang, and E. Hovy, “Race: Large-scale reading comprehension dataset from examinations,” arXiv
2017
Earlier work this paper cites.
C. Sun, A. Shrivastava, S. Singh, and A. Gupta, “Revisiting unreasonable effectiveness of data in deep learning era,” in Proceedings of the IEEE international conference on computer vision
2017
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “Deep contextualized word representations,” arXiv
2018
Earlier work this paper cites.
S. Gidaris, P. Singh, and N. Komodakis, “Unsupervised representation learning by predicting image rotations,” arXiv
2018
Earlier work this paper cites.
A. v. d. Oord, Y. Li, and O. Vinyals, “Representation learning with contrastive predictive coding,” arXiv
2018
Earlier work this paper cites.
N. Sayed, B. Brattoli, and B. Ommer, “Cross and learn: Cross-modal self-supervision,” in GCPR
2018
Earlier work this paper cites.
G. Wang, L. Zhang, Z. Yang, and X.-Y. Li, “Socialite: Social activity mining and friend auto-labeling,” in 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC)
2018
Earlier work this paper cites.
M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “Deep contextualized word representations,” in NAACL-HLT
2018
Earlier work this paper cites.
X. Liang, F. Ren, Y. Liu, L. Pan, Y. Hou, Y. Zhang, and L. I. Yan, “N-reader: Machine reading comprehension model based on double layers of self-attention,” Journal of Chinese Information Processing
2018
Cited alongside, same era.
J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” arXiv
2018
Cited alongside, same era.
B. Korbar, D. Tran, and L. Torresani, “Cooperative learning of audio and video models from self-supervised synchronization,” arXiv
2018
Cited alongside, same era.
D. Ha and J. Schmidhuber, “World Models,” Mar. 2018
2018
Cited alongside, same era.
I. Higgins, A. Pal, A. A. Rusu, L. Matthey, C. P. Burgess, A. Pritzel, M. Botvinick, C. Blundell, and A. Lerchner, “DARLA: Improving Zero-Shot Transfer in Reinforcement Learning,” June 2018
2018
Cited alongside, same era.
S. Zhang, Z. Hu, A. Subramonian, and Y. Sun, “Motif-driven contrastive learning of graph representations,” CoRR
2020
Later among the works it cites.
2021
Later among the works it cites.
J. Li, T. Tang, W. X. Zhao, and J.-R. Wen, “Pretrained language models for text generation: A survey,” arXiv
2021
Later among the works it cites.
2021
Later among the works it cites.
B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
E. Reiter, “A structured review of the validity of bleu,” Computational Linguistics
2018
Cited alongside, same era.
T. Shen, T. Zhou, G. Long, J. Jiang, and C. Zhang, “Bi-directional block self-attention for fast and memory-efficient sequence modeling,” in ICLR
2018
Cited alongside, same era.
A. Williams, N. Nangia, and S. R. Bowman, “A broad-coverage challenge corpus for sentence understanding through inference,” in NAACL-HLT
2018
Cited alongside, same era.
A. Katiyar and C. Cardie, “Nested named entity recognition revisited,” in NAACL-HLT
2018
Cited alongside, same era.
Y. Wan, W. Yan, J. Gao, Z. Zhao, J. Wu, and P. S. Yu, “Improved dynamic memory network for dialogue act classification with adversarial training,” in IEEE International Conference on Big Data, Big Data 2018, Seattle, WA, USA, December 10-13, 2018
2018
Cited alongside, same era.
P. Rajpurkar, R. Jia, and P. Liang, “Know what you don’t know: Unanswerable questions for squad,” arXiv
2018
Cited alongside, same era.
W. Hu, B. Liu, J. Gomes, M. Zitnik, P. Liang, V. Pande, and J. Leskovec, “Pre-training graph neural networks,” arXiv
2019
Cited alongside, same era.
2021
Later among the works it cites.
T. Schick and H. Schütze, “Exploiting cloze-questions for few-shot text classification and natural language inference,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
2021
Later among the works it cites.
X. Han, Z. Zhang, N. Ding, Y. Gu, X. Liu, Y. Huo, J. Qiu, L. Zhang, W. Han, M. Huang, et al
2021
Later among the works it cites.
Y. Cui, W. Che, T. Liu, B. Qin, and Z. Yang, “Pre-training with whole word masking for chinese BERT,” T-ASL
2021
Later among the works it cites.
T. Schick and H. Schütze, “Exploiting cloze-questions for few-shot text classification and natural language inference,” in EACL
2021
Later among the works it cites.
X. Wang, T. Gao, Z. Zhu, Z. Zhang, Z. Liu, J. Li, and J. Tang, “KEPLER: A unified model for knowledge embedding and pre-trained language representation,” Trans. Assoc. Comput. Linguistics
2021
Later among the works it cites.
T. Gao, X. Yao, and D. Chen, “Simcse: Simple contrastive learning of sentence embeddings,” CoRR
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
S. Kiegeland and J. Kreutzer, “Revisiting the weaknesses of reinforcement learning for neural machine translation,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
2021
Later among the works it cites.
R. Y. Pang and H. He, “Text generation by learning from demonstrations,” in Proceedings of the international conference on learning representations
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
H. Bao, L. Dong, S. Piao, and F. Wei, “Beit: Bert pre-training of image transformers,” in International Conference on Learning Representations
2021
Later among the works it cites.
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
2021
Later among the works it cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International Conference on Machine Learning
2021
Later among the works it cites.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
2021
Later among the works it cites.
P. Goyal, M. Caron, B. Lefaudeux, M. Xu, P. Wang, V. Pai, M. Singh, V. Liptchinsky, I. Misra, A. Joulin, et al
2021
Later among the works it cites.
J. Li, P. Zhou, C. Xiong, and S. C. H. Hoi, “Prototypical contrastive learning of unsupervised representations,” in ICLR
2021
Later among the works it cites.
X. Chen, S. Xie, and K. He, “An empirical study of training self-supervised vision transformers,” arXiv
2021
Later among the works it cites.
Z. Xie, Y. Lin, Z. Yao, Z. Zhang, Q. Dai, Y. Cao, and H. Hu, “Self-supervised learning with swin transformers,” arXiv
2021
Later among the works it cites.
Z. Li, Z. Chen, F. Yang, W. Li, Y. Zhu, C. Zhao, R. Deng, L. Wu, R. Zhao, M. Tang, et al
2021
Later among the works it cites.
2021
Later among the works it cites.
Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang, “Graph contrastive learning with adaptive augmentation,” in WWW
2021
Later among the works it cites.
C. Mavromatis and G. Karypis, “Graph infoclust: Maximizing coarse-grain mutual information in graphs,” in PAKDD
2021
Later among the works it cites.
Q. Sun, J. Li, H. Peng, J. Wu, Y. Ning, P. S. Yu, and L. He, “SUGAR: subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism,” in WWW
2021
Later among the works it cites.
Y. You, T. Chen, Y. Shen, and Z. Wang, “Graph contrastive learning automated,” CoRR
2021
Later among the works it cites.
M. Xu, H. Wang, B. Ni, H. Guo, and J. Tang, “Self-supervised graph-level representation learning with local and global structure,” CoRR
2021
Later among the works it cites.
J. Cao, X. Lin, S. Guo, L. Liu, T. Liu, and B. Wang, “Bipartite graph embedding via mutual information maximization,” in WSDM
2021
Later among the works it cites.
X. Wang, N. Liu, H. Han, and C. Shi, “Self-supervised heterogeneous graph neural network with co-contrastive learning,” KDD
2021
Later among the works it cites.
D. Kim and A. Oh, “How to find your friendly neighborhood: Graph attention design with self-supervision,” in ICLR
2021
Later among the works it cites.
M. Sun, J. Xing, H. Wang, B. Chen, and J. Zhou, “Mocl: Contrastive learning on molecular graphs with multi-level domain knowledge,” CoRR
2021
Later among the works it cites.
Y.-A. Chung, C. Zhu, and M. Zeng, “SPLAT: Speech-language joint pre-training for spoken language understanding,” in ACL
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al
2021
Later among the works it cites.
2021
Later among the works it cites.
N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. B. Brown, D. Song, U. Erlingsson, et al
2021
Later among the works it cites.
S. Abnar, M. Dehghani, B. Neyshabur, and H. Sedghi, “Exploring the limits of large scale pre-training,” arXiv
2021
Later among the works it cites.
Q. Chen, Y. Wang, T. Yang, X. Zhang, J. Cheng, and J. Sun, “You only look one-level feature,” arXiv
2021
Later among the works it cites.
B. Graham, A. El-Nouby, H. Touvron, P. Stock, A. Joulin, H. Jégou, and M. Douze, “Levit: a vision transformer in convnet’s clothing for faster inference,” arXiv
2021
Later among the works it cites.
D. Zhou, B. Kang, X. Jin, L. Yang, X. Lian, Z. Jiang, Q. Hou, and J. Feng, “Deepvit: Towards deeper vision transformer,” arXiv
2021
Later among the works it cites.
W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,” arXiv
2021
Later among the works it cites.
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis, and D. Manocha, “M3detr: Multi-representation, multi-scale, mutual-relation 3d object detection with transformers,” arXiv
2021
Later among the works it cites.
J. M. J. Valanarasu, P. Oza, I. Hacihaliloglu, and V. M. Patel, “Medical transformer: Gated axial-attention for medical image segmentation,” arXiv
2021
Later among the works it cites.
C. Tosh, A. Krishnamurthy, and D. Hsu, “Contrastive learning, multi-view redundancy, and linear models,” in Algorithmic Learning Theory
2021
Later among the works it cites.
B. Han, C. Zheng, H. Chan, K. Paster, M. Zhang, and J. Ba, “Learning domain invariant representations in goal-conditioned block mdps,” Advances in Neural Information Processing Systems
2021
Later among the works it cites.
2021
Later among the works it cites.
M. Schwarzer, N. Rajkumar, M. Noukhovitch, A. Anand, L. Charlin, R. D. Hjelm, P. Bachman, and A. C. Courville, “Pretraining representations for data-efficient reinforcement learning,” Advances in Neural Information Processing Systems
2021
Later among the works it cites.
R. Shah and V. Kumar, “RRL: Resnet as representation for Reinforcement Learning,” Nov. 2021
2021
Later among the works it cites.
M. Schwarzer, A. Anand, R. Goel, R. D. Hjelm, A. Courville, and P. Bachman, “Data-Efficient Reinforcement Learning with Self-Predictive Representations,” May 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
Q. Liu, L. Yu, L. Rimell, and P. Blunsom, “Pretraining the noisy channel model for task-oriented dialogue,” CoRR
2021
Later among the works it cites.
2021
Later among the works it cites.
W. Jin, X. Liu, X. Zhao, Y. Ma, N. Shah, and J. Tang, “Automated self-supervised learning for graphs,” CoRR
2021
Later among the works it cites.
M. Jin, Y. Zheng, Y. Li, C. Gong, C. Zhou, and S. Pan, “Multi-scale contrastive siamese networks for self-supervised graph representation learning,” CoRR
2021
Later among the works it cites.
S. Lin, P. Zhou, Z.-Y. Hu, S. Wang, R. Zhao, Y. Zheng, L. Lin, E. Xing, and X. Liang, “Prototypical graph contrastive learning,” 2021
2021
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. H. Chi, Q. V. Le, D. Zhou, et al
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
A. Singh, R. Hu, V. Goswami, G. Couairon, W. Galuba, M. Rohrbach, and D. Kiela, “Flava: A foundational language and vision alignment model,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2022
Later among the works it cites.
2022
Later among the works it cites.
M.-H. Guo, T.-X. Xu, J.-J. Liu, Z.-N. Liu, P.-T. Jiang, T.-J. Mu, S.-H. Zhang, R. R. Martin, M.-M. Cheng, and S.-M. Hu, “Attention mechanisms in computer vision: A survey,” Computational Visual Media
2022
Later among the works it cites.
2022
Later among the works it cites.
Q. Li, H. Peng, J. Li, C. Xia, R. Yang, L. Sun, P. S. Yu, and L. He, “A survey on text classification: From traditional to deep learning,” ACM Transactions on Intelligent Systems and Technology (TIST)
2022
Later among the works it cites.
2022
Later among the works it cites.
N. Du, Y. Huang, A. M. Dai, S. Tong, D. Lepikhin, Y. Xu, M. Krikun, Y. Zhou, A. W. Yu, O. Firat, et al
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Wei, M. Bosma, V. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le, “Finetuned language models are zero-shot learners,” in International Conference on Learning Representations
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Wang, S. Mishra, P. Alipoormolabashi, Y. Kordi, A. Mirzaei, A. Naik, A. Ashok, A. S. Dhanasekaran, A. Arunkumar, D. Stap, et al
2022
Later among the works it cites.
S. Mishra, D. Khashabi, C. Baral, and H. Hajishirzi, “Cross-task generalization via natural language crowdsourcing instructions,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
V. Uc-Cetina, N. Navarro-Guerrero, A. Martin-Gonzalez, C. Weber, and S. Wermter, “Survey on reinforcement learning for language processing,” Artificial Intelligence Review
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” in Advances in Neural Information Processing Systems
2022
Later among the works it cites.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2022
Later among the works it cites.
Z. Xie, Z. Zhang, Y. Cao, Y. Lin, J. Bao, Z. Yao, Q. Dai, and H. Hu, “Simmim: A simple framework for masked image modeling,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
H. Bao, L. Dong, S. Piao, and F. Wei, “BEit: BERT pre-training of image transformers,” in International Conference on Learning Representations
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
Z. Hou, X. Liu, Y. Dong, C. Wang, J. Tang, et al
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
A. Zlotchevski, D. Drain, A. Svyatkovskiy, C. B. Clement, N. Sundaresan, and M. Tufano, “Exploring and evaluating personalized models for code generation,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
X. Zhu, J. Zhu, H. Li, X. Wu, H. Li, X. Wang, and J. Dai, “Uni-perceiver: Pre-training unified architecture for generic perception for zero-shot and few-shot tasks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2022
Later among the works it cites.
2022
Later among the works it cites.
Z. Zhang, Y. Li, J. Wang, B. Liu, D. Li, Y. Guo, X. Chen, and Y. Liu, “Remos: reducing defect inheritance in transfer learning via relevant model slicing,” in Proceedings of the 44th International Conference on Software Engineering
2022
Later among the works it cites.
2022
Later among the works it cites.
F. Deng, I. Jang, and S. Ahn, “Dreamerpro: Reconstruction-free model-based reinforcement learning with prototypical representations,” in International Conference on Machine Learning
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
Z. Zhang, A. Zhang, M. Li, and A. Smola, “Automatic chain of thought prompting in large language models,” in International Conference on Learning Representations
2023
Closest in time.
OpenAI, “Gpt-4 technical report,” 2023
2023
Closest in time.
G. Wang, N. Ivanov, B. Chen, Q. Wang, and Q. Yan, “Graph learning for interactive threat detection in heterogeneous smart home rule data,” in 2023 ACM SIGMOD International Conference on Management of Data
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
G. Wang, H. Guo, A. Li, X. Liu, and Q. Yan, “Federated iot interaction vulnerability analysis,” in 2023 IEEE 39th International Conference on Data Engineering (ICDE)
2023
Closest in time.