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  2022 (13)
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. Patterson, D. A.; Gonzalez, J.; Hölzle, U.; Le, Q. V.; Liang, C.; Munguia, L.; Rothchild, D.; So, D. R.; Texier, M.; and Dean, J. Computer, 55(7): 18–28. 2022.
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink [link]Paper   doi   link   bibtex  
Emergent Abilities of Large Language Models. Wei, J.; Tay, Y.; Bommasani, R.; Raffel, C.; Zoph, B.; Borgeaud, S.; Yogatama, D.; Bosma, M.; Zhou, D.; Metzler, D.; Chi, E. H.; Hashimoto, T.; Vinyals, O.; Liang, P.; Dean, J.; and Fedus, W. Trans. Mach. Learn. Res., 2022. 2022.
Emergent Abilities of Large Language Models [link]Paper   link   bibtex  
Pathways: Asynchronous Distributed Dataflow for ML. Barham, P.; Chowdhery, A.; Dean, J.; Ghemawat, S.; Hand, S.; Hurt, D.; Isard, M.; Lim, H.; Pang, R.; Roy, S.; Saeta, B.; Schuh, P.; Sepassi, R.; Shafey, L. E.; Thekkath, C. A.; and Wu, Y. In Proceedings of Machine Learning and Systems 2022, MLSys 2022, Santa Clara, CA, USA, August 29 - September 1, 2022, 2022.
Pathways: Asynchronous Distributed Dataflow for ML [link]Paper   link   bibtex  
Designing Effective Sparse Expert Models. Zoph, B.; Bello, I.; Kumar, S.; Du, N.; Huang, Y.; Dean, J.; Shazeer, N.; and Fedus, W. CoRR, abs/2202.08906. 2022.
Designing Effective Sparse Expert Models [link]Paper   link   bibtex  
Pathways: Asynchronous Distributed Dataflow for ML. Barham, P.; Chowdhery, A.; Dean, J.; Ghemawat, S.; Hand, S.; Hurt, D.; Isard, M.; Lim, H.; Pang, R.; Roy, S.; Saeta, B.; Schuh, P.; Sepassi, R.; Shafey, L. E.; Thekkath, C. A.; and Wu, Y. CoRR, abs/2203.12533. 2022.
Pathways: Asynchronous Distributed Dataflow for ML [link]Paper   doi   link   bibtex  
PaLM: Scaling Language Modeling with Pathways. Chowdhery, A.; Narang, S.; Devlin, J.; Bosma, M.; Mishra, G.; Roberts, A.; Barham, P.; Chung, H. W.; Sutton, C.; Gehrmann, S.; Schuh, P.; Shi, K.; Tsvyashchenko, S.; Maynez, J.; Rao, A.; Barnes, P.; Tay, Y.; Shazeer, N.; Prabhakaran, V.; Reif, E.; Du, N.; Hutchinson, B.; Pope, R.; Bradbury, J.; Austin, J.; Isard, M.; Gur-Ari, G.; Yin, P.; Duke, T.; Levskaya, A.; Ghemawat, S.; Dev, S.; Michalewski, H.; Garcia, X.; Misra, V.; Robinson, K.; Fedus, L.; Zhou, D.; Ippolito, D.; Luan, D.; Lim, H.; Zoph, B.; Spiridonov, A.; Sepassi, R.; Dohan, D.; Agrawal, S.; Omernick, M.; Dai, A. M.; Pillai, T. S.; Pellat, M.; Lewkowycz, A.; Moreira, E.; Child, R.; Polozov, O.; Lee, K.; Zhou, Z.; Wang, X.; Saeta, B.; Diaz, M.; Firat, O.; Catasta, M.; Wei, J.; Meier-Hellstern, K.; Eck, D.; Dean, J.; Petrov, S.; and Fiedel, N. CoRR, abs/2204.02311. 2022.
PaLM: Scaling Language Modeling with Pathways [link]Paper   doi   link   bibtex  
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. Patterson, D. A.; Gonzalez, J.; Hölzle, U.; Le, Q. V.; Liang, C.; Munguia, L.; Rothchild, D.; So, D. R.; Texier, M.; and Dean, J. CoRR, abs/2204.05149. 2022.
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink [link]Paper   doi   link   bibtex  
muNet: Evolving Pretrained Deep Neural Networks into Scalable Auto-tuning Multitask Systems. Gesmundo, A.; and Dean, J. CoRR, abs/2205.10937. 2022.
muNet: Evolving Pretrained Deep Neural Networks into Scalable Auto-tuning Multitask Systems [link]Paper   doi   link   bibtex  
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems. Gesmundo, A.; and Dean, J. CoRR, abs/2205.12755. 2022.
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems [link]Paper   doi   link   bibtex  
Emergent Abilities of Large Language Models. Wei, J.; Tay, Y.; Bommasani, R.; Raffel, C.; Zoph, B.; Borgeaud, S.; Yogatama, D.; Bosma, M.; Zhou, D.; Metzler, D.; Chi, E. H.; Hashimoto, T.; Vinyals, O.; Liang, P.; Dean, J.; and Fedus, W. CoRR, abs/2206.07682. 2022.
Emergent Abilities of Large Language Models [link]Paper   doi   link   bibtex  
A Review of Sparse Expert Models in Deep Learning. Fedus, W.; Dean, J.; and Zoph, B. CoRR, abs/2209.01667. 2022.
A Review of Sparse Expert Models in Deep Learning [link]Paper   doi   link   bibtex  
Scaling Instruction-Finetuned Language Models. Chung, H. W.; Hou, L.; Longpre, S.; Zoph, B.; Tay, Y.; Fedus, W.; Li, E.; Wang, X.; Dehghani, M.; Brahma, S.; Webson, A.; Gu, S. S.; Dai, Z.; Suzgun, M.; Chen, X.; Chowdhery, A.; Narang, S.; Mishra, G.; Yu, A.; Zhao, V. Y.; Huang, Y.; Dai, A. M.; Yu, H.; Petrov, S.; Chi, E. H.; Dean, J.; Devlin, J.; Roberts, A.; Zhou, D.; Le, Q. V.; and Wei, J. CoRR, abs/2210.11416. 2022.
Scaling Instruction-Finetuned Language Models [link]Paper   doi   link   bibtex  
Efficiently Scaling Transformer Inference. Pope, R.; Douglas, S.; Chowdhery, A.; Devlin, J.; Bradbury, J.; Levskaya, A.; Heek, J.; Xiao, K.; Agrawal, S.; and Dean, J. CoRR, abs/2211.05102. 2022.
Efficiently Scaling Transformer Inference [link]Paper   doi   link   bibtex  
  2021 (3)
A graph placement methodology for fast chip design. Mirhoseini, A.; Goldie, A.; Yazgan, M.; Jiang, J. W.; Songhori, E. M.; Wang, S.; Lee, Y.; Johnson, E.; Pathak, O.; Nazi, A.; Pak, J.; Tong, A.; Srinivasa, K.; Hang, W.; Tuncer, E.; Le, Q. V.; Laudon, J.; Ho, R.; Carpenter, R.; and Dean, J. Nat., 594(7862): 207–212. 2021.
A graph placement methodology for fast chip design [link]Paper   doi   link   bibtex  
Deep learning-enabled medical computer vision. Esteva, A.; Chou, K.; Yeung, S.; Naik, N.; Madani, A.; Mottaghi, A.; Liu, Y.; Topol, E. J.; Dean, J.; and Socher, R. npj Digit. Medicine, 4. 2021.
Deep learning-enabled medical computer vision [link]Paper   doi   link   bibtex  
Carbon Emissions and Large Neural Network Training. Patterson, D. A.; Gonzalez, J.; Le, Q. V.; Liang, C.; Munguia, L.; Rothchild, D.; So, D. R.; Texier, M.; and Dean, J. CoRR, abs/2104.10350. 2021.
Carbon Emissions and Large Neural Network Training [link]Paper   link   bibtex  
  2020 (4)
Customization scenarios for de-identification of clinical notes. Hartman, T.; Howell, M. D.; Dean, J.; Hoory, S.; Slyper, R.; Laish, I.; Gilon, O.; Vainstein, D.; Corrado, G.; Chou, K.; Po, M. J.; Williams, J.; Ellis, S.; Bee, G.; Hassidim, A.; Amira, R.; Beryozkin, G.; Szpektor, I.; and Matias, Y. BMC Medical Informatics Decis. Mak., 20(1): 14. 2020.
Customization scenarios for de-identification of clinical notes [link]Paper   doi   link   bibtex  
1.1 The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design. Dean, J. In 2020 IEEE International Solid- State Circuits Conference, ISSCC 2020, San Francisco, CA, USA, February 16-20, 2020, pages 8–14, 2020.
1.1 The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design [link]Paper   doi   link   bibtex  
Chip Placement with Deep Reinforcement Learning. Mirhoseini, A.; Goldie, A.; Yazgan, M.; Jiang, J. W. J.; Songhori, E. M.; Wang, S.; Lee, Y.; Johnson, E.; Pathak, O.; Bae, S.; Nazi, A.; Pak, J.; Tong, A.; Srinivasa, K.; Hang, W.; Tuncer, E.; Babu, A.; Le, Q. V.; Laudon, J.; Ho, R.; Carpenter, R.; and Dean, J. CoRR, abs/2004.10746. 2020.
Chip Placement with Deep Reinforcement Learning [link]Paper   link   bibtex  
Interlocking Backpropagation: Improving depthwise model-parallelism. Gomez, A. N.; Key, O.; Gou, S.; Frosst, N.; Dean, J.; and Gal, Y. CoRR, abs/2010.04116. 2020.
Interlocking Backpropagation: Improving depthwise model-parallelism [link]Paper   link   bibtex  
  2019 (4)
Deep Learning for Solving Important Problems. Dean, J. In The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019, pages 1, 2019.
Deep Learning for Solving Important Problems [link]Paper   doi   link   bibtex  
SysML: The New Frontier of Machine Learning Systems. Ratner, A.; Alistarh, D.; Alonso, G.; Andersen, D. G.; Bailis, P.; Bird, S.; Carlini, N.; Catanzaro, B.; Chung, E.; Dally, B.; Dean, J.; Dhillon, I. S.; Dimakis, A. G.; Dubey, P.; Elkan, C.; Fursin, G.; Ganger, G. R.; Getoor, L.; Gibbons, P. B.; Gibson, G. A.; Gonzalez, J. E.; Gottschlich, J.; Han, S.; Hazelwood, K. M.; Huang, F.; Jaggi, M.; Jamieson, K. G.; Jordan, M. I.; Joshi, G.; Khalaf, R.; Knight, J.; Konečný, J.; Kraska, T.; Kumar, A.; Kyrillidis, A.; Li, J.; Madden, S.; McMahan, H. B.; Meijer, E.; Mitliagkas, I.; Monga, R.; Murray, D. G.; Papailiopoulos, D. S.; Pekhimenko, G.; Rekatsinas, T.; Rostamizadeh, A.; Ré, C.; Sa, C. D.; Sedghi, H.; Sen, S.; Smith, V.; Smola, A.; Song, D.; Sparks, E. R.; Stoica, I.; Sze, V.; Udell, M.; Vanschoren, J.; Venkataraman, S.; Vinayak, R.; Weimer, M.; Wilson, A. G.; Xing, E. P.; Zaharia, M.; Zhang, C.; and Talwalkar, A. CoRR, abs/1904.03257. 2019.
SysML: The New Frontier of Machine Learning Systems [link]Paper   link   bibtex  
Accelerating Deep Learning by Focusing on the Biggest Losers. Jiang, A. H.; Wong, D. L.; Zhou, G.; Andersen, D. G.; Dean, J.; Ganger, G. R.; Joshi, G.; Kaminsky, M.; Kozuch, M.; Lipton, Z. C.; and Pillai, P. CoRR, abs/1910.00762. 2019.
Accelerating Deep Learning by Focusing on the Biggest Losers [link]Paper   link   bibtex  
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design. Dean, J. CoRR, abs/1911.05289. 2019.
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design [link]Paper   link   bibtex  
  2018 (12)
A New Golden Age in Computer Architecture: Empowering the Machine-Learning Revolution. Dean, J.; Patterson, D. A.; and Young, C. IEEE Micro, 38(2): 21–29. 2018.
A New Golden Age in Computer Architecture: Empowering the Machine-Learning Revolution [link]Paper   doi   link   bibtex  
Reply: metrics to assess machine learning models. Rajkomar, A.; Dai, A. M.; Sun, M.; Hardt, M.; Chen, K.; Rough, K.; and Dean, J. npj Digit. Medicine, 1. 2018.
Reply: metrics to assess machine learning models [link]Paper   doi   link   bibtex  
Scalable and accurate deep learning with electronic health records. Rajkomar, A.; Oren, E.; Chen, K.; Dai, A. M.; Hajaj, N.; Hardt, M.; Liu, P. J.; Liu, X.; Marcus, J.; Sun, M.; Sundberg, P.; Yee, H.; Zhang, K.; Zhang, Y.; Flores, G.; Duggan, G. E.; Irvine, J.; Le, Q.; Litsch, K.; Mossin, A.; Tansuwan, J.; Wang, D.; Wexler, J.; Wilson, J.; Ludwig, D.; Volchenboum, S. L.; Chou, K.; Pearson, M.; Madabushi, S.; Shah, N. H.; Butte, A. J.; Howell, M. D.; Cui, C.; Corrado, G. S.; and Dean, J. npj Digit. Medicine, 1. 2018.
Scalable and accurate deep learning with electronic health records [link]Paper   doi   link   bibtex  
Dynamic control flow in large-scale machine learning. Yu, Y.; Abadi, M.; Barham, P.; Brevdo, E.; Burrows, M.; Davis, A.; Dean, J.; Ghemawat, S.; Harley, T.; Hawkins, P.; Isard, M.; Kudlur, M.; Monga, R.; Murray, D. G.; and Zheng, X. In Proceedings of the Thirteenth EuroSys Conference, EuroSys 2018, Porto, Portugal, April 23-26, 2018, pages 18:1–18:15, 2018.
Dynamic control flow in large-scale machine learning [link]Paper   doi   link   bibtex  
A Hierarchical Model for Device Placement. Mirhoseini, A.; Goldie, A.; Pham, H.; Steiner, B.; Le, Q. V.; and Dean, J. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings, 2018.
A Hierarchical Model for Device Placement [link]Paper   link   bibtex  
Faster Discovery of Neural Architectures by Searching for Paths in a Large Model. Pham, H.; Guan, M. Y.; Zoph, B.; Le, Q. V.; and Dean, J. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Workshop Track Proceedings, 2018.
Faster Discovery of Neural Architectures by Searching for Paths in a Large Model [link]Paper   link   bibtex  
Efficient Neural Architecture Search via Parameter Sharing. Pham, H.; Guan, M. Y.; Zoph, B.; Le, Q. V.; and Dean, J. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, pages 4092–4101, 2018.
Efficient Neural Architecture Search via Parameter Sharing [link]Paper   link   bibtex  
The Case for Learned Index Structures. Kraska, T.; Beutel, A.; Chi, E. H.; Dean, J.; and Polyzotis, N. In Proceedings of the 2018 International Conference on Management of Data, SIGMOD Conference 2018, Houston, TX, USA, June 10-15, 2018, pages 489–504, 2018.
The Case for Learned Index Structures [link]Paper   doi   link   bibtex  
Scalable and accurate deep learning for electronic health records. Rajkomar, A.; Oren, E.; Chen, K.; Dai, A. M.; Hajaj, N.; Liu, P. J.; Liu, X.; Sun, M.; Sundberg, P.; Yee, H.; Zhang, K.; Duggan, G. E.; Flores, G.; Hardt, M.; Irvine, J.; Le, Q. V.; Litsch, K.; Marcus, J.; Mossin, A.; Tansuwan, J.; Wang, D.; Wexler, J.; Wilson, J.; Ludwig, D.; Volchenboum, S. L.; Chou, K.; Pearson, M.; Madabushi, S.; Shah, N. H.; Butte, A. J.; Howell, M. D.; Cui, C.; Corrado, G.; and Dean, J. CoRR, abs/1801.07860. 2018.
Scalable and accurate deep learning for electronic health records [link]Paper   link   bibtex  
Efficient Neural Architecture Search via Parameter Sharing. Pham, H.; Guan, M. Y.; Zoph, B.; Le, Q. V.; and Dean, J. CoRR, abs/1802.03268. 2018.
Efficient Neural Architecture Search via Parameter Sharing [link]Paper   link   bibtex  
Dynamic Control Flow in Large-Scale Machine Learning. Yu, Y.; Abadi, M.; Barham, P.; Brevdo, E.; Burrows, M.; Davis, A.; Dean, J.; Ghemawat, S.; Harley, T.; Hawkins, P.; Isard, M.; Kudlur, M.; Monga, R.; Murray, D. G.; and Zheng, X. CoRR, abs/1805.01772. 2018.
Dynamic Control Flow in Large-Scale Machine Learning [link]Paper   link   bibtex  
Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration. Chen, P. C.; Gadepalli, K.; MacDonald, R.; Liu, Y.; Nagpal, K.; Kohlberger, T.; Dean, J.; Corrado, G. S.; Hipp, J. D.; and Stumpe, M. C. CoRR, abs/1812.00825. 2018.
Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration [link]Paper   link   bibtex  
  2017 (8)
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation. Johnson, M.; Schuster, M.; Le, Q. V.; Krikun, M.; Wu, Y.; Chen, Z.; Thorat, N.; Viégas, F. B.; Wattenberg, M.; Corrado, G.; Hughes, M.; and Dean, J. Trans. Assoc. Comput. Linguistics, 5: 339–351. 2017.
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation [link]Paper   doi   link   bibtex  
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. Shazeer, N.; Mirhoseini, A.; Maziarz, K.; Davis, A.; Le, Q. V.; Hinton, G. E.; and Dean, J. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, 2017.
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer [link]Paper   link   bibtex  
Device Placement Optimization with Reinforcement Learning. Mirhoseini, A.; Pham, H.; Le, Q. V.; Steiner, B.; Larsen, R.; Zhou, Y.; Kumar, N.; Norouzi, M.; Bengio, S.; and Dean, J. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, pages 2430–2439, 2017.
Device Placement Optimization with Reinforcement Learning [link]Paper   link   bibtex  
In-Datacenter Performance Analysis of a Tensor Processing Unit. Jouppi, N. P.; Young, C.; Patil, N.; Patterson, D. A.; Agrawal, G.; Bajwa, R.; Bates, S.; Bhatia, S.; Boden, N.; Borchers, A.; Boyle, R.; Cantin, P.; Chao, C.; Clark, C.; Coriell, J.; Daley, M.; Dau, M.; Dean, J.; Gelb, B.; Ghaemmaghami, T. V.; Gottipati, R.; Gulland, W.; Hagmann, R.; Ho, C. R.; Hogberg, D.; Hu, J.; Hundt, R.; Hurt, D.; Ibarz, J.; Jaffey, A.; Jaworski, A.; Kaplan, A.; Khaitan, H.; Killebrew, D.; Koch, A.; Kumar, N.; Lacy, S.; Laudon, J.; Law, J.; Le, D.; Leary, C.; Liu, Z.; Lucke, K.; Lundin, A.; MacKean, G.; Maggiore, A.; Mahony, M.; Miller, K.; Nagarajan, R.; Narayanaswami, R.; Ni, R.; Nix, K.; Norrie, T.; Omernick, M.; Penukonda, N.; Phelps, A.; Ross, J.; Ross, M.; Salek, A.; Samadiani, E.; Severn, C.; Sizikov, G.; Snelham, M.; Souter, J.; Steinberg, D.; Swing, A.; Tan, M.; Thorson, G.; Tian, B.; Toma, H.; Tuttle, E.; Vasudevan, V.; Walter, R.; Wang, W.; Wilcox, E.; and Yoon, D. H. In Proceedings of the 44th Annual International Symposium on Computer Architecture, ISCA 2017, Toronto, ON, Canada, June 24-28, 2017, pages 1–12, 2017.
In-Datacenter Performance Analysis of a Tensor Processing Unit [link]Paper   doi   link   bibtex  
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. Shazeer, N.; Mirhoseini, A.; Maziarz, K.; Davis, A.; Le, Q. V.; Hinton, G. E.; and Dean, J. CoRR, abs/1701.06538. 2017.
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer [link]Paper   link   bibtex  
In-Datacenter Performance Analysis of a Tensor Processing Unit. Jouppi, N. P.; Young, C.; Patil, N.; Patterson, D. A.; Agrawal, G.; Bajwa, R.; Bates, S.; Bhatia, S.; Boden, N.; Borchers, A.; Boyle, R.; Cantin, P.; Chao, C.; Clark, C.; Coriell, J.; Daley, M.; Dau, M.; Dean, J.; Gelb, B.; Ghaemmaghami, T. V.; Gottipati, R.; Gulland, W.; Hagmann, R.; Ho, C. R.; Hogberg, D.; Hu, J.; Hundt, R.; Hurt, D.; Ibarz, J.; Jaffey, A.; Jaworski, A.; Kaplan, A.; Khaitan, H.; Koch, A.; Kumar, N.; Lacy, S.; Laudon, J.; Law, J.; Le, D.; Leary, C.; Liu, Z.; Lucke, K.; Lundin, A.; MacKean, G.; Maggiore, A.; Mahony, M.; Miller, K.; Nagarajan, R.; Narayanaswami, R.; Ni, R.; Nix, K.; Norrie, T.; Omernick, M.; Penukonda, N.; Phelps, A.; Ross, J.; Salek, A.; Samadiani, E.; Severn, C.; Sizikov, G.; Snelham, M.; Souter, J.; Steinberg, D.; Swing, A.; Tan, M.; Thorson, G.; Tian, B.; Toma, H.; Tuttle, E.; Vasudevan, V.; Walter, R.; Wang, W.; Wilcox, E.; and Yoon, D. H. CoRR, abs/1704.04760. 2017.
In-Datacenter Performance Analysis of a Tensor Processing Unit [link]Paper   link   bibtex  
Device Placement Optimization with Reinforcement Learning. Mirhoseini, A.; Pham, H.; Le, Q. V.; Steiner, B.; Larsen, R.; Zhou, Y.; Kumar, N.; Norouzi, M.; Bengio, S.; and Dean, J. CoRR, abs/1706.04972. 2017.
Device Placement Optimization with Reinforcement Learning [link]Paper   link   bibtex  
The Case for Learned Index Structures. Kraska, T.; Beutel, A.; Chi, E. H.; Dean, J.; and Polyzotis, N. CoRR, abs/1712.01208. 2017.
The Case for Learned Index Structures [link]Paper   link   bibtex  
  2016 (8)
The Beckman report on database research. Abadi, D.; Agrawal, R.; Ailamaki, A.; Balazinska, M.; Bernstein, P. A.; Carey, M. J.; Chaudhuri, S.; Dean, J.; Doan, A.; Franklin, M. J.; Gehrke, J.; Haas, L. M.; Halevy, A. Y.; Hellerstein, J. M.; Ioannidis, Y. E.; Jagadish, H. V.; Kossmann, D.; Madden, S.; Mehrotra, S.; Milo, T.; Naughton, J. F.; Ramakrishnan, R.; Markl, V.; Olston, C.; Ooi, B. C.; Ré, C.; Suciu, D.; Stonebraker, M.; Walter, T.; and Widom, J. Commun. ACM, 59(2): 92–99. 2016.
The Beckman report on database research [link]Paper   doi   link   bibtex  
TensorFlow: A System for Large-Scale Machine Learning. Abadi, M.; Barham, P.; Chen, J.; Chen, Z.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Irving, G.; Isard, M.; Kudlur, M.; Levenberg, J.; Monga, R.; Moore, S.; Murray, D. G.; Steiner, B.; Tucker, P. A.; Vasudevan, V.; Warden, P.; Wicke, M.; Yu, Y.; and Zheng, X. In 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, Savannah, GA, USA, November 2-4, 2016, pages 265–283, 2016.
TensorFlow: A System for Large-Scale Machine Learning [link]Paper   link   bibtex  
Building Machine Learning Systems that Understand. Dean, J. In Proceedings of the 2016 International Conference on Management of Data, SIGMOD Conference 2016, San Francisco, CA, USA, June 26 - July 01, 2016, pages 1, 2016.
Building Machine Learning Systems that Understand [link]Paper   doi   link   bibtex  
Large-Scale Deep Learning For Building Intelligent Computer Systems. Dean, J. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, San Francisco, CA, USA, February 22-25, 2016, pages 1, 2016.
Large-Scale Deep Learning For Building Intelligent Computer Systems [link]Paper   doi   link   bibtex  
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, G. S.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Goodfellow, I. J.; Harp, A.; Irving, G.; Isard, M.; Jia, Y.; Józefowicz, R.; Kaiser, L.; Kudlur, M.; Levenberg, J.; Mané, D.; Monga, R.; Moore, S.; Murray, D. G.; Olah, C.; Schuster, M.; Shlens, J.; Steiner, B.; Sutskever, I.; Talwar, K.; Tucker, P. A.; Vanhoucke, V.; Vasudevan, V.; Viégas, F. B.; Vinyals, O.; Warden, P.; Wattenberg, M.; Wicke, M.; Yu, Y.; and Zheng, X. CoRR, abs/1603.04467. 2016.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems [link]Paper   link   bibtex  
TensorFlow: A system for large-scale machine learning. Abadi, M.; Barham, P.; Chen, J.; Chen, Z.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Irving, G.; Isard, M.; Kudlur, M.; Levenberg, J.; Monga, R.; Moore, S.; Murray, D. G.; Steiner, B.; Tucker, P. A.; Vasudevan, V.; Warden, P.; Wicke, M.; Yu, Y.; and Zhang, X. CoRR, abs/1605.08695. 2016.
TensorFlow: A system for large-scale machine learning [link]Paper   link   bibtex  
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. Wu, Y.; Schuster, M.; Chen, Z.; Le, Q. V.; Norouzi, M.; Macherey, W.; Krikun, M.; Cao, Y.; Gao, Q.; Macherey, K.; Klingner, J.; Shah, A.; Johnson, M.; Liu, X.; Kaiser, L.; Gouws, S.; Kato, Y.; Kudo, T.; Kazawa, H.; Stevens, K.; Kurian, G.; Patil, N.; Wang, W.; Young, C.; Smith, J.; Riesa, J.; Rudnick, A.; Vinyals, O.; Corrado, G.; Hughes, M.; and Dean, J. CoRR, abs/1609.08144. 2016.
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation [link]Paper   link   bibtex  
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation. Johnson, M.; Schuster, M.; Le, Q. V.; Krikun, M.; Wu, Y.; Chen, Z.; Thorat, N.; Viégas, F. B.; Wattenberg, M.; Corrado, G.; Hughes, M.; and Dean, J. CoRR, abs/1611.04558. 2016.
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation [link]Paper   link   bibtex  
  2015 (2)
The rise of cloud computing systems. Dean, J. In SOSP History Day 2015, Monterey, California, USA, October 4, 2015, pages 12:1–12:40, 2015.
The rise of cloud computing systems [link]Paper   doi   link   bibtex  
Distilling the Knowledge in a Neural Network. Hinton, G. E.; Vinyals, O.; and Dean, J. CoRR, abs/1503.02531. 2015.
Distilling the Knowledge in a Neural Network [link]Paper   link   bibtex  
  2014 (2)
The Beckman Report on Database Research. Abadi, D. J.; Agrawal, R.; Ailamaki, A.; Balazinska, M.; Bernstein, P. A.; Carey, M. J.; Chaudhuri, S.; Dean, J.; Doan, A.; Franklin, M. J.; Gehrke, J.; Haas, L. M.; Halevy, A. Y.; Hellerstein, J. M.; Ioannidis, Y. E.; Jagadish, H. V.; Kossmann, D.; Madden, S.; Mehrotra, S.; Milo, T.; Naughton, J. F.; Ramakrishnan, R.; Markl, V.; Olston, C.; Ooi, B. C.; Ré, C.; Suciu, D.; Stonebraker, M.; Walter, T.; and Widom, J. SIGMOD Rec., 43(3): 61–70. 2014.
The Beckman Report on Database Research [link]Paper   doi   link   bibtex  
Zero-Shot Learning by Convex Combination of Semantic Embeddings. Norouzi, M.; Mikolov, T.; Bengio, S.; Singer, Y.; Shlens, J.; Frome, A.; Corrado, G.; and Dean, J. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, 2014.
Zero-Shot Learning by Convex Combination of Semantic Embeddings [link]Paper   link   bibtex  
  2013 (9)
The tail at scale. Dean, J.; and Barroso, L. A. Commun. ACM, 56(2): 74–80. 2013.
The tail at scale [link]Paper   doi   link   bibtex  
Spanner: Google's Globally Distributed Database. Corbett, J. C.; Dean, J.; Epstein, M.; Fikes, A.; Frost, C.; Furman, J. J.; Ghemawat, S.; Gubarev, A.; Heiser, C.; Hochschild, P.; Hsieh, W. C.; Kanthak, S.; Kogan, E.; Li, H.; Lloyd, A.; Melnik, S.; Mwaura, D.; Nagle, D.; Quinlan, S.; Rao, R.; Rolig, L.; Saito, Y.; Szymaniak, M.; Taylor, C.; Wang, R.; and Woodford, D. ACM Trans. Comput. Syst., 31(3): 8. 2013.
Spanner: Google's Globally Distributed Database [link]Paper   doi   link   bibtex  
On rectified linear units for speech processing. Zeiler, M. D.; Ranzato, M.; Monga, R.; Mao, M. Z.; Yang, K.; Le, Q. V.; Nguyen, P.; Senior, A. W.; Vanhoucke, V.; Dean, J.; and Hinton, G. E. In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2013, Vancouver, BC, Canada, May 26-31, 2013, pages 3517–3521, 2013.
On rectified linear units for speech processing [link]Paper   doi   link   bibtex  
Multilingual acoustic models using distributed deep neural networks. Heigold, G.; Vanhoucke, V.; Senior, A. W.; Nguyen, P.; Ranzato, M.; Devin, M.; and Dean, J. In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2013, Vancouver, BC, Canada, May 26-31, 2013, pages 8619–8623, 2013.
Multilingual acoustic models using distributed deep neural networks [link]Paper   doi   link   bibtex  
DeViSE: A Deep Visual-Semantic Embedding Model. Frome, A.; Corrado, G. S.; Shlens, J.; Bengio, S.; Dean, J.; Ranzato, M.; and Mikolov, T. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States, pages 2121–2129, 2013.
DeViSE: A Deep Visual-Semantic Embedding Model [link]Paper   link   bibtex  
Distributed Representations of Words and Phrases and their Compositionality. Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G. S.; and Dean, J. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States, pages 3111–3119, 2013.
Distributed Representations of Words and Phrases and their Compositionality [link]Paper   link   bibtex  
Efficient Estimation of Word Representations in Vector Space. Mikolov, T.; Chen, K.; Corrado, G.; and Dean, J. In 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings, 2013.
Efficient Estimation of Word Representations in Vector Space [link]Paper   link   bibtex  
Distributed Representations of Words and Phrases and their Compositionality. Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G.; and Dean, J. CoRR, abs/1310.4546. 2013.
Distributed Representations of Words and Phrases and their Compositionality [link]Paper   link   bibtex  
Using Web Co-occurrence Statistics for Improving Image Categorization. Bengio, S.; Dean, J.; Erhan, D.; Ie, E.; Le, Q. V.; Rabinovich, A.; Shlens, J.; and Singer, Y. CoRR, abs/1312.5697. 2013.
Using Web Co-occurrence Statistics for Improving Image Categorization [link]Paper   link   bibtex  
  2012 (3)
Building high-level features using large scale unsupervised learning. Le, Q. V.; Ranzato, M.; Monga, R.; Devin, M.; Corrado, G.; Chen, K.; Dean, J.; and Ng, A. Y. In Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012, 2012.
Building high-level features using large scale unsupervised learning [pdf]Paper   link   bibtex  
Large Scale Distributed Deep Networks. Dean, J.; Corrado, G.; Monga, R.; Chen, K.; Devin, M.; Le, Q. V.; Mao, M. Z.; Ranzato, M.; Senior, A. W.; Tucker, P. A.; Yang, K.; and Ng, A. Y. In Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States, pages 1232–1240, 2012.
Large Scale Distributed Deep Networks [link]Paper   link   bibtex  
Spanner: Google's Globally-Distributed Database. Corbett, J. C.; Dean, J.; Epstein, M.; Fikes, A.; Frost, C.; Furman, J. J.; Ghemawat, S.; Gubarev, A.; Heiser, C.; Hochschild, P.; Hsieh, W. C.; Kanthak, S.; Kogan, E.; Li, H.; Lloyd, A.; Melnik, S.; Mwaura, D.; Nagle, D.; Quinlan, S.; Rao, R.; Rolig, L.; Saito, Y.; Szymaniak, M.; Taylor, C.; Wang, R.; and Woodford, D. In 10th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2012, Hollywood, CA, USA, October 8-10, 2012, pages 251–264, 2012.
Spanner: Google's Globally-Distributed Database [link]Paper   link   bibtex  
  2011 (1)
Building high-level features using large scale unsupervised learning. Le, Q. V.; Monga, R.; Devin, M.; Corrado, G.; Chen, K.; Ranzato, M.; Dean, J.; and Ng, A. Y. CoRR, abs/1112.6209. 2011.
Building high-level features using large scale unsupervised learning [link]Paper   link   bibtex  
  2010 (2)
MapReduce: a flexible data processing tool. Dean, J.; and Ghemawat, S. Commun. ACM, 53(1): 72–77. 2010.
MapReduce: a flexible data processing tool [link]Paper   doi   link   bibtex  
Evolution and future directions of large-scale storage and computation systems at Google. Dean, J. In Proceedings of the 1st ACM Symposium on Cloud Computing, SoCC 2010, Indianapolis, Indiana, USA, June 10-11, 2010, pages 1, 2010.
Evolution and future directions of large-scale storage and computation systems at Google [link]Paper   doi   link   bibtex  
  2009 (2)
Back-off language model compression. Harb, B.; Chelba, C.; Dean, J.; and Ghemawat, S. In INTERSPEECH 2009, 10th Annual Conference of the International Speech Communication Association, Brighton, United Kingdom, September 6-10, 2009, pages 352–355, 2009.
Back-off language model compression [link]Paper   link   bibtex  
Challenges in building large-scale information retrieval systems: invited talk. Dean, J. In Proceedings of the Second International Conference on Web Search and Web Data Mining, WSDM 2009, Barcelona, Spain, February 9-11, 2009, pages 1, 2009.
Challenges in building large-scale information retrieval systems: invited talk [link]Paper   doi   link   bibtex  
  2008 (2)
MapReduce: simplified data processing on large clusters. Dean, J.; and Ghemawat, S. Commun. ACM, 51(1): 107–113. 2008.
MapReduce: simplified data processing on large clusters [link]Paper   doi   link   bibtex  
Bigtable: A Distributed Storage System for Structured Data. Chang, F.; Dean, J.; Ghemawat, S.; Hsieh, W. C.; Wallach, D. A.; Burrows, M.; Chandra, T.; Fikes, A.; and Gruber, R. E. ACM Trans. Comput. Syst., 26(2): 4:1–4:26. 2008.
Bigtable: A Distributed Storage System for Structured Data [link]Paper   doi   link   bibtex  
  2007 (2)
Large Language Models in Machine Translation. Brants, T.; Popat, A. C.; Xu, P.; Och, F. J.; and Dean, J. In EMNLP-CoNLL 2007, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, June 28-30, 2007, Prague, Czech Republic, pages 858–867, 2007.
Large Language Models in Machine Translation [link]Paper   link   bibtex  
MapReduce and Other Building Blocks for Large-Scale Distributed Systems at Google. Dean, J. In Proceedings of the 2007 USENIX Annual Technical Conference, Santa Clara, CA, USA, June 17-22, 2007, 2007.
MapReduce and Other Building Blocks for Large-Scale Distributed Systems at Google [link]Paper   link   bibtex  
  2006 (3)
LPI Linux certification - in a nutshell: a desktop quick reference: pass the LPIC-1 and LPIC-2 exams, 2nd Edition. Pritchard, S.; Pessanha, B. G.; Langfeldt, N.; Stanger, J.; and Dean, J. O'Reilly, 2006.
LPI Linux certification - in a nutshell: a desktop quick reference: pass the LPIC-1 and LPIC-2 exams, 2nd Edition [link]Paper   link   bibtex  
Experiences with MapReduce, an abstraction for large-scale computation. Dean, J. In 15th International Conference on Parallel Architectures and Compilation Techniques (PACT 2006), Seattle, Washington, USA, September 16-20, 2006, pages 1, 2006.
Experiences with MapReduce, an abstraction for large-scale computation [link]Paper   doi   link   bibtex  
Bigtable: A Distributed Storage System for Structured Data (Awarded Best Paper!). Chang, F.; Dean, J.; Ghemawat, S.; Hsieh, W. C.; Wallach, D. A.; Burrows, M.; Chandra, T.; Fikes, A.; and Gruber, R. In 7th Symposium on Operating Systems Design and Implementation (OSDI '06), November 6-8, Seattle, WA, USA, pages 205–218, 2006.
Bigtable: A Distributed Storage System for Structured Data (Awarded Best Paper!) [link]Paper   link   bibtex  
  2004 (1)
MapReduce: Simplified Data Processing on Large Clusters. Dean, J.; and Ghemawat, S. In 6th Symposium on Operating System Design and Implementation (OSDI 2004), San Francisco, California, USA, December 6-8, 2004, pages 137–150, 2004.
MapReduce: Simplified Data Processing on Large Clusters [link]Paper   link   bibtex  
  2003 (1)
Web Search for a Planet: The Google Cluster Architecture. Barroso, L. A.; Dean, J.; and Hölzle, U. IEEE Micro, 23(2): 22–28. 2003.
Web Search for a Planet: The Google Cluster Architecture [link]Paper   doi   link   bibtex  
  2001 (1)
LPI Linux certification in a nutshell - a desktop quick reference: covers exams 101 102 for LPI level 1. Dean, J. O'Reilly, 2001.
link   bibtex  
  2000 (2)
A Comparison of Techniques to Find Mirrored Hosts on the WWW. Bharat, K.; Broder, A. Z.; Dean, J.; and Henzinger, M. R. IEEE Data Eng. Bull., 23(4): 21–26. 2000.
A Comparison of Techniques to Find Mirrored Hosts on the WWW [pdf]Paper   link   bibtex  
A comparison of techniques to find mirrored hosts on the WWW. Bharat, K.; Broder, A. Z.; Dean, J.; and Henzinger, M. R. J. Am. Soc. Inf. Sci., 51(12): 1114–1122. 2000.
A comparison of techniques to find mirrored hosts on the WWW [link]Paper   doi   link   bibtex  
  1999 (3)
Control of Walking in the Stick Insect: From Behavior and Physiology to Modeling. Dean, J.; Kindermann, T.; Schmitz, J.; Schumm, M.; and Cruse, H. Auton. Robots, 7(3): 271–288. 1999.
Control of Walking in the Stick Insect: From Behavior and Physiology to Modeling [link]Paper   doi   link   bibtex  
Finding Related Pages in the World Wide Web. Dean, J.; and Henzinger, M. R. Comput. Networks, 31(11-16): 1467–1479. 1999.
Finding Related Pages in the World Wide Web [link]Paper   doi   link   bibtex  
A Comparison of Techniques to Find Mirrored Hosts on the WWW. Bharat, K.; Broder, A. Z.; Dean, J.; and Henzinger, M. R. In 1999 ACM Digital Library Workshop on Organizing Web Space (WOWS), August 14, 1999, at Radisson Hotel, Berkeley, CA, USA (in conjunction with ACM DL'99), pages 2–12, 1999.
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  1998 (1)
Walknet–a biologically inspired network to control six-legged walking. Cruse, H.; Kindermann, T.; Schumm, M.; Dean, J.; and Schmitz, J. Neural Networks, 11(7-8): 1435–1447. 1998.
Walknet–a biologically inspired network to control six-legged walking [link]Paper   doi   link   bibtex  
  1997 (4)
Continuous Profiling: Where Have All the Cycles Gone?. Anderson, J. M.; Berc, L. M.; Dean, J.; Ghemawat, S.; Henzinger, M. R.; Leung, S.; Sites, R. L.; Vandevoorde, M. T.; Waldspurger, C. A.; and Weihl, W. E. ACM Trans. Comput. Syst., 15(4): 357–390. 1997.
Continuous Profiling: Where Have All the Cycles Gone? [link]Paper   doi   link   bibtex  
\emphProfileMe: Hardware Support for Instruction-Level Profiling on Out-of-Order Processors. Dean, J.; Hicks, J. E.; Waldspurger, C. A.; Weihl, W. E.; and Chrysos, G. Z. In Proceedings of the Thirtieth Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 30, Research Triangle Park, North Carolina, USA, December 1-3, 1997, pages 292–302, 1997.
\emphProfileMe: Hardware Support for Instruction-Level Profiling on Out-of-Order Processors [link]Paper   doi   link   bibtex  
Call Graph Construction in Object-Oriented Languages. Grove, D.; DeFouw, G.; Dean, J.; and Chambers, C. In Proceedings of the 1997 ACM SIGPLAN Conference on Object-Oriented Programming Systems, Languages & Applications, OOPSLA 1997, Atlanta, Georgia, October 5-9, 1997, pages 108–124, 1997.
Call Graph Construction in Object-Oriented Languages [link]Paper   doi   link   bibtex  
Continuous Profiling: Where Have All the Cycles Gone?. Anderson, J. M.; Berc, L. M.; Dean, J.; Ghemawat, S.; Henzinger, M. R.; Leung, S.; Sites, R. L.; Vandevoorde, M. T.; Waldspurger, C. A.; and Weihl, W. E. In Proceedings of the Sixteenth ACM Symposium on Operating System Principles, SOSP 1997, St. Malo, France, October 5-8, 1997, pages 1–14, 1997.
Continuous Profiling: Where Have All the Cycles Gone? [link]Paper   doi   link   bibtex  
  1996 (2)
Simplifying Neural Networks for Controlling Walking by Exploiting Physical Properties. Cruse, H.; Bartling, C.; Dean, J.; Kindermann, T.; Schmitz, J.; Schumm, M.; and Wagner, H. In Artificial Neural Networks - ICANN 96, 1996 International Conference, Bochum, Germany, July 16-19, 1996, Proceedings, pages 433–438, 1996.
Simplifying Neural Networks for Controlling Walking by Exploiting Physical Properties [link]Paper   doi   link   bibtex  
Vortex: An Optimizing Compiler for Object-Oriented Languages. Dean, J.; DeFouw, G.; Grove, D.; Litvinov, V.; and Chambers, C. In Proceedings of the 1996 ACM SIGPLAN Conference on Object-Oriented Programming Systems, Languages & Applications, OOPSLA 1996, San Jose, California, USA, October 6-10, 1996, pages 83–100, 1996.
Vortex: An Optimizing Compiler for Object-Oriented Languages [link]Paper   doi   link   bibtex  
  1995 (6)
Walking: A Complex Behavior Controlled by Simple Networks. Cruse, H.; Bartling, C.; Dreifert, M.; Schmitz, J.; Brunn, D. E.; Dean, J.; and Kindermann, T. Adapt. Behav., 3(4): 385–418. 1995.
Walking: A Complex Behavior Controlled by Simple Networks [link]Paper   doi   link   bibtex  
A modular artificial neural net for controlling a six-legged walking system. Cruse, H.; Bartling, C.; Cymbalyuk, G. S.; Dean, J.; and Dreifert, M. Biol. Cybern., 72(5): 421–430. 1995.
A modular artificial neural net for controlling a six-legged walking system [link]Paper   doi   link   bibtex  
Optimization of Object-Oriented Programs Using Static Class Hierarchy Analysis. Dean, J.; Grove, D.; and Chambers, C. In ECOOP'95 - Object-Oriented Programming, 9th European Conference, Århus, Denmark, August 7-11, 1995, Proceedings, pages 77–101, 1995.
Optimization of Object-Oriented Programs Using Static Class Hierarchy Analysis [link]Paper   doi   link   bibtex  
A Framework for Selective Recompilation in the Presence of Complex Intermodule Dependencies. Chambers, C.; Dean, J.; and Grove, D. In 17th International Conference on Software Engineering, Seattle, Washington, USA, April 23-30, 1995, Proceedings, pages 221–230, 1995.
A Framework for Selective Recompilation in the Presence of Complex Intermodule Dependencies [link]Paper   doi   link   bibtex  
Profile-Guided Receiver Class Prediction. Grove, D.; Dean, J.; Garrett, C.; and Chambers, C. In Proceedings of the Tenth Annual Conference on Object-Oriented Programming Systems, Languages, and Applications, OOPSLA 1995, Austin, Texas, USA, October 15-19, 1995, pages 108–123, 1995.
Profile-Guided Receiver Class Prediction [link]Paper   doi   link   bibtex  
Selective Specialization for Object-Oriented Languages. Dean, J.; Chambers, C.; and Grove, D. In Proceedings of the ACM SIGPLAN'95 Conference on Programming Language Design and Implementation (PLDI), La Jolla, California, USA, June 18-21, 1995, pages 93–102, 1995.
Selective Specialization for Object-Oriented Languages [link]Paper   doi   link   bibtex  
  1994 (2)
Towards Better Inlining Decisions Using Inlining Trials. Dean, J.; and Chambers, C. In Proceedings of the 1994 ACM Conference on LISP and Functional Programming, Orlando, Florida, USA, 27-29 June 1994, pages 273–282, 1994.
Towards Better Inlining Decisions Using Inlining Trials [link]Paper   doi   link   bibtex  
Identifying Profitable Specialization in Object-Oriented Languages. Dean, J.; Chambers, C.; and Grove, D. In PEPM'94 - ACM SIGPLAN Workshop on Partial Evaluation and Semantics-Based Program Manipulation, Walt Disney World Vilage, Orlando, Florida, USA, 25 June 1994, Proceedings. Technical Report 94/9, pages 85–96, 1994.
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  1992 (3)
Kinematic Model of a Stick Insect as an Example of a Six-Legged Walking System. Müller-Wilm, U.; Cruse, H.; Eltze, J.; Dean, J.; Weidemann, H.; and Pfeiffer, F. Adapt. Behav., 1(2): 155–169. 1992.
Kinematic Model of a Stick Insect as an Example of a Six-Legged Walking System [link]Paper   doi   link   bibtex  
A model of leg coordination in the stick insect, \emphCarausius morosus. Dean, J. Biol. Cybern., 66(4): 335–343. 1992.
A model of leg coordination in the stick insect, \emphCarausius morosus [link]Paper   doi   link   bibtex  
A model of leg coordination in the stick insect, \emphCarausim morosus. Dean, J. Biol. Cybern., 66(4): 345–355. 1992.
A model of leg coordination in the stick insect, \emphCarausim morosus [link]Paper   doi   link   bibtex