| Large-scale matrix factorization with distributed stochastic gradient descent R Gemulla, E Nijkamp, PJ Haas, Y Sismanis Proceedings of the 17th ACM SIGKDD international conference on Knowledge …, 2011 | 849 | 2011 |
| Deep learning with tensorflow: A review B Pang, E Nijkamp, YN Wu Journal of Educational and Behavioral Statistics 45 (2), 227-248, 2020 | 198 | 2020 |
| Learning non-convergent non-persistent short-run MCMC toward energy-based model E Nijkamp, M Hill, SC Zhu, YN Wu Advances in Neural Information Processing Systems 32, 2019 | 196 | 2019 |
| Codegen: An open large language model for code with multi-turn program synthesis E Nijkamp, B Pang, H Hayashi, L Tu, H Wang, Y Zhou, S Savarese, ... arXiv preprint arXiv:2203.13474, 2022 | 156 | 2022 |
| On the anatomy of mcmc-based maximum likelihood learning of energy-based models E Nijkamp, M Hill, T Han, SC Zhu, YN Wu Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5272-5280, 2020 | 123 | 2020 |
| A conversational paradigm for program synthesis E Nijkamp, B Pang, H Hayashi, L Tu, H Wang, Y Zhou, S Savarese, ... arXiv preprint arXiv:2203.13474 30, 2022 | 100 | 2022 |
| Learning latent space energy-based prior model B Pang, T Han, E Nijkamp, SC Zhu, YN Wu Advances in Neural Information Processing Systems 33, 21994-22008, 2020 | 97 | 2020 |
| Flow contrastive estimation of energy-based models R Gao, E Nijkamp, DP Kingma, Z Xu, AM Dai, YN Wu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 86 | 2020 |
| MapReduce and PACT-comparing data parallel programming models A Alexandrov, S Ewen, M Heimel, F Hueske, O Kao, V Markl, E Nijkamp, ... Datenbanksysteme für Business, Technologie und Web (BTW), 2011 | 77 | 2011 |
| Massively parallel data analysis with pacts on nephele A Alexandrov, M Heimel, V Markl, D Battré, F Hueske, E Nijkamp, S Ewen, ... Proceedings of the VLDB Endowment 3 (1-2), 1625-1628, 2010 | 72 | 2010 |
| ProGen2: exploring the boundaries of protein language models E Nijkamp, J Ruffolo, EN Weinstein, N Naik, A Madani arXiv preprint arXiv:2206.13517, 2022 | 57 | 2022 |
| Divergence triangle for joint training of generator model, energy-based model, and inferential model T Han, E Nijkamp, X Fang, M Hill, SC Zhu, YN Wu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 56 | 2019 |
| Towards holistic and automatic evaluation of open-domain dialogue generation B Pang, E Nijkamp, W Han, L Zhou, Y Liu, K Tu Association for Computational Linguistics (ACL), 2020 | 55 | 2020 |
| Scaling dynamic authority-based search using materialized subgraphs A Balmin, H Hwang, E Nijkamp, B Reinwald US Patent 9,171,077, 2015 | 54 | 2015 |
| Learning multi-layer latent variable model via variational optimization of short run mcmc for approximate inference E Nijkamp, B Pang, T Han, L Zhou, SC Zhu, YN Wu Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 36 | 2020 |
| Joint training of variational auto-encoder and latent energy-based model T Han, E Nijkamp, L Zhou, B Pang, SC Zhu, YN Wu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 34 | 2020 |
| Scaling dynamic authority-based search using materialized subgraphs A Balmin, H Hwang, E Nijkamp, B Reinwald US Patent 10,521,435, 2019 | 30 | 2019 |
| Binrank: Scaling dynamic authority-based search using materialized subgraphs H Hwang, A Balmin, B Reinwald, E Nijkamp IEEE Transactions on Knowledge and Data Engineering 22 (8), 1176-1190, 2010 | 26 | 2010 |
| Learning energy-based model with flow-based backbone by neural transport mcmc E Nijkamp, R Gao, P Sountsov, S Vasudevan, B Pang, SC Zhu, YN Wu arXiv preprint arXiv:2006.06897 2, 2020 | 20 | 2020 |
| Long document summarization with top-down and bottom-up inference B Pang, E Nijkamp, W Kryściński, S Savarese, Y Zhou, C Xiong arXiv preprint arXiv:2203.07586, 2022 | 19 | 2022 |
