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The size of Transformer models is growing at an unprecedented pace.
Scaling distributed machine learning with the parameter server
Li, M., Andersen, D. G., Park, J. W., Smola, A. J., Ahmed, A., Josifovski, V., Long, J., Shekita, E. J., and Su, B.-Y · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
Raghu, M., Gilmer, J., Yosinski, J., and Sohl-Dickstein, J · 2017
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Earlier work this paper cites.
Gpipe: Efficient training of giant neural networks using pipeline parallelism
Huang, Y., Cheng, Y., Bapna, A., Firat, O., Chen, M. X., Chen, D., Lee, H., Ngiam, J., Le, Q. V., Wu, Y., et al · 2018
Earlier work this paper cites.
Insights on representational similarity in neural networks with canonical correlation
Morcos, A., Raghu, M., and Bengio, S · 2018
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Mesh-tensorflow: Deep learning for supercomputers
Shazeer, N., Cheng, Y., Parmar, N., Tran, D., Vaswani, A., Koanantakool, P., Hawkins, P., Lee, H., Hong, M., Young, C., Sepassi, R., and Hechtman, B · 2018
Earlier work this paper cites.
Gpipe: Efficient training of giant neural networks using pipeline parallelism
Huang, Y., Cheng, Y., Bapna, A., Firat, O., Chen, D., Chen, M., Lee, H., Ngiam, J., Le, Q. V., Wu, Y., and Chen, z · 2019
Cited alongside, same era.
Parallax: Sparsity-aware data parallel training of deep neural networks
Kim, S., Yu, G.-I., Park, H., Cho, S., Jeong, E., Ha, H., Lee, S., Jeong, J. S., and Chun, B.-G · 2019
Cited alongside, same era.
Pipedream: Generalized pipeline parallelism for dnn training
Narayanan, D., Harlap, A., Phanishayee, A., Seshadri, V., Devanur, N. R., Ganger, G. R., Gibbons, P. B., and Zaharia, M · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Cited alongside, same era.
Zero: Memory optimization towards training a trillion parameter models
Rajbhandari, S., Rasley, J., Ruwase, O., and He, Y · 2019
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
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A unified architecture for accelerating distributed DNN training in heterogeneous gpu/cpu clusters
Jiang, Y., Zhu, Y., Lan, C., Yi, B., Cui, Y., and Guo, C · 2020
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torchgpipe: On-the-fly pipeline parallelism for training giant models
Kim, C., Lee, H., Jeong, M., Baek, W., Yoon, B., Kim, I., Lim, S., and Kim, S · 2020
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Gshard: Scaling giant models with conditional computation and automatic sharding
Lepikhin, D., Lee, H., Xu, Y., Chen, D., Firat, O., Huang, Y., Krikun, M., Shazeer, N., and Chen, Z · 2020
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Cited alongside, same era.
Megatron-lm: Training multi-billion parameter language models using model parallelism
Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., and Catanzaro, B · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 2019
Cited alongside, same era.
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., et al · 2020
Cited alongside, same era.
Li, S., Zhao, Y., Varma, R., Salpekar, O., Noordhuis, P., Li, T., Paszke, A., Smith, J., Vaughan, B., Damania, P., et al · 2020
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Hetpipe: Enabling large DNN training on (whimpy) heterogeneous GPU clusters through integration of pipelined model parallelism and data parallelism
Park, J. H., Yun, G., Yi, C. M., Nguyen, N. T., Lee, S., Choi, J., Noh, S. H., and ri Choi, Y · 2020
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Shen, S., Baevski, A., Morcos, A. S., Keutzer, K., Auli, M., and Kiela, D · 2020
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On layer normalization in the transformer architecture
Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., and Liu, T · 2020
Later among the works it cites.