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Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data.
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., & Stoyanov, V. (2019) · 1907
Earlier work this paper cites.
R-transformer: Recurrent neural network enhanced transformer
Wang, Z., Ma, Y., Liu, Z., & Tang, J. (2019) · 1907
Earlier work this paper cites.
CTRL: A conditional transformer language model for controllable generation
Keskar, N. S., McCann, B., Varshney, L. R., Xiong, C., & Socher, R. (2019) · 1909
Earlier work this paper cites.
Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019) · 1910
Earlier work this paper cites.
Transformer-transducer: End-to-end speech recognition with self-attention
Yeh, C., Mahadeokar, J., Kalgaonkar, K., Wang, Y., Le, D., Jain, M., Schubert, K., Fuegen, C., & Seltzer, M. L. (2019) · 1910
Earlier work this paper cites.
The temporal logic of programs
Pnueli, A. (1977) · 1977
Earlier work this paper cites.
Image segmentation techniques
Haralick, R. M., & Shapiro, L. G. (1985) · 1985
Earlier work this paper cites.
Of brittleness and bottlenecks: Challenges in the creation of pattern-recognition and expert-system models
Mark A Musen, J. V. d. L. (1988) · 1988
Earlier work this paper cites.
Multilayer perceptrons for classification and regression
Murtagh, F. (1990) · 1990
Earlier work this paper cites.
Constructive learning of recurrent neural networks: limitations of recurrent cascade correlation and a simple solution
Giles, C. L., Chen, D., Sun, G., Chen, H., Lee, Y., & Goudreau, M. W. (1995) · 1995
Earlier work this paper cites.
Long short-term memory
Hochreiter, S., & Schmidhuber, J. (1997) · 1997
Earlier work this paper cites.
Building applied natural language generation systems
Reiter, E., & Dale, R. (1997) · 1997
Earlier work this paper cites.
Indoor-outdoor image classification
Szummer, M., & Picard, R. W. (1998) · 1998
Earlier work this paper cites.
Natural language question answering: the view from here
Hirschman, L., & Gaizauskas, R. J. (2001) · 2001
Earlier work this paper cites.
Pixel-bert: Aligning image pixels with text by deep multi-modal transformers
Huang, Z., Zeng, Z., Liu, B., Fu, D., & Fu, J. (2020) · 2004
Earlier work this paper cites.
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, A., & Schmidhuber, J. (2005) · 2005
Earlier work this paper cites.
A survey of image classification methods and techniques for improving classification performance
Lu, D., & Weng, Q. (2007) · 2007
Earlier work this paper cites.
A survey automatic text summarization
Tas, O., & Kiyani, F. (2007) · 2007
Earlier work this paper cites.
ADASYN: adaptive synthetic sampling approach for imbalanced learning
He, H., Bai, Y., Garcia, E. A., & Li, S. (2008) · 2008
Earlier work this paper cites.
Generative language modeling for automated theorem proving
Polu, S., & Sutskever, I. (2020) · 2009
Earlier work this paper cites.
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Cernocký, J., & Khudanpur, S. (2010) · 2010
Earlier work this paper cites.
New types of deep neural network learning for speech recognition and related applications: an overview
Deng, L., Hinton, G. E., & Kingsbury, B. (2013) · 2013
Earlier work this paper cites.
Deep learning for monaural speech separation
Huang, P., Kim, M., Hasegawa-Johnson, M., & Smaragdis, P. (2014) · 2014
Earlier work this paper cites.
A survey and classification of controlled natural languages
Kuhn, T. (2014) · 2014
Earlier work this paper cites.
VQA: visual question answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015
Earlier work this paper cites.
Advances in natural language processing
Hirschberg, J., & Manning, C. D. (2015) · 2015
Earlier work this paper cites.
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J. S., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E. R., Weber, M., Arbel, T., Avants, B. B., Ayache, N., Buendia, P., Collins, D. L., Cordier, N., Corso, J. J., Criminisi, A., Das, T., Delingette, H., Demiralp, Ç., Durst, C. R., Dojat, M., Doyle, S., Festa, J., Forbes, F., Geremia, E., Glocker, B., Golland, P., Guo, X., Hamamci, A., Iftekharuddin, K. M., Jena, R., John, N. M., Konukoglu, E., Lashkari, D., Mariz, J. A., Meier, R., Pereira, S., Precup, D., Price, S. J., Raviv, T. R., Reza, S. M. S., Ryan, M. T., Sarikaya, D., Schwartz, L. H., Shin, H., Shotton, J., Silva, C. A., Sousa, N. J., Subbanna, N. K., Székely, G., Taylor, T. J., Thomas, O. M., Tustison, N. J., Ünal, G. B., Vasseur, F., Wintermark, M., Ye, D. H., Zhao, L., Zhao, B., Zikic, D., Prastawa, M., Reyes, M., & Leemput, K. V. (2015) · 2015
Earlier work this paper cites.
An introduction to convolutional neural networks
O’Shea, K., & Nash, R. (2015) · 2015
Earlier work this paper cites.
Segmentation from natural language expressions
Hu, R., Rohrbach, M., & Darrell, T. (2016) · 2016
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
van den Oord, A., Kalchbrenner, N., Espeholt, L., Kavukcuoglu, K., Vinyals, O., & Graves, A. (2016) · 2016
Earlier work this paper cites.
Where to look: Focus regions for visual question answering
Shih, K. J., Singh, S., & Hoiem, D. (2016) · 2016
Earlier work this paper cites.
Deep dynamic neural networks for multimodal gesture segmentation and recognition
Wu, D., Pigou, L., Kindermans, P., Le, N. D., Shao, L., Dambre, J., & Odobez, J. (2016) · 2016
Earlier work this paper cites.
Automatic speech recognition
Yu, D., & Deng, L. (2016) · 2016
Earlier work this paper cites.
Speech intention classification with multimodal deep learning
Gu, Y., Li, X., Chen, S., Zhang, J., & Marsic, I. (2017) · 2017
Earlier work this paper cites.
Multi-key privacy-preserving deep learning in cloud computing
Li, P., Li, J., Huang, Z., Li, T., Gao, C., Yiu, S., & Chen, K. (2017) · 2017
Earlier work this paper cites.
Unsupervised image-to-image translation networks
Liu, M., Breuel, T. M., & Kautz, J. (2017) · 2017
Earlier work this paper cites.
Deep learning takes on translation
Monroe, D. (2017) · 2017
Earlier work this paper cites.
Learning to optimize: Training deep neural networks for wireless resource management
Sun, H., Chen, X., Shi, Q., Hong, M., Fu, X., & Sidiropoulos, N. D. (2017) · 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, L., & Polosukhin, I. (2017) · 2017
Earlier work this paper cites.
Computation offloading for mobile edge computing: A deep learning approach
Yu, S., Wang, X., & Langar, R. (2017) · 2017
Earlier work this paper cites.
Speech-transformer: A no-recurrence sequence-to-sequence model for speech recognition
Dong, L., Xu, S., & Xu, B. (2018) · 2018
Earlier work this paper cites.
Image transformer
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, L., Shazeer, N., Ku, A., & Tran, D. (2018) · 2018
Earlier work this paper cites.
Improving language understanding with unsupervised learning
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018) · 2018
Earlier work this paper cites.
Supervised speech separation based on deep learning: An overview
Wang, D., & Chen, J. (2018) · 2018
Earlier work this paper cites.
This looks like that: Deep learning for interpretable image recognition
Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., & Su, J. (2019a) · 2019
Earlier work this paper cites.
Cross-lingual language model pretraining
Conneau, A., & Lample, G. (2019) · 2019
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M., Lee, K., & Toutanova, K. (2019) · 2019
Earlier work this paper cites.
A comprehensive survey of deep learning for image captioning
Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019) · 2019
Earlier work this paper cites.
A survey of deep learning-based object detection
Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., & Qu, R. (2019) · 2019
Earlier work this paper cites.
Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex
Livezey, J. A., Bouchard, K. E., & Chang, E. F. (2019) · 2019
Earlier work this paper cites.
Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Lu, J., Batra, D., Parikh, D., & Lee, S. (2019) · 2019
Earlier work this paper cites.
Speech recognition using deep neural networks: A systematic review
Nassif, A. B., Shahin, I., Attili, I. B., Azzeh, M., & Shaalan, K. (2019) · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I. et al. (2019) · 2019
Earlier work this paper cites.
Deep smart scheduling: A deep learning approach for automated big data scheduling over the cloud
Rjoub, G., Bentahar, J., Wahab, O. A., & Bataineh, A. (2019) · 2019
Earlier work this paper cites.
Learning a SAT solver from single-bit supervision
Selsam, D., Lamm, M., Bünz, B., Liang, P., de Moura, L., & Dill, D. L. (2019) · 2019
Earlier work this paper cites.
CLUTRR: A diagnostic benchmark for inductive reasoning from text
Sinha, K., Sodhani, S., Dong, J., Pineau, J., & Hamilton, W. L. (2019) · 2019
Earlier work this paper cites.
LXMERT: learning cross-modality encoder representations from transformers
Tan, H., & Bansal, M. (2019) · 2019
Earlier work this paper cites.
Graph transformer networks
Yun, S., Jeong, M., Kim, R., Kang, J., & Kim, H. J. (2019) · 2019
Earlier work this paper cites.
From recognition to cognition: Visual commonsense reasoning
Zellers, R., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · 2019
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Deep learning in mobile and wireless networking: A survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · 2019
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Object detection with deep learning: A review
Zhao, Z., Zheng, P., Xu, S., & Wu, X. (2019) · 2019
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Visualizing transformers for NLP: A brief survey
Brasoveanu, A. M. P., & Andonie, R. (2020) · 2020
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., & Amodei, D. (2020) · 2020
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End-to-end object detection with transformers
Zero-shot text-to-image generation
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021) · 2021
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Deep and reinforcement learning for automated task scheduling in large-scale cloud computing systems
Rjoub, G., Bentahar, J., Abdel Wahab, O., & Saleh Bataineh, A. (2021) · 2021
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Transformer-based machine learning for fast SAT solvers and logic synthesis
Shi, F., Lee, C., Bashar, M. K., Shukla, N., Zhu, S., & Narayanan, V. (2021) · 2021
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Attention is all you need in speech separation
Subakan, C., Ravanelli, M., Cornell, S., Bronzi, M., & Zhong, J. (2021) · 2021
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Hybridctrm: Bridging cnn and transformer for multimodal brain image segmentation
Sun, Q., Fang, N., Liu, Z., Zhao, L., Wen, Y., Lin, H. et al. (2021b) · 2021
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Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · 2020
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Natural language processing
Chowdhary, K., & Chowdhary, K. (2020) · 2020
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ELECTRA: pre-training text encoders as discriminators rather than generators
Clark, K., Luong, M., Le, Q. V., & Manning, C. D. (2020a) · 2020
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Conformer: Convolution-augmented transformer for speech recognition
Gulati, A., Qin, J., Chiu, C., Parmar, N., Zhang, Y., Yu, J., Han, W., Wang, S., Zhang, Z., Wu, Y., & Pang, R. (2020) · 2020
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Data-efficient image recognition with contrastive predictive coding
Hénaff, O. J. (2020) · 2020
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Deep learning architectures in emerging cloud computing architectures: Recent development, challenges and next research trend
Jauro, F., Chiroma, H., Gital, A. Y., Almutairi, M., Abdulhamid, S. M., & Abawajy, J. H. (2020) · 2020
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A multi-layer bidirectional transformer encoder for pre-trained word embedding: A survey of bert
Kaliyar, R. K. (2020) · 2020
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Training data-efficient image transformers & distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., & Jégou, H. (2021) · 2021
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Bertology meets biology: Interpreting attention in protein language models
Vig, J., Madani, A., Varshney, L. R., Xiong, C., Socher, R., & Rajani, N. F. (2021) · 2021
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KM-BART: knowledge enhanced multimodal BART for visual commonsense generation
Xing, Y., Shi, Z., Meng, Z., Lakemeyer, G., Ma, Y., & Wattenhofer, R. (2021) · 2021
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Videogpt: Video generation using VQ-VAE and transformers
Yan, W., Zhang, Y., Abbeel, P., & Srinivas, A. (2021) · 2021
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Florence: A new foundation model for computer vision
Yuan, L., Chen, D., Chen, Y., Codella, N., Dai, X., Gao, J., Hu, H., Huang, X., Li, B., Li, C., Liu, C., Liu, M., Liu, Z., Lu, Y., Shi, Y., Wang, L., Wang, J., Xiao, B., Xiao, Z., Yang, J., Zeng, M., Zhou, L., & Zhang, P. (2021) · 2021
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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P. H. S., & Zhang, L. (2021) · 2021
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XLS-R: self-supervised cross-lingual speech representation learning at scale
Babu, A., Wang, C., Tjandra, A., Lakhotia, K., Xu, Q., Goyal, N., Singh, K., von Platen, P., Saraf, Y., Pino, J., Baevski, A., Conneau, A., & Auli, M. (2022) · 2022
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Beit: BERT pre-training of image transformers
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Comprehensive comparative study of multi-label classification methods
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Temporal convolutional networks and transformers for classifying the sleep stage in awake or asleep using pulse oximetry signals
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Deep transfer learning & beyond: Transformer language models in information systems research
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Chest l-transformer: local features with position attention for weakly supervised chest radiograph segmentation and classification
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Tedge-caching: Transformer-based edge caching towards 6g networks
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Fully transformer network for skin lesion analysis
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Explainable transformer-based deep learning model for the detection of malaria parasites from blood cell images
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Transformers in vision: A survey
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Towards javascript program repair with generative pre-trained transformer (GPT-2)
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Knowledge distillation-based deep learning classification network for peripheral blood leukocytes
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Transconver: transformer and convolution parallel network for developing automatic brain tumor segmentation in mri images
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Symmetric transformer-based network for unsupervised image registration
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GLIDE: towards photorealistic image generation and editing with text-guided diffusion models
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Training language models to follow instructions with human feedback
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Robust speech recognition via large-scale weak supervision
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Sigt: An efficient end-to-end MIMO-OFDM receiver framework based on transformer
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A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks
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Trust-driven reinforcement selection strategy for federated learning on IoT devices
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Charformer: Fast character transformers via gradient-based subword tokenization
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Quan-transformer based channel feedback for ris-aided wireless communication systems
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Actor-critic with transformer for cloud computing resource three stage job scheduling
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Automated diagnosis of atrial fibrillation using ECG component-aware transformer
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Bigssl: Exploring the frontier of large-scale semi-supervised learning for automatic speech recognition
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